Master‟s Thesis
A Study on the Elevator Integrated Delivery System
in Relation to Passenger Traffic
-Focusing on Residential Buildings
HyunJung Olivia Yoo (유현정)
Department of Civil & Environmental Engineering
KAIST
2011
A Study on the Elevator Integrated Delivery System
in Relation to Passenger Traffic
-Focusing on Residential Buildings
A Study on the Elevator Integrated Delivery
System in Relation to Passenger Traffic
-Focusing on Residential Buildings
Advisor
:
Professor Park Jiyoung
Co-advisor :
Professor Kim Jinkeun
by
HyunJung Yoo
Department of Civil & Environmental Engineering
KAIST
A thesis submitted to the faculty of KAIST in partial fulfillment of the
requirements for the degree of Master of Science in Engineering in the
Department of Civil & Environmental Engineering. The study was conducted
in accordance with Code of Research Ethics1
May, 31st , 2011
Approved by
___________________
Professor Park Jiyoung
1
Declaration of Ethical Conduct in Research: I, as a graduate student of KAIST, hereby declare that I have not
committed any acts that may damage the credibility of my research. These include, but are not limited to:
falsification, thesis written by someone else, distortion of research findings or plagiarism. I affirm that my thesis
contains honest conclusions based on my own careful research under the guidance of my thesis advisor.
A Study on the Elevator Integrated Delivery System
in Relation to Passenger Traffic
-Focusing on Residential Buildings
HyunJung Yoo
The present dissertation has been approved by the dissertation committee
as a master‟s thesis at KAIST
5 /31,2011
Committee head
Jiyoung Park
Committee member
Jinkeun Kim
Committee member
Seongju Jang
MCE
20094335
유 현 정. Yoo, Hyun Jung. A Study on the Elevator Integrated Delivery System in
Relation to Passenger Traffic Focusing on Residential Buildings.주거 건물 이용자를
고려한 신개념 엘레베이터 통합 택배 시스템에 관한 연구.Department of Civil and
Environmental Engineering. 2011. 50 p. Advisor Prof. Park, Jiyoung. Text in English.
ABSTRACT
High density apartments and high-rise buildings are common form of buildings observed in South Korea and in
many other countries or cities that have small availability of territory. Moreover, with the growth of cyberspace,
online commerce has contributed in development of domestic delivery. In fact, it is expected that currently an
economically active person in Korea receives around forty parcels per year and it is foreseen to increase with
time. This paper deals with the transportation of these parcels within the building: their movement, their
influence to other passengers, and their energy consumption. A new type of delivery system is suggested in this
paper, so-called “elevator integrated delivery system,” which is a modified version of existing passenger
elevator, that automatically moves goods to their addressed floor. This paper investigates the feasibility and
benefits of this new system and observes five different possible operation methods for two scenarios of amount
of deliveries. Although the research deals with a conceptual idea of futuristic door to door service, it shows that
the system is beneficial in energy saving when there are heavy amount of parcel deliveries. Moreover, it is
observed that overall, automatic instant delivery of parcels require less energy and waiting time for passengers,
as well as for goods.
Keywords: delivery, elevator, lift, parcel, passenger traffic
i
ii
Table of Contents
Abstract ································································································i
Table of Contents ····················································································iii
List of Tables ··························································································v
List of Figures ························································································ vi
Chapter 1. Introduction
1.1 Research Background ········································································· 1
1.2 Dissertation Structure ········································································· 4
Chapter 2. Literature Review
2.1 Overview ······················································································· 5
2.1.1 History & Influence ······································································· 5
2.1.2 Elevator Design ············································································ 7
2.1.3 Research Trends ··········································································· 8
2.2 Passenger Traffic Pattern in Elevator ······················································· 8
2.2.1 Passenger Traffic Demand ································································ 9
2.2.2 Calculation Methods······································································· 9
2.2.3 Passengers Face to Time Tolerance ··················································· 12
Chapter 3. Elevator Integrated Delivery System
3.1 Elevator Integrated Delivery System······················································ 13
iii
Chapter 4. Methodology
4.1 Five Models of Elevator Integrated Delivery System ··································· 16
4.2 Simulation Tool ·············································································· 18
4.3 Research Data & Assumptions·······························································18
4.3.1 Analysis Data ············································································· 18
4.3.2 Building Data ············································································· 19
4.3.3 Elevator Data ···············································································21
4.3.4 Passenger Data ·············································································22
4.3.5 Goods Data ·················································································24
Chapter 5. Results & Analysis
5.1 Results ························································································ 26
5.1.1 Case I) ····················································································· 27
5.1.2 Case II) ···················································································· 31
5.1.3 case III)······················································································33
5.1.4 Case IV) ·····················································································35
5.1.5 Case V) ······················································································37
5.2 Comparison & Discussion ·································································· 39
Chapter 6. Conclusion
6.1 Research Summary ·········································································· 43
6.2 Research Limitation and Future Work ···················································· 44
Appendix ··························································································· 45
iv
List of Tables
2.1 Passenger‟s Time Tolerance ·································································12
4.1 Electricity Consumption for Various Loads and Direction ····························· 19
4.2 Elevator Data Values ········································································ 21
4.3 Two Observed Cases of Demands ························································· 24
5.1 Summary Table for Passengers of Case I) ················································ 28
5.2 Summary Table for Goods of Case I) ····················································· 29
5.3 Summary Table for Energy Consumption of Case I) ···································· 30
5.4 Summary Table for Passengers of Case II) ··············································· 31
5.5 Summary Table for Goods of Case II) ···················································· 32
5.6 Summary Table for Energy Consumption of Case II) ··································· 32
5.7 Summary Table for Passengers of Case III) ·············································· 33
5.8 Summary Table for Goods of Case III) ··················································· 34
5.9 Summary Table for Energy Consumption of Case III) ·································· 35
5.10 Summary Table for Passengers of Case IV)············································· 36
5.11 Summary Table for Goods of Case IV) ·················································· 36
5.12 Summary Table for Energy Consumption of Case IV) ································ 37
5.13 Summary Table for Passengers of Case V)·············································· 37
5.14 Summary Table for Goods of Case V) ··················································· 38
5.15 Summary Table for Energy Consumption of Case V) ································· 38
5.16 Ratings of Case I), II), III), IV), V) ······················································ 40
5.17 Summary Table for Passengers of Case I), II), III), IV), V) ·························· 41
5.18 Summary Table for Goods of Case I), II), III), IV), V) ································ 41
5.19 Summary Table for Energy Consumption of Case I), II), III), IV), V) ·············· 42
A.1 Values of H & S with Respect to Number of Passengers ······························ 45
v
List of Figures
1.1 Amount of Yearly Delivery ································································ 1
2.1 Historical & Present Elevator Concept ··················································· 5
2.2 Rail System, Pneumatic Tube System & Automated Guided Vehicle ················· 6
2.3 Ubi-locker System ············································································ 7
2.4 Diagram of Different Time Periods ·························································10
3.1 Different Possible Combinations of Elevator Integrated Delivery System ···········13
3.2 Typical Building Section & Elevator Integrated Delivery System ····················14
3.3 Plan View of Elevator Integrated Delivery System ·····································14
3.4 Detail Section View of Integrated Delivery System ····································15
4.1 Illustration of Case I), II), III), IV), V) ·····················································17
4.2 Floor Plan of Direct and Corridor Access Types ······································· 13
4.3 Passenger Demand Graph of Strakosch Residential Template ·························23
4.4 Passenger Activity Graph of Strakosch Residential Template ························· 23
4.5 Plan View of Elevator Division ··························································· 25
5. 1 Waiting Times of 10 Datasets ····························································· 26
5.2 Transit Times of 10 Datasets ······························································· 27
5.3 Time to Destination of 10 Datasets ························································ 27
5.4 Passenger Activity Graph: Only Passenger & Case I) ··································· 28
5.5 Passenger Activity Graph: Only Passenger & Case II) ·································· 31
5.6 Passenger Activity Graph: Only Passenger & Case III) ································· 33
5.7 Passenger Activity Graph: Only Passenger & Case IV) ································ 35
5.8 Possible Future Operation System: Off-Peak Delivery·································· 40
vi
Chapter 1. Introduction
1.1 Research Background & Purpose
The limited land of Korea pushed its cities into high-density urban development. In fact, about 60% of
Korean population lives in apartments, in which more than half of them are over fifteen floors [21]. On the
other hand, this particular common form of residency has encouraged the development of home delivery
service together with cyber shopping malls and home shopping commerce. For the past ten years, the domestic
express industry has grown by an average of 27%, and the market size has expanded five times bigger than in
year 2000 [24]. Moreover, it is believed by the experts that the growth of the industry will continue in the
future.
Fig 1.1 Amount of Yearly Delivery
A parcel is defined as an object that weighs less than 30 kg and the sum of the height, depth and
length measures less than 160cm*. Currently, it is expected that an economically active person in Korea
receives around forty delivery packages per year [13].
With this in mind, several problems are caused by the growth of the delivery industry. The
transportation of parcels greatly contributes in urban congestion and emits air pollution. Moreover, often the
*
Fair Trade Commission. Chapter 1 General Provisions Article 2 : Terms of Delivery Standards
1
trucks and motorcycles park illegally and block the roadways during the parcel hand over. Also, the random
delivery times in buildings cause waste in energy and time during the transmittal, as the appointed mailman
has to use the elevator to deliver each object that is much lighter to the addressed person. Currently, the energy
use caused by elevator(s) is estimated to 5-15% of total building energy consumption [1]. In the near future,
with the increasing demand of freight and parcel transportation, a development in urban and architectural
(inside building) robotic network delivery is foreseen to reduce the current inconveniences mentioned above
[28]. Furthermore, this delivery system could also extend to serve mails, or any daily deliveries such as
newspaper or morning coffee, etc.
This study focuses on the vertical transportation system of the passengers and parcels in buildings.
Some commercial and office buildings have a separate freight elevator, but it is not widely used in residential
edifices. In addition, an automatic documents or small parcel delivery system already currently exist in
building types such as hospitals and libraries. However, this is the first attempt to study an automatic parcel
delivery system taking place in residential buildings. In this study, automatic freight delivery system is
assumed to be integrated in existing elevator. This specially designed elevator has two separate loadings, one
for the freight and the other one for the passenger. The separation can exist in different combination of forms.
Perhaps, the freight loading can be on the top part of the elevator cabinet, and the passenger on the bottom or,
they can be separated side by side. The system is further explained in chapter 3. The purpose of this paper is to
observe if automatic freight/passenger elevator can be useful in reducing energy consumption and time waste
in package deliveries. In fact, the integration of the system in existing elevator reduces the needs for
renovation, use of space, and employment of new materials that lead to extra investments and pollution of
environment. Although a well-maintained elevator can easily have a life expectancy of more than 50 years, the
changing social and economic conditions usually demand that such equipment be replaced or upgraded in
short period [25]. Furthermore, in this study, the physical elements of the automatic system are altogether
ignored as there is no existing design specification yet on this system. Therefore the research is based on a
purely conceptual elevator design and its possible ways of operation to verify the new approach of delivery +
passenger elevator‟s feasibility and its benefits in residential buildings in relation to existing passenger traffic.
Residential apartments are chosen as the location of research subject as the parcels are mostly delivered to
dwelled houses. In this study, five different operational coordinations are investigated: I) current elevator
arrangement with delivery man system II) Instant delivery with goods integrated elevator III) Interval delivery
2
with goods integrated elevator IV) Overnight delivery with goods integrated elevator and V) Delivery with
separate freight elevator. In the recent studies, passengers‟ traffic pattern has been investigated to select the
appropriate elevator design; however this study is the first to explore together the passenger traffic as well as
the parcel delivery demands. Moreover many studies have been done on office building traffic pattern, but this
work targets residential buildings as it concerns with the door-to-door services. In essence, the purpose of this
study is to investigate a conceptual freight + passenger elevator considering the current residential traffic
pattern to verify its feasibility and benefits. Hopefully, this thesis will serve as a basis for the future
transportation of passenger and parcel system in buildings.
3
1.2 Dissertation Structure
The present study is divided into six chapters, including the introduction. In Chapter 2, literature
review on elevator engineering is overviewed, concentrating on the analysis of passenger traffic in elevator
design. Also, different methods and elements in energy consumption of elevator are reviewed. In Chapter 3,
the new elevator integrated delivery system is defined and described. Then in Chapter 4, methodologies to
verify the system‟s feasibility and to investigate its benefits are discussed, first by naming the four cases
examined and by listing their related assumptions. Then the procedures for the calculation and simulation
follow. In Chapter 5, the results of the simulation are presented for each case (from I to V) described in the
previous chapter. Finally, the paper concludes with a summary of the study in Chapter 6, and discusses about
its limitation and possible future works on the topic.
4
Chapter 2. Literature Review
Chapter 2 introduces historical flow of passenger and goods elevator, as well as some important
specifications and elements in elevator design. Additionally, elevator field‟s research trend is summarized.
Then, evolution and existing way of calculating lift related times (waiting, passenger, round-trip, etc) set by lift
engineers are studied. Moreover, agreed quantification of tolerated passenger waiting time is overviewed. The
goal of this chapter is to give a general idea of past researches done on the current field, and also to
acknowledge studies and facts that are to be engaged in the later part of the paper.
2.1 Overview
2.1.1 History & Influence
The first concept of moving vertically an object or a person appeared in third century B.C as a form of
hoist. They were operated by animal and human power, or by water-driven mechanisms. However, the elevator,
as one knows it today, is first developed during the 1800s. They mostly used hydraulic plunger or steam for
lifting capability. Then, with the great invention of electricity, the modern concept of elevator began to evolve.
Namely, Otis promoted elevators to businesses to move freights from one floor to the other, maintained and
operated with help of lift boys. Until then, there was no clear distinction between passenger and freight
elevators. However, in 1857, first commercial elevator for passengers was installed in a department store of
New York City [10]. The advances in electronic systems during World War II has brought many changes in
elevator design and installation such as the automatic programming that eliminated operators at the ground
level or morning and evening peak scheduling, etc [11].
Fig 2.1 Historical (Hoist) & Present (Traction) Elevator Concepts
5
Accordingly, the development of modern elevator deeply affected practically and aesthetically in both
architecture and urban development by allowing high rise commercial and residential buildings, as well as
skyscrapers. With the recurrent use of lifts in buildings, standards have been formed by types of building,
number of passengers, and their traffic pattern for planning and for service quality. In fact, the centralized main
core that one often can notice in multilevel building is a consequence of lift [14]. In most cases, lifts are placed
together (when there are more than one elevator) near the entry point of the building to reduce the walking
distance and reduce congestion of users. Moreover, the introduction of elevator has also affected in localization
of different functional space in a building. For example, in mixed-use buildings, it is common to place
commercials on the lower, offices in the middle and residential on the top floors [3]. On the other hand, not
only passenger elevator has evolved, but also way to deliver goods within a building has diversified through
years. Namely, dumbwaiters have been invented around 1800 by Thomas Jefferson and pneumatic capsule
transportation, a cylindrical container that is propelled through a network of tubes traveling by compressed air,
was first conceived in 1860‟s [29]. Moreover, autonomous logistics, a system of unmanned equipment that
transfers goods; and automated guided vehicles, a mobile robot that follows navigation guides often located on
the floor have developed in recent years. These systems are widely used in present days, particularly in
libraries, hospitals, warehouses (figure 2.2a, b, c), etc. to deliver documents and packages.
Fig 2.2 Rail System, Pneumatic Tube System & Automated Guided Vehicle
More specific to delivery system, recently, parcel keeping lockers are widely being installed in the
lobbies of apartments. Deliveryman puts the the parcel into the locker, also known as “Ubi-lockers,” without
delivering them door-to-door. This prevents the deliveryman from taking the elevator and to use the related
energy as he/she only needs to put the parcels in the locker usually located on the first floor of the building. The
ubi-lockers keep the goods until the someone picks up the parcel. Actually, these systems are common in
Japan, where 95% of apartments have this kind of parcel lockers already in use. This recent system shows how
much the number of parcel deliveries is being increased, and how it has become an essential part of daily lives.
6
Fig 2.3 Ubi-locker System
2.1.2 Elevator Design
The two most popular types of elevators are: traction and hydraulic elevators. The way they operate and
their purposes are different one from the other. Hydraulic elevators use fluid in a tank, which is pumped and
released by the electric motor to move the car. Whereas, traction elevator, as one can see in fig 2.1., possesses a
counterweight (weight of elevator + 40% of its maximum rated load) that balances in the reverse direction to
displace the car. Hydraulic elevators are used in lower buildings compared to traction, as higher the building is,
it needs more fluid and power to move the car. Also, the installation fee and maintenance cost reaches higher.
However, it has advantages in space saving and initial installation cost [22]. On the other hand, the cable and
pulley system is considered more energy efficient, safer, and eco-friendly as they do not use hydraulic fluid.
Moreover, they are widely used in high-rise buildings and skyscrapers. In case of traction type, there are many
other parameters that have to be considered: gear, drive, roping, programming and motor. These can be decided
upon many perspectives; the installer‟s budget, the quality service targeted to its users and quantity of energy
consumption previewed by the building owner [8].
The freight elevator is comparatively a recent product due to the substantial increase in its need. In fact,
the freight elevator first existed in form of dumbwaiter. By definition, dumbwaiter is a smaller elevator that can
have all the performance characteristics of an elevator. Furthermore, the word freight elevator represents
elevator that can handle heavier mass and greater volume of an object, mainly for the purpose of its delivery,
not designed for passengers. Therefore, it does not need to satisfy lift standards. Consequently, it has looser
safety regulation. On the other hand, the service elevator stands for an elevator that can transport passengers, as
well as goods [15]. The elevator integrated delivery system plays a part in latter category of elevator as it is first
foreseen to respect passenger elevator standards and then to play an additional role as a delivery system.
7
2.1.3 Research Trends
Most of researches related to elevators are done in practical fields, largely by companies and
organizations rather than academic association. The researches can be divided into two general categories;
hardware or software analysis. Technical researches include physical replacement of motor type, elimination of
gear, and addition of safety equipment to increase service quality to its users. The second category has been
productive in the beginning of the electric elevators apparition. First, to serve as basis of elevatoring in a
building, calculation methods of round trip time, waiting time, and transit time have been investigated and
expressed in mathematical equations in terms of many other components of the physical lift and demand
specification. The corresponding equations are shown in section 2.2. Based on these equations, studies on how
to provide the right size, and design of elevators in buildings were followed as a new trend. Then, automatic
scheduling, various programs controlling the elevators have been investigated to increase elevator efficiency in
terms of passenger‟s waiting time and transit time. Currently, with the increase of interest in sustainability,
research on observation of energy consumption and ways to reduce electricity usage in lifts are one of the new
lift related research fields.
2.2 Passenger Traffic Pattern in Elevator
2.2.1 Passenger Traffic Demand
In the late 1960‟s, researchers such as Hall and Fruin took interest in pedestrians‟ movement in
buildings [9][12]. Also, lift‟s appropriate service profile has begun to be actively researched in the 1970‟s
namely by Barney, Santos, Peters, and Strakosch [17]. In European and North American countries, where
vertical transportation system is considerably studied, the designation of a specific elevator size and number
depends on the passengers‟ demand and pattern. Actually, peak hour passengers are usually expressed as a
percentage of total building population per 5 minutes. Moreover, the traffic can be categorized into four types
[4] :
I)
Incoming
II)
Outgoing
III)
Two-way
IV)
Interfloor
8
Where, incoming traffic consists of passengers that take the elevator from the home floor (usually first
floor) to other floors (upper floors). In opposition, outgoing traffic denotes passengers going out of the building
through the home floor from a certain floor. For example, in office buildings, incoming traffic mostly happens
in the morning, whereas the outgoing traffic is observed during the afternoon. However, in some buildings such
as apartments, the incoming and outgoing traffic is not very clear; this situation belongs to two-way traffic,
where both, incoming and outgoing happen in same time. Finally, the interfloor traffic designates trips that
happen in the same building, between floors that are not home floor.
2.2.2 Calculation Methods
Elevator design is purely in concern of its passengers; the time that they have to wait to get on the lift,
and the time they have to travel to get to the destination floor. By definition, round trip time denotes time taken
for a lift to make a single round trip of the building. The waiting time is the period of time that a passenger
spends waiting for an elevator car measured from the instant that the passenger arrives in front of the lift until
the instant the passenger enters the car. Moreover, transit stands for the period of time that a passenger spends
travelling in an elevator car, measured from the instant that the passenger boards the car, until the instant that
the passenger alights at the destination floor. Finally, time to destination includes both, which can be explained
as the sum of waiting time and transit. The diagram in figure 2.3 summarizes the listed time periods. These
numbers can be calculated in two ways, first by mathematical equation, which is the way that most of past
engineers employed, or by running simulations which is a more recent form of calculation method. Today, both
methodologies are still accepted, but the latter one gives a more detailed values. For instance, simulation can
measure each individual passenger‟s waiting time, but traditional observation by analyzer uses the hall call
response time. Therefore, the simulation gives a better evaluation of quality of service as they reflect the true
experience of each passenger instead of grouping the time period. However, assumptions used in the tool
become very crucial and good knowledge of simulation handling is required to get true and acceptable values.
9
Fig 2.4 Diagram of Different Time Periods
First vertical transportation system studies and traffic planning‟s are mostly done in terms of office
buildings as they have typical repeating traffic pattern; morning (incoming) and afternoon (outgoing) peaks. In
most of the office building cases, up-peak consideration satisfies when calculating for the installation
requirement of the lift. Barney and Dos Santos introduced the conventional round trip time equation of a single
lift during the up-peak traffic condition, which is given by:
RTT =
(
)(
)
Eq. 2.1
[5]
{Where H: highest reversal floor tv: floor transit time S:average number of stops
P: average number of passengers tp: one-way single passenger transfer time}
The equation is subject to a number of assumptions, such as that the passengers arrive uniformly in
time, and that all interfloor distance (3.3m) and flight times are equal. Tregenza has argued that the arrival rate
of passengers follow Poisson process rather than uniform density function [30]. However, in later publication of
Barney, he defends that the values of S and H using the Poisson probability density function are always smaller
than ones with a rectangular probability density function [2]. This means that the assumption of uniform arrival
of passengers gives slightly conservative designs. Thus, it is also acceptable to simplify the passengers‟ arrival
rate into uniform distribution.
10
Respectively, the values of H, the highest reversal floor and S, the expected number of car stops above
the lobby are shown by Shroeder and Jones by the following equations :
=
S =N {
∑
( )
(
Eq. 2.2
) }
Eq. 2.3
[21]
[6]
{Where N:number if served floors above the main terminal P: average number of passengers}
These equations assume that all floors are equally populated, in other words, all floors have the same
transfer demand. Also, that people only enter from the lobby floor to travel in up-going direction.
The values for probable up stops have been calculated and they are charted. The table is still widely
used by lift traffic engineers (Appendix 1, Table A1). This table deals with up-peak condition with different
number of floors (N) and car loads (P). In later publication, Barney also argues that the average number of
passengers (P), also understood as car loads can be assumed to 80% (%CF) of actual maximum car capacity
(AC) [4].
In two-way system, the H-S table can be used to determine the probable up stops, as it is same as in the
up-peak condition. On the other hand, the two-way probable down stops are assumed to be about 70~80% of
the probable up stops [24].
Consequently, contrarily to incoming peak where one is concerned only about the upper floor traveling
passengers, the required maximum waiting time and round trip time should satisfy both the up and down travel
passengers in the case of two-way traffic. The passenger‟s tolerance towards the travel and waiting time will be
discussed later in this chapter.
The number of people served in a given period of time, also called the handling capacity, is calculated
from the round trip time of an elevator. The basic time period is generally established as a 5 min. The 5 min
observation has been used for office buildings; however it is found that 5-minute is also a convenient time
period to measure peak traffic on elevators in any type of building [19]. The two-way handling capacity can be
calculated from the following formula:
11
HC =
𝑃𝑡 𝑥 300 𝑠𝑒𝑐𝑜𝑛𝑑𝑠
Eq.2.4
𝑅𝑇𝑇
[27]
{Where HC: Handling Capacity Pt: Passengers travelling up & down RTT: Round trip time}
These equations contribute in finding passenger‟s average waiting and transit time. There are respective
equations to find the two required times; however it will not be shown in this paper, as it demands complexity.
In this paper, the simulation tool is used to find these values. Peters has completed the „general analysis‟
method to complete the research, which plays part in his developed software „Elevate‟ [18].
The General Analysis allows assessing Round trip Time of two-way peak traffic of residential buildings
and many other surpluses. In this study, the simulation software, which employs the General Analysis method,
is used to analyze the proposed residential building‟s traffic [7]. Furthermore, Peters has also proposed to
correct the conventional RTT formula by enabling calculation in flight time for any given interfloor distance
and also allowing other conditions such as express zone [17].
2.2.3 Passengers face to time tolerance
According to several studies, passengers get impatient after 60 seconds of waiting time in residential
buildings. Therefore it is suggested that the maximum waiting time does not surpass 60 seconds. In the case of
trip time, a ride of about 100 seconds becomes the limit of tolerance for people in an elevator making several
stops, each for one person. Tolerance will lengthen to about 150 seconds if a few people are being served at
each stop; the „average person‟ feels more tolerant if two people are being served at a time, Finally if monotony
is relieved by a changing scene, passenger may tolerate a ride as long as 180 seconds (Table 2.1). Although
these are values found from observations, the time can be influenced by passengers‟ mission, urgency,
atmosphere, feeling, etc. [26]
Standard Waiting Time
Transit Time Tolerance,
Transit Time Tolerance,
Transit Time Tolerance,
Tolerance in Residential
one passenger per each
few passengers per each
when relieved from
Building
stop
stop
monotony
60 sec
100 sec
150 sec
Table 2.1 Passenger‟s Time Tolerance
12
180 sec
Chapter 3. Elevator Integrated Delivery System
This section explains the new delivery system patented by the author of this paper. Throughout the
thesis, different possible operations of this specific delivery system are observed. Therefore, it is important first
to understand the concept of this new elevator before pursuing the reading of current paper.
3.1 Elevator Integrated Delivery System
As described in introduction, this paper deals with a new system of delivery so-called “Elevator
Integrated Delivery System” [31]. Basically, it consists of a standard elevator used in any buildings, which
additionally and automatically collects the delivery goods and dispatches them without any human intervention.
A patent has been filed and accepted on this topic, and this paper reviews its feasibility in its operation system
rather than its mechanical system. Although for realization of this project, the physical elements are essential to
be reviewed; this paper only covers its conceptual physical component as a start of the research.
Fig 3.1 Different Possible Combinations of Elevator Integrated Delivery System (Section)
Broadly, the elevator space can be formed in different combinations such as in figure 3.1. The first and
second images show the case when the goods space is placed on top or on the bottom of the passenger‟s
allocated space. Then the third image shows the goods space on one side of the elevator vehicle. In the previous
case, it is inevitable to investigate the floor height of the building to consider its feasibility. Typical floor height
of residential and office buildings are 2.6m and 3.5m respectively. In case of residences, there is a need to
lower the elevator ceiling so that certain size of good can get into the elevator or to remove a part of floor slab
to transport the parcel under the floor. However, if one looks at the office building, there is plenty of head space
left to transfer the goods in the elevator car.
13
Elevator
Shaft
Service Area
Service Area
House Unit
Fig 3.2 Typical Building Section & Elevator Integrated Delivery System Section
The latter case will be used to explain the system for easier understanding. Figure3.2 left image shows a
typical simplified section of a building. The highlighted spaces measuring normally 60-80cm, represent the area
between the ceiling and its upper floor slab. The idea is to move and deliver the good to the home owner using
this unused space. Again, the system can work in different way, using this same space. Right side image
explains in detail how the system works for the case when the goods are to be placed on top of the elevator
vehicle. The movement of the goods can either take place in form of robotic self-moving parcels, or transported
by horizontal systems of rail. Other possibilities are also open.
Each of them can detect their destination through sensors such as RFID tag. They can be distributed
from domestic parcel delivery center, or local post office describing where and which floor the parcel is headed.
Then, a RFID reader from the elevator can direct the parcel to the addressed floor. If the parcels are to be
collected first, and then to be distributed, parcel pool is to be located near the lift machine room, on the first or
underground floor depending on the physical structure.
Unit A
Unit B
Unit A
Elevator
Unit B
Fig 3.3 Plan view of Building with Elevator Integrated Delivery System (General & Zoom in)
14
Figure 3.3 shows the floor plan of a building using the elevator integrated delivery system which has
the parcel compartment on the top of the elevator vehicle. As an example, a direct access type apartment layout
is chosen to display how the system works. A separate elevator doors are to be located in both sides of the car
that permit the good to exit independently from the passengers to the addressed household. Section drawing in
figure 3.4 gives additional explanation to the figure 3.3 to better understands the system. In fact, once the parcel
gets out of the car, it can slide down from the ceiling to the floor using a simple system as shown below. Again,
this is one example out of many possibilities.
Elevator
Shaft
(Goods)
Unit A
Elevator
(Passenger)
Service Area
Unit B
House Unit
Service Area
Fig 3.4 Detail Section View of Integrated Delivery System
Compared to the ubi-lockers mentioned in section 2.1, this system has advantage that the parcel receiving
person does not have to descend from his/her house to get the package. If a person has to come down and get
the package, it would not make any difference than having a deliveryman handing over the package as an
individual is asked to take twice the elevator (going up and down or vice versa) to receive the package.
Moreover, the package receiving person is face to waste of energy as he/she has to carry the parcel to upper
floors. Therefore, this system has a different approach of delivery service compared to the parcel keeping
lockers.
15
Chapter 4. Methodology
In this Section, the five models of delivery system investigated are listed and the used simulation tool
„Elevate‟ is presented. Moreover, the condition of assumed analysis, building, elevator, passenger and goods
are overviewed. For each, assumptions and their background reasoning are included.
4.1 Five Observed Models of Delivery System
In this study, five different cases are modeled and simulated using the elevator traffic software
„Elevate‟. In each case, the corresponding average waiting time, transition time and time to destination of an
individual are observed. Additionally, the total waiting time of packages and the vehicle energy consumption
are calculated and measured. The investigated five models are as follows:
I)
Current Delivery System (Manual Instantaneous Delivery)
II) Instant Delivery with Goods Integrated Elevator
III) Interval Delivery with Goods Integrated Elevator
IV) Overnight Delivery with Goods Integrated Elevator
V) Delivery with Separate Freight Elevator
Case I), the current delivery system is first observed to serve as the basis for comparison. By current
delivery, it means a manual delivery system in which a person has to take the elevator similarly to other
passengers of the edifice from the home floor to the destination floor. Then, this delivering person takes back
the vehicle with or without other passengers to exit the building. The period of time that this person debarks
and embarks again in the elevator is set to five minutes. This period includes the process for the delivery man to
walk to the addressed house, ring the doorbell, wait until someone answers the door, deliver the parcel and
finally walk back to the front of the elevator. In other words, the deliveryman arrives back and waits for the
elevator after five minutes of his/her debarkation at the same floor (other than first floor). The additional
passenger waiting time, transit and time to destination that are caused by this system are observed, as well as
the waiting time of the parcels to reach its destined address from the moment it arrives into the building.
16
Cases II) to V) are alternative delivery systems that can be operated. Case II), instant delivery with
goods integrated elevator follows exactly the same procedure as the first one without any manual interruption.
This implies that there is no need for individual to walk to the house, nor to wait for someone to answer the
door. Case II) only counts the time that the good has to wait for the actual delivery to take place (in this case,
nearly zero as it features instant delivery), its transition time and it‟s time to destination. Passengers additional
waiting time, transition time and time to destination are also observed.
In case III), parcels are collected until it reaches a certain number on the first floor. In this study, this
number is set to six, as it is assumed that the elevator takes six goods at once (see section goods delivery).
Therefore the elevator dispatches the parcels whenever six of them are collected.
The overnight delivery, Case IV) implies that all packages are collected at the first floor and wait until
the time where there is no recurring passenger demand. This usually happens in night time in residential
buildings. Using Strakosch residential demand pattern, it is assumed that the dispatching of goods is assumed to
start at 10 o‟clock in this study. This represents an extreme case where the waiting time of packages is
comparatively high to any other suggested operations. It is interesting to compare its energy consumption face
to other conditions, and to see whether or not it is beneficial to operate in such a way in terms of energy saving
and passengers time saving.
Case V) represents a case in which a separate elevator is reserved for goods delivery only. Often, freight
elevators can be found in big commercial buildings or office buildings, but it is rare to be installed in a
residential building. This may be because freight elevators require high energy use and installation fee.
However, its installation can allow fast delivery (no waiting time for package delivery) and no additional
waiting time for the passengers due to goods‟ delivery. Again each case will be investigated in terms of
passenger waiting time, transit, and time to destination. Similarly, in turn, goods‟ total time to destination,
transition time will be calculated as well as the required elevator‟s energy consumption.
a)
b)
c)
d)
Fig 4.1 Illustration of Case a) I, b) II, c)III d) IV e)V
17
e)
4.2 Simulation Tool
For the investigation of different case studies mentioned above, simulation tool „Elevate‟ developed in
1989 by Peters Research Ltd. is utilized. Due to the continuous updates and supports offered to the software
users, „Elevate‟ is broadly known and it is used in more than 60 countries. Specifically, in this paper, Elevate
8.0, the latest version updated in 2007 is employed. The developer of the software, Dr. Richard Peters is one of
the most active elevator engineers in practical and theoretical ground. Moreover, the software has been
extensively tested and compared by Peters Research Ltd. with reliable calculation program or method as well as
other old software programs. In fact, the program is appointed as the international standard for Elevator Traffic
Analysis and it is approved by many vertical transportation engineering experts including Gina Barney, Bruce
A. Powell, Lutfi Al-Sharif, and many more [1], [16], [20].
4.3 Research Data & Assumptions
4.3.1 Analysis Data
The dispatching algorithm used in the simulation is group collective algorithm. Among other dispatcher
algorithms such as down collective, destination control, double deck, etc., the reason why group collective is
chosen is because it represents the standard and simplest algorithm used in elevators. Moreover, other forms of
dispatching systems (heuristic algorithms) are used when there is more than one elevator, or when the demand
and destination floors have a specific known pattern. In the subject of this paper, it is reasonable to observe the
most standardly used lift algorithm, as it is the one that is often employed in residential buildings that has one
elevator. Group collective stands for elevator control that travels in one direction and collects all calls
registered. When there are no more requests in that direction, it turns around and answers again other calls from
the reversed direction. When there are no calls from the passengers, the car stays idle, or moves to the home
floor. The elevator will stop only for up landing calls in the up direction and down calls in the down direction,
where all calls are being remembered until answered. In Elevate 8.0, the provided group collective algorithm is
programmed so that the elevator returns to the first floor (home floor) when there are no more calls. In terms of
energy, the marketing price of one kwh is measured to 123.01won according to Kepco (Korea Electric Power
Corporation) in February 2011. If this value is converted to U.S. currency, it corresponds to 0.11$ per kwh. This
price is used in this study to calculate the monetary energy cost for each case. Energy modeling is a complex
subject that has dependency in the mass of car, its efficiency, counterbalancing ratio, rope length, etc. Although
18
there exists always electricity usage even when the elevator is not in use, for standby mode and lighting, etc.,
„Elevate‟ only takes account the energy used while the car is traveling. However, it takes account the loads
(mass) and their direction for energy modeling (see table 4.1). The power consumed during each trip can be
defined for different loads (0%, 25%, 75%, 100%), in both up and down directions. Drive off value corresponds
to the circumstance when the vehicle is in standby mode, which usually happens during nights when there is no
elevator usage. However, in this study, standby mode is not considered. On the other hand, drive on value refers
to the amount of energy consumed when the car stays idle. Notice that depending on the elevator‟s direction
(upward or downward), the relationship of percentage load to energy reverses. When the elevator goes up, it is
advantageous to have smaller load, whereas when the elevator goes down, it is advantageous to have heavier
total load. Other all non-specified components are run in preset configurations of „Elevate‟.
Drive
Drive
0%
25%
50%
75%
100%
0%
25%
50%
75%
100%
Off
On
up
up
up
up
up
down
down
down
down
down
1.6
1.6
1.6
1.6
8.3
15.6
22.9
18.6
11.3
4.2
1.6
1.6
Table 4.1 Electricity Consumption for Various Loads and Direction (Unit: Kw)
4.3.2 Building Data
In Korea, the required size and number of elevators are defined by the Building Codes. Exceptionally
for residential buildings, the floor area is not concerned, but the type of apartment and the number of
households are considered. There are two typical apartment types in Korea; direct access type or corridor
system type as shown in the figure 4.2.
Fig 4.2 Floor Plan of a) Direct and b) Corridor Access Types
19
Accordingly, regulations on lift installation are predefined by the Korean Building Codes 2, stated as
below:
- Apartments having more than 6 floors are required to install lift that can ride more than six persons
- In case of direct access type apartment, one or more lift per access should be installed, and the lift
should be able to ride the total number of households above fourth floor multiplied by 0.3 (0.15 if each
household unit is designed for one person residency)
- In case of corridor system type apartment, one or more lift is required at every hundred households.
The lift should be able to ride the total number of households above fourth floor multiplied by 0.2 (0.1 if each
household unit is designed for one person residency)
Due to the demand for privacy and increased concern on environmental quality in modern society, the
direct access type is much more popular to the consumers and it surpasses the corridor system type apartments
in quantity. Accordingly, this study focuses on direct access type, which can have at most four households, two
on each side of the stair hall. Moreover, according to National Statistical Office, most of the Korean apartment
measures fifteenth floor; this number is also used as a standard in this paper, where each floor height is
assumed to 2.6m, a common height used in residential buildings. In fact, the height of the floor is served in
calculating the traveling time between levels by the elevator in the simulation tool.
For instance, if the building has 60 households, which means four households per floor (maximum
number of possible households in direct access type), it has 44 households above fourth floor. Then, if 0.3 is
multiplied to the value as mentioned in the code, it results to 13.2. This means that the elevator has to be able to
ride more than 13.2 persons at once, and one lift is enough as a mean of transportation. Consequently a
standard 1000kg, 15-person of 65kg elevator will be selected (see elevator data section). As seen above, the
number of car and its size depend on the demand. Most Asian countries follow similar procedure to determine
the appropriate installation of elevator.
Moreover, the entrance level bias on first floor is set to 100%. In other
words, the incoming traffic only enters the building from the first floor to reach their destination. Furthermore,
it is assumed that there is no interfloor traffic, as there is very low probability that a resident visits another
resident in another floor. This also means that, exit level bias on the first floor is 100% as well; all outgoing
traffic leaves the apartment through first floor. No percentage of absenteeism is accorded in the building.
2
Act on Housing Standards, Article 4 : Lifts
20
4.3.3 Elevator Data
In the observed building, it is assumed that there is one single deck elevator that responds to determined
passenger and goods demands. Single deck traction type elevator stops at each floor whenever there is a request
call from or to the floor specified. The assumed capacity of the elevator is 1000kg, which corresponds to
maximum 15 passengers in vehicle. Moreover, the most widely used 1000kg elevator has a car area of 2.4m2
(1.5mx1.6m).
The capacity factor of the elevator is set to 80%, which means that when the elevator is 80%
full in terms of mass (800kg), people are subject to refuse to embark in the car as it feels full. This is a common
phenomenon observed and described [23]. Therefore the elevator has almost always a non-occupied mass of
20% of the full capacity. In present research, this mass is fulfilled by the parcels‟ mass (6x30kg=180kg). The
time that takes the elevator to open its door is assumed to 1.8 seconds and 2.9 seconds to close. Similarly,
elevator‟s speed, acceleration, jerk, and start delays are 2.5m/s, 0.7m/s 2, 1.4m/s2 and 0.5s respectively. These
are common standard values that are given in „Elevate,‟ these are on-field measured numbers. Table 3.2
summarizes the conditional assumptions of the elevator, where there is no time accounted for leveling delay. In
mid-rise buildings, the conventional elevator geared non-regenerative drive is the most commonly employed.
Although geared non-regenerative has a higher energy efficiency than hydraulic elevators which are used in
low-rise buildings (usually less than seven floors), the use of geared generative or gearless drive could better
raise the efficiency and reduce the energy consumption. However in this study, again, for standardization, the
traction typed geared non-regenerative drive is investigated.
Capacity
1000kg (15 pers)
Car area
2.4m2 (1.5mx1.6m)
Capacity Factor (mass)
80%
Door opening
1.8 second
Door closing
2.9 second
Speed
2.5m/s
Acceleration
0.7m/s2
Jerk
1.4m/s2
Start delay
0.5 second
Table 4.2. Elevator Data Values
21
4.3.4 Passenger Data
The passenger pattern depends on the type of buildings; in office buildings, a clear critical up-peak and
down-peak periods can mostly be observed during the morning and afternoon. In certain cases, interfloor
traffic, which represents traffic from one floor to the other that is not from or to lobby floor, can also be seen.
Whereas, in apartment buildings, these clear up-peak and down peak periods are harder to recognize. In fact,
residential buildings have a two-way traffic demand. The two-way lobby traffic means a type of traffic where
people embark to the car from one entrance and leave at various stops during the up trip. Contrarily, during the
elevator‟s down trip, people board to the car from various stops and get off at one exit floor (lobby floor). In
result, the two way traffic‟s round trip time is considerably longer than in the case of incoming traffic, as the
elevator has to serve up and down passengers on each round trip. However, residential apartment critical period
serves much less passengers (~5% of population) than in office buildings. In this study, data from Strakosch
residential surveys is used. The residential demand has been collected from various residential apartments and
it is represented in terms of percentage of building population that travels up or down in period of five minutes.
In this case, the critical traffic happens in the late afternoon (15:00~) or early evening period (18:30~) when the
tenants and children return home from elsewhere while others leave for evening entertainment [27]. Few other
forms of traffic can be observed in figure 4.3, such as in the morning, there is a clean downward demand that
represents people leaving from home to work or school. However one can observe that it is not the most critical
traffic in terms of total passenger activity from the following graph in figure 4.4. Moreover, in residential
buildings, interfloor traffic rarely happens, as the apartment‟s tenants usually do not visit each other. For this
reason, interfloor traffic is ignored in this study. Furthermore, it is assumed that an individual takes 1.2 second
each to get on and also to descend from the vehicle. Also, there is zero stair factor, meaning that the passengers
have no choice to whether or not to take the elevator. They necessarily move one floor to another using the
elevator whether he/she has to wait a long time or the car is full or even in the situation where the traveler
resides in lower floor. In European and North America, elevators are designed for a standard individual mass of
75kg and 0.21m2 of area occupancy, however, in Asian countries; standard mass is set to 65kg. Accordingly,
this study has employed 65kg as the standard mass of individual to match Korean manufactured elevator.
Nevertheless, the area occupancy of the lift already gives a smaller constraint; if the elevator is full in terms of
area having 100% of capacity factor, it represents 11.4 occupants (2.4m2/0.21 m2 ), and when the elevator is
full by mass with 80% of capacity factor, it gives 12.3 passengers(800kg/65kg). This means that no more than
11 passengers can occupy the elevator at one time.
22
Fig 4.3 Example Passenger Demand Graph of Strakosch Residential Template
Fig 4.4 Example Total Passenger Activity Graph of Strakosch Residential Template
23
4.3.5 Goods Data
According to statistics described in Chapter 1, if there are four households of four family members per
floor, and if each of them get about three packages per month, the total number of monthly parcels to be
delivered results to 720. This issues a shipping of 24 parcels per day in average. The number of parcels
addressed to first floor can be ignored, as they do not use elevator as mean of transportation, which results to
22.4 parcel deliveries per day. However, six parcels are assumed as one unit of elevator trip considering their
mass and size (see figure 4.4). Then, if one evaluates the number of parcels rounded up in units of elevator
trips, 24 parcels can be kept for delivery per day calculation. 24 deliveries per day equals to four elevator
travels. Without doubt, four lift trips can easily get into daily use of elevator without interrupting the current
usage. This implies that with the current number of deliveries, it is not enough to consider the installation of a
whole new elevator system. One can conclude that 24 parcels a day are not enough to employ the new system
suggested. As mentioned in the first part of the thesis, the number of delivery is increasing in a dramatic
fashion. With this in mind, the current paper deals with future condition and system of elevator delivery.
Consequently, higher amount of deliveries are considered, where two possible demands are investigated for
each case; when there are about three times and ten times more amounts of parcels than present. This can
translate into 72 and 222 packages per day respectively, or 12 and 37 additional travels of elevator caused by
the parcels respectively. The two conditions are summarized as below:
Delivery Demand Assumption 1
Delivery Demand Assumption 2
72 parcels/ day
222 parcels/ day
1 parcel/10 minutes
1 parcel/ 3.33 minutes
12 travels/day
37 travels/day
8 a.m to 8 p.m ]8,8]
8 a.m to 8:20 p.m ]8,8:20]
~3x current demand
~10x current demand
Table 4.3 Two Observed Cases of Demands
24
With the previously determined size of the elevator (see elevator data), the area that is allowed equals to
2.4m2. Usually, a parcel is defined as an object in which the sum of height, depth and length measures less than
160cm and weighs less than 30kg. If simply, one assumes that the parcel box consists of three equal sized sides,
each of them measures 53.3cm. Accordingly, the elevator area is divided into six rectangles. Furthermore, if
one assumes that each parcel weighs 30kg, it sums up to 180kg, which is less than the remaining authorized
mass (200kg) of the lift‟s maximum weight (see elevator data).
Fig 4.5 Plan View of Elevator Division into Six Rectangular Areas for Goods Embarkation
25
Chapter 5. Results & Analysis
In this Chapter, the results for five models of delivery system, explained in previous chapter, are given
in components of passenger waiting time, transition time and time to destination. Two scenarios have been
investigated for each case; when there are 72parcels/day and 222parcels/day. Additionally, total parcel
waiting time is also calculated. Finally, numerical and monetary values of electricity usage are established for
each case to determine which operation mode is better in terms of energy saving. For each, ten randomly
generated demands that respect Strakosch residential pattern have been employed. In result, all of the
demands showed similar trend subject to daily average waiting, transition, and destination time (see figure
5.1;5.2;5.3). In these graphs, the Cases IV) Overnight Delivery & V) Separate Delivery, are excluded.
Although the numbers may be different, all cases have the same tendency in increasing and decreasing of the
interval face to different cases. With this in mind, one representative hourly dataset out of ten is chosen and
presented in this chapter to keep consistency and also to be able to compare and justify the relationship among
the five cases. Again, its conformity has been proven with ten different data pool.
Fig 5.1 Waiting Times of 10 Datasets (excluding separate elevator & overnight delivery)
26
Fig 5.2 Transit time of 10 Datasets (excluding separate elevator & overnight delivery)
Fig 5.3 Time to Destination of 10 Datasets (excluding separate elevator & overnight delivery)
5.1 Results & Analysis
5.1.1 Case I) Current Delivery System (Manual Instantaneous Delivery)
The current delivery that has 72 parcels and 222 parcels a day are simulated respectively. By current
delivery, it means a situation where a deliveryman is artificially put into existing travel demand data that
consists of home tenants tracking. Depending on the two scenarios mentioned above, the deliveryman visits the
apartment either once every ten minutes or once every 3.33 minutes. The data is modeled such that man
manually delivers the parcels and exits the building. Therefore, the deliveryman is necessarily assumed to
return to the home floor after five minutes. The five minute period includes the time for the deliveryman to
27
arrive at the door, ring the bell and hand down the parcel to the client. If one compares the current data to only
passenger data, one can obtain additional 142 and 444 elevator call demands (for going up and going down for
each delivery) distributed equally during the elevator operation hours, from 9 a.m. to 9 p.m. for 72parcels/day
and 9 a.m. to 9:20 p.m. for 222 parcels/day. It means one parcel delivery per ten minutes and 3.33 minutes
respectively. This situation can easily be noticed in the passenger demand graph of figure 5.4. Green stands for
number of travelers going up, and red is used for downward passengers. The left graph shows the case when
there is only passenger without goods delivery, whereas the right side graph represents the current delivery
system with 222 parcels per day. The latter has bigger green and red area compared to the left one because the
deliveryman is counted as another passenger traveling up and also down without any modification in mass and
area occupancy compared to the only passenger case. In another words, each parcel is considered as another
passenger with an additional downward travel. Therefore, if the car is full with passengers, the delivery has to
wait until the next elevator trip.
1 No. 1000 kg elevators @ 2.50 m/s
Run no: 1
Total Passenger Activity
30
30
13
13
28
12
26
11
24
10
22
28
12
24
persons per 5 minutes
22
20
9
18
8
16
7
14
6
12
5
10
4
8
% population per five minutes
persons per 5 minutes
26
4
2
08:00
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
11
10
20
9
18
8
16
7
14
6
12
5
10
3
6
0
07:00
Total Passenger Activity
Incom ing - green; Interfloor - yellow ; Outgoing - red
Incom ing - green; Interfloor - yellow ; Outgoing - red
4
8
3
6
2
4
1
2
0
07:00
0
22:00
% population per five minutes
1 No. 1000 kg elevators @ 2.50 m/s
Run no: 1
2
1
08:00
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
0
22:00
time (hrs:min)
time (hrs:min)
Fig 5.4 Passenger Activity Graph: Only passenger and Case I) Current Delivery System
Obviously, as more traffic is introduced in the demand, the average waiting time, transit and time to
destination increase. The results obtained from the modeled simulation are as followings:
Unit: Seconds
Average
Waiting time
Average Transit
Average Time to
Destination
Longest Time
to Destination
Only Passenger
44.5
35.4
79.8
145.7
72 goods/day
46.9
37.3
84.2
189.6
222goods/day
57.3
41.4
98.7
195.9
Table 5.1 Summary Table for Passengers of Case I) Current Delivery System
28
The table shows that with 72 goods of delivery per day, the average passenger waiting time and transit
time each increases about 2 to 2.5 seconds only. In turn, it results in average 4.4 seconds of delay in terms of
passenger journey time. On the other hand, 222 goods of delivery per day requires additional 12.8 seconds in
terms of average waiting time and 6 seconds in average transit, both equaling to 98.7 seconds of average time
to destination. The increase in journey time is direct function of number of goods; however it is not necessarily
proportional to it. Furthermore, the time to destination increases more rapidly as the number of goods gets
higher.
Secondly, the total distribution time for goods is investigated. In order to calculate the total waiting
time, the following equation has been used:
Total Time for Distribution = ∑𝑛 𝑅𝑖
𝐷𝑖
Eq. 5.1
n=number of parcels/day, R=Reserved Time, D=Dispatching Time (unit: Seconds)
The transit time and waiting time are required depending on the traffic that already exists from the
home tenants. There is no goods reserved time, which can be defined as the time that the goods spend on the
first floor from its arrival on the purpose of collection delivery. In this case, as the deliveryman instantly
delivers the parcel, this value is considered to be zero. Furthermore, total dispatching time represents the sum
of total waiting time and total transit time, which is in this case, equal to total destination time. Moreover,
average time to destination of goods can be calculated by dividing the total time to destination with the total
number of parcels delivered. This procedure results to the values in the following table:
Unit: Seconds
72 goods/day
Total Reserved
Time
0
Total Dispatching
Time
4254.5
Total Time
to Destination
4254.5
Average Time
for Distribution
59.1
222goods/day
0
16815.1
16815.1
75.7
Table 5.2 Summary Table for Goods of Case I) Current Delivery System
In both scenarios, the average time to destination of goods is reduced about 20 to 25 seconds compared
to the related passenger‟s values. In fact, the waiting time in total dispatching time influences the most in the
reduction of time to destination. The goods transit time and passenger‟s transit time does not alter much, as
goods travel to same number of floors and they are distributed in same way as passengers. Also, it is assumed
29
that the goods take the same amount of time to discharge from the elevator as humans. However, the waiting
time is relatively low compared to passengers; this can be explained by the fact that the goods do not have a
peak distribution hour, they arrive in uniform function. For example, the number of passengers increases in late
afternoon in Strakosch residential template; however the amount of goods arriving in the late afternoon is
identical as anytime. Therefore the total waiting time at peak hours are smaller and contributes in reduction of
average time to destination.
Next, energy consumption of the modeled case is investigated. „Elevate 8.0‟ provides a tool that
calculates the energy consumption using the demand list. In the specific dataset observed, there are 2006 hall
calls from passengers. This number can vary from dataset to dataset, as well as from set number of people per
floor. Similarly to previous sections, ten datasets showed resembling tendency of increase and decrease of
energy consumption. Again, one dataset is used to represent the multiple cases in order to be able to compare.
Daily energy consumption of goods for Case I) Current Delivery System with deliveryman is shown in the
table 5.3.
Daily Energy Consumption
Monetary Value (0.11$/kW)
72 goods/day
70.11 kW
7.74$
222goods/day
69.46 kW
7.64$
Table 5.3 Summary Table for Energy Consumption of Case I) Current Delivery System
Interestingly, when there are 222 deliveries per day, it gives a lower amount of elevator‟s daily energy
consumption. Repeated simulations of other dataset have shown that this is not necessarily true. It is possible
that the electricity consumed in case of 222 goods gets bigger than 72 goods, but also vice versa. The reason
why is because the average interval (the gap in which elevator returns to the first floor) is measured around 3
minutes. This means that even if there is no goods to be transported, the elevator comes back to the home floor
every 3 minutes to transfer the passengers. Therefore, the energy consumed only depends on if the package is
lucky enough to get onto the elevator with the passengers right after its arrival, or if it is unlucky to arrive so
that the elevator has just departed from the first floor and is objected to descend back to the first floor to
answer the package hall call. This said, another fact of statement that can be made is that the weight of goods
does not contribute enough to make differences in electricity consumption. Therefore, whether there is 72
deliveries or 222 deliveries during the day does not decide the amount of electricity consumption, but rather the
number of packages taking the elevator together with the passenger influences the energy value.
30
5.1.2 Case II) Instant Delivery with Goods Integrated Elevator
In the case of instant delivery with goods integrated elevator, when a parcel arrives, it is instantly
distributed to the addressed floor. In other words, Case II) is similar to Case I), but it does not need a human, to
deliver the parcel. Therefore, there is no additional downward travel to the existing traffic. In this case, as
mentioned in section 3.1, loads arrive uniformly in a given period, and only one parcel embarks in the elevator
at each travel. In the case of 72 goods per day, the interval of arrival in delivery is set to ten minutes during 12
hours. Whereas when 222 goods are delivered per day, one delivery is previewed in every 3.33minutes from 9
a.m. to 9:20 p.m. The operation hours of the goods delivery system are integrated in usual residential traffic.
1 No. 1000 kg elevators @ 2.50 m/s
Run no: 1
Total Passenger Activity
1 No. 1000 kg elevators @ 2.50 m/s
Run no: 1
Incom ing - green; Interfloor - yellow ; Outgoing - red
30
Total Passenger Activity
Incom ing - green; Interfloor - yellow ; Outgoing - red
13
30
12
28
11
26
13
28
26
12
persons per 5 minutes
22
20
9
18
8
16
7
14
6
12
5
10
4
8
20
9
18
8
16
7
14
6
12
5
10
4
3
6
2
4
2
4
1
2
0
07:00
10
22
8
3
6
11
24
08:00
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
1
2
0
22:00
0
07:00
% population per five minutes
10
% population per five minutes
persons per 5 minutes
24
08:00
09:00
time (hrs:min)
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
0
22:00
time (hrs:min)
Fig 5.5 Passenger Activity Graph: Only Passenger and Case II) Instant Delivery
In the graph shown in fig.5.5, one can notice that the up going traffic has increased (green area), but the
red traffic stays the same. This is because, when automatic system is introduced, there is no need to use the
elevator to exit on the first floor. In this case, the parcels and passengers are differentiated with different mass
and area occupancy. The parcel weighs 30kg and occupies one sixth of the elevator area. They can always get
into the elevator even when the elevator is full with traveling passengers as the area reserved for goods is
separately positioned to the passenger area.
Unit: Seconds
Average Transit
72 goods/day
Average Waiting
time
46.5
36.2
Average Time to
Destination
82.7
Longest Time to
Destination
175.6
222goods/day
48.2
38.0
86.2
181.3
Table 5.4 Summary Table for Passengers of Case II) Instant Delivery
31
Compared to Case I), Case II) gives lower demand for in downward traffic. Also, the passengers
waiting time and transit time are reduced. Especially, when there are ten times more goods than present,
average time to destination decreases about 12.5 seconds, which is much smaller than when the goods are
delivered manually.
In terms of goods average time to destination, the calculation is done in the same manner as Case I)
(Eq. 5.1). Similar to Case I), there is no reserved time for goods, as they are instantly delivered through the
automatic system. Meanwhile, the waiting and transit time corresponding to all goods are summed up, resulting
in total dispatching time. These values are exclusively calculated by reviewing each goods case one by one.
This process has given the following outcomes:
Unit: Seconds
72 goods/day
Total Reserved
Time
0
Total Dispatching
Time
4350
Total Time
to Destination
4350
Average Time
for Distribution
60.4
222goods/day
0
14889
14889
67.1
Table 5.5 Summary Table for Goods of Case II) Instant Delivery
The difference is negligible for the Case I) & II) when there are 72 goods/day. One downward travel
every ten minutes in case I) is not enough to create difference in time. Nevertheless, when the lift is subject to
222goods/day, the story is different. There is a reduction of 8.6 seconds in goods average time to destination in
Case II) compared to Case I). This is due to the increase of travel time caused by the series of deliveryman‟s
downward travels in case I). As the vehicle calls grows and expanse to the peak hours, the Case I) necessarily
shows a longer average time to destination.
In terms of energy consumption, same rule applies as in Case I). The question of reduction of energy
use in 222goods/day compared to 72goods/day (Table 5.6) is not in interest, but the overall energy
consumption is to be investigated. One can notice that there is not much of difference with Case I), and this
will be explained further in discussion section.
Energy Consumption
Monetary Value (0.11$/kW)
72 goods/day
69.70 kW
7.67$
222goods/day
68.64 kW
7.55$
Table 5.6 Summary Table for Energy Consumption of Case II) Instant Delivery
32
5.1.3 Case III) Interval Delivery with Goods Integrated Elevator
Case III) consists of a system that delivers the parcels whenever six of them are collected. The parcels
wait on the first floor until they can fully occupy the elevator area reserved for goods. The difference with the
previous case is that the elevator does not continuously deliver the goods whenever they arrive. There is a
reserving time required for the parcels until the elevator begins to deliver. However, this waiting time, socalled “reserved time” does not surpass one hour (in case of 72goods) or 20 minutes (in case of 222goods).
This number is set as 72 goods per day equals to 12 travels of full elevator per hour and 222 goods per day
equals to 37 travels of full elevator per twenty minutes, during the daily hours of elevator operation. Fig.5.6
illustrates the case when there are 37 travels from 9 a.m. to 9:20a.m. In every 20minutes, one can notice an
increase of traffic in upward travel direction shown in green. Only upward demand is influenced as the
automatic delivery system does not need to come back to the home floor after the parcel dispatching. Similar
graph can be drawn for 12 travels per day, with loosen peaks. Accordingly, table 5.7 lists the passengers
waiting time.
1 No. 1000 kg elevators @ 2.50 m/s
Run no: 1
1 No. 1000 kg elevators @ 2.50 m/s
Run no: 1
Total Passenger Activity
Incom ing - green; Interfloor - yellow ; Outgoing - red
Total Passenger Activity
Incom ing - green; Interfloor - yellow ; Outgoing - red
30
30
13
13
28
28
12
12
26
11
10
persons per 5 minutes
22
20
9
18
8
16
7
14
6
12
5
10
4
8
3
6
0
07:00
08:00
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
20
9
18
8
16
7
14
6
12
5
10
4
8
3
2
4
1
2
10
22
6
2
4
11
24
% population per five minutes
persons per 5 minutes
24
1
2
0
22:00
0
07:00
% population per five minutes
26
08:00
09:00
time (hrs:min)
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
0
22:00
time (hrs:min)
Fig 5.6 Passenger Activity Graph: Only passenger and Case III) Interval Delivery
Unit: Seconds
Average Transit
72 goods/day
Average Waiting
time
46.4
36.9
Average Time to
Destination
83.3
Longest Time to
Destination
179.3
222goods/day
49.9
41.1
91.0
185.2
Table 5.7 Summary Table for Passengers of Case III) Interval Delivery
33
Average time to destination of Case III) represents values in between Case I) the deliveryman service
and Case II), the instant automatic delivery system in both scenarios with 72 goods and 222 goods. This proves
that the interval operation system with the provided conditions is less profitable than instantaneous automatic
delivery. The reason why is because the interval delivery still continues during the peak hours. In case of
instant delivery, the continuous delivery affects less in waiting and transit time as there is only one parcel to
deliver in each travel. However, in interval delivery, six goods have to be delivered every 20 minutes no matter
the hours of operation as the space for the parcels is separately reserved. In case II), the additional waiting time
is smaller as they are spread out to a small quantity. However, in this case, the smaller waiting times are
gathered together at once, which create longer waiting and transit time. This phenomenon can be observed
especially during the peak hours.
For goods, equation 5.1 is employed to calculate the total distribution time. First, all reserving interval
of each good, before the beginning of delivery, is measured. This means that although for some goods, the
waiting time is zero, the total average for goods waiting time is considered as some have to wait until
dispatching at maximum one hour or 20 minutes depending on the scenario (72 or 222 goods per day).
Unit: Seconds
72 goods/day
Total Reserved
Time
111600
Total Dispatching
Time
7250
Total Time
to Destination
118850
Average Time
for Distribution
1650.7
222goods/day
112200
22012
134212
604.6
Table 5.8 Summary Table for Goods of Case III) Interval Delivery
Compared to previous cases, the average time for distribution is much higher, obviously as in Case III),
there is no reserved time, the goods reserving time before the delivery influences much in the increase of value.
However, other factor such as goods waiting and transit time at the elevator affects the average time for
distribution. If one observes the average time for distribution excluding the reserved time, 100.69 seconds and
99.15 seconds are obtained for 72 goods/day and 222goods/day respectively. The fact that the second scenario
has a bigger value does not signify anything as the interval of delivery is different for each. However,
compared to the previous case of instant delivery, these values are much higher (~40 and ~30 seconds). Again,
this proves the inefficiency of collecting the goods for delivery operation. Therefore, instant delivery can be
said to be better than interval delivery in the given conditions in terms of goods average time for distribution.
34
In terms of energy consumption, there is not much of value shift from Case I) & II). One can notice that
here, 222 goods have higher value than 72 goods unlike in Case I) and II). Again as explained earlier, the
amount of energy consumed is almost independent from the number of goods delivered.
Energy Consumption
Monetary Value (0.11$/kW)
72 goods/day
70.37 kW
7.74$
222goods/day
70.69 kW
7.78$
Table 5.9 Summary Table for Energy Consumption for Case III) Interval Delivery
5.1.4 Case IV) Overnight Delivery with Goods Integrated Elevator
Overnight delivery implies that goods do not give any interference in passengers‟ travel. In Fig 5.7, one
can notice a tall upward demand at 10 p.m., which represent all of the daily parcels that are waiting to be
delivered. Therefore there is no additional waiting time and transit related to this situation (table 5.10).
Especially, the graph is drawn in bigger passenger range and shows the case of 72 goods in order to include all
the goods demand in the shown graph. For both scenarios, whether there are 72 goods or 222 goods a day, the
measured times for passengers stay the same. As mentioned in chapter 4, the parcels are only delivered when
there is no passenger, from 10 p.m. until necessary depending on the number of parcels to distribute. Please
note that the scale of graphs are different in Case IV) & V).
Run no: 1
Total Passenger Activity
Incom ing - green; Interfloor - yellow ; Outgoing - red
Total Passenger Activity
Incom ing - green; Interfloor - yellow ; Outgoing - red
80
80
34
75
32
70
30
65
34
75
32
70
30
65
persons per 5 minutes
26
55
24
50
22
45
20
40
18
35
16
14
30
12
25
10
20
8
15
6
10
4
5
2
28
% population per five minutes
persons per 5 minutes
28
60
60
26
55
24
50
22
45
20
40
18
35
16
14
30
12
25
10
20
8
15
6
10
4
5
2
0
0
07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00
0
0
07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00
time (hrs:min)
time (hrs:min)
Fig. 5.7 Passenger Activity Graph: Only passenger and Case IV) overnight delivery
35
% population per five minutes
Run no: 1
Unit: Seconds
Average Waiting time
Average Transit
72 goods/day
44.5
35.4
Average Time to
Destination
79.8
222goods/day
44.5
35.4
79.8
Table 5.10 Summary Table for Passengers of Case IV) Overnight Delivery
In average, it takes about 20 minutes to deliver 72 goods and about 62 minutes for 222 goods without
interruption. Although it does not take much time to deliver these goods, the total waiting time of parcels is the
greatest of the presented five cases (table 5.11). Total dispatching time is also the greatest; to calculate the
dispatching time, it is assumed that all goods arrive at the elevator at 10 p.m., and all the waiting time after 10
p.m. is measured. To calculate the total reserved time, the Equation 4.1 is used, as well as Equation 4.2 to
calculate the reserved time. The first term, 79200, in Equation 4.2 represent the beginning of the delivery time
which is 10p.m. in this case.
Reserved Time = ∑𝑛 79 00
𝐴𝑖
Eq. 5.2
Where, n=number of parcels/day, A=goods arrival time (unit: Seconds)
Unit: Seconds
72 goods/day
Total Reserved
Time
2052000
Total Dispatching
Time
45976.1
Total Time
to Destination
2097976.1
Average Time
for D stribution
29138.6
222goods/day
6282600
427754.6
6710354.6
30226.8
Table 5.11 Summary Table for Goods of Case IV) Overnight Delivery
Case IV) has also increased use of electricity, as there are elevator trips exclusively for goods delivery
(table 5.12) unlike in the cases when the goods are delivered together with the passengers as seen in Case I),
II), III). The additional 12 or 37 travels depending on the scenario each require a supplementary energy use of
3.2 kW and 6.88 kW. In this case, obviously the deliveries of 222 goods/day require more energy than when
there are 72 goods/day since more trips from the elevator are required.
36
Energy Consumption
Monetary Value (0.11$/kW)
72 goods/day
73.31 kW
8.06 $
222goods/day
76.52 kW
8.42 $
Table 5.12 Summary Table for Energy Consumption of Case IV) Overnight Delivery
5.1.5 Case V) Delivery with Separate Freight Elevator
The last observed case, delivery with separate freight elevator, is a case that can be seen in high density
office or commercial buildings. It is mostly employed when there are high demands in freight transportation
or when the sizes of parcels require a volume that cannot be transferred with passengers. In that sense, the
advantage of having a separate freight elevator is that there is less limitation in delivery goods and also that it
does not require additional time for passengers to destination. The conditions are similar to Case IV), but
there is neither waiting time for goods to be delivered in addition to absence of passengers waiting time. On
the contrary, the disadvantage of having a freight elevator is that an additional space, installation fee, and
operation fee are required. In the case of residential buildings, freight elevators are rarely installed as until
present, there is small demand for freight transportation.
Table 5.13 shows the values corresponding to case V). In terms of passengers‟ waiting time, again, like
in case IV), there is no additional time required and the values are identical to when there is only passengers
to transport.
Unit: Seconds
Average Waiting time
Average Transit
Average Time to
Destination
72 goods/day
44.5
35.4
79.8
222goods/day
44.5
35.4
79.8
Table 5.13 Summary Table for Passengers of Case V) Separate Delivery
The separate freight elevator permits neither reserved time nor the waiting time for goods. Therefore,
only transit time is considered to calculate the total time of journey. Table 5.15 shows that the average times
for distribution of goods are 20.1 and 20.5 seconds respectively for daily goods deliveries of 72 and 222.
Theoretically two values shall be the same, as it is an average value of transit time. However, the difference
may occur due to the demand of different traveling floors.
37
Unit: Seconds
Total Reserved
Total Dispatching
Total Time
Average Time
Time
Time
to Destination
for Distribution
72 goods/day
0
1445.9
1445.9
20.1
222goods/day
0
4541.1
4541.1
20.5
Table 5.14 Summary Table for Goods of Case V) Separate Delivery
In contrast to parcels‟ short average distribution time, the energy required for the distribution of goods
and passengers is the greatest among the presented five cases. Having two elevators consumes more energy
although the amount of the demand is same. In addition, the installation fee and maintenance fee makes the
current model uneconomical.
Energy Consumption
Monetary Value (0.11$/kW)
72 goods/day
95.4kW
10.49$
222goods/day
104.9kW
11.54$
Table 5.15 Summary Table for Energy Consumption of Case V) Separate Delivery
38
5.2 Comparison & Discussion
Table 5.15, 5.16, & 5.17 Summarize all numbers studied in section 5.1: passengers and goods journey
times, as well as energy consumptions for five cases presented. Overall, in terms of passenger‟s average
waiting time and maximum waiting time, Cases II) Instant delivery and IV) Overnight Delivery show the best
options omitting Case V) Separate delivery due to its economical disadvantages. However, Case IV) Overnight
delivery has the longest goods waiting time, and relatively high energy consumption.
In terms of goods, the choice shall be in between the Case I) Deliveryman, or Case II) Instant delivery,
with preference to the latter one. In fact, current delivery system, Case I) is observed to be a bad option,
however this system requires additional time, energy and workforce of the deliveryman. One can notice that if
the number of parcel deliveries augments, this system becomes more and more inefficient in terms of passenger
and goods average journey times.
Furthermore, it is interesting to compare Case II) Instant delivery and Case III) Interval delivery
operations. In terms of passenger and goods average time to destination, instant delivery is advantageous. This
is because interval delivery creates congestion at peak hours and consequently extends the waiting times. In
terms of electricity consumption, the overall energy consumption of Case I) deliveryman and Case II) Instant
and Case III) Interval are considered moderate. One can observe small difference of usage between these three
cases. In fact, when 10 datasets of these three cases have been modeled, they did not show continuous trend in
which one is the most advantageous mode. The advantageous mode changes one from the other depending on
the situation with small variations. This again can give a conclusion that the traffic and the elevator pattern
influences much in electricity usage, and between the three cases, there are not one mode that is more
advantageous than the other. Therefore all three cases show a moderate value in energy consumption, which are
also considered as the best three modes in the modes that have been investigated. Case IV) Overnight delivery
and Case V) Separate delivery are disadvantageous in terms of electricity usage, as it requires additional trips,
where Case V) uses the highest amount of electricity accordingly due to its higher required number of trips.
Overall, if one out of five options is to be chosen, the Case II), which is the elevator integrated delivery
system operating in instant delivery mode is to be suggested, as it provides good enough passenger‟s average
time to destination, best average time for goods to get delivered and moderate use of electricity. Case V) shall
be neglected in the comparison as it has different initial condition, which is the operation of two elevators
39
rather than one. The results have been summarized and rated in table 5.18.
+ to +++++
Passenger Avg.
Passenger Max.
Goods Avg.
Energy
Worst to best
Waiting Time
Waiting Time
Waiting Time
Consumption
Case I
+
+
++++
+++
++++
++++
+++++
+++
++
++
++
+++
+++++
+++++
+
+
+++++
+++++
+++++
+
Deliveryman
Case II
Instant
Case III
Interval
Case IV
Separate
Case V
Overnight
Table 5.16 Rating of Passenger‟s & Goods‟ Waiting Time and Energy consumption of Case I), II), III), IV), V)
Taking the idea further, actually, a combination of Case II) Instant delivery and Case IV) Overnight
delivery could be the most advantageous future operation system. Overnight delivery considers passenger‟s
traffic, by avoiding intervention in passenger‟s travels. For instance, Fig 5.8 can be an example of future
operation system, where the elevator integrated delivery system detects the low peak hours of passengers and
distribute the goods at these periods. Accordingly, a new Case VI) has been modeled to verify its feasibility. All
assumptions stayed the same as in any other previous cases studied, but with only delivery dispatching time
periods starting from 8:30 to 10:30; 13:30 to 14:30; 16:30 to 17:30; and so on from 21:30, which are the low
peak hours of traffic shown with gray rectangles in figure 5.8. In results, slightly reduced time have been
obtained in terms of passenger waiting time exposed to 72 goods per day, whereas a significant reduction of
time has been shown in case of 222 goods/day compared to the Case II) Instant delivery as shown in the
summary table 5.15. This gives another conclusion, that if one favors the reduction of passenger‟s waiting time
over goods‟ waiting time, it is wiser decision to adopt the Case VI) Combination as operation mode.
1 No. 1000 kg elevators @ 2.50 m/s
Run no: 1
Total Passenger Activity
Incom ing - green; Interfloor - yellow ; Outgoing - red
30
13
28
12
26
10
persons per 5 minutes
22
20
9
18
8
16
7
14
6
12
5
10
4
8
3
6
2
4
1
2
0
07:00
% population per five minutes
11
24
08:00
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
0
22:00
time (hrs:min)
Fig. 5.8 Possible Future Operation System: Off-Peak Delivery
40
Unit: Seconds
Average Waiting time
Average Transit
Case I
72 goods/day
46.9
37.3
Average Time to
Destination
84.2
Deliveryman
222goods/day
57.3
41.4
98.7
Case II
72 goods/day
46.5
36.2
82.7
Instant
222goods/day
48.2
38.0
86.2
Case III
72 goods/day
46.4
36.9
83.3
Interval
222goods/day
49.9
41.1
91.0
Case IV
72 goods/day
44.5
35.4
79.8
Overnight
222goods/day
44.5
35.4
79.8
Case V
72 goods/day
44.5
35.4
79.8
Separate
222goods/day
44.5
35.4
79.8
Case VI
72 goods/day
Combination
222 goods/day
Table 5.17 Summary of Passenger Time of Case I), II), III), IV), V)
Unit: Seconds
Total Reserved Time
0
Total Dispatching
Time
4254.5
Total Time
to Destination
4254.5
Average Time
for Distribution
59.1
Case I
72 goods/day
Deliveryman
222goods/day
0
16815.1
16815.1
75.7
Case II
72 goods/day
0
4350
4350
60.4
Instant
222goods/day
0
14889
14889
67.1
Case III
72 goods/day
111600
7250
118850
1650.7
Interval
222goods/day
112200
22012
134212
604.6
Case IV
72 goods/day
2052000
45976.1
2097976.1
29138.6
Overnight
222goods/day
6282600
427754.6
6710354.6
30226.8
Case V
72 goods/day
0
1452.8
1452.8
20.2
Separate
222goods/day
0
4561.8
4561.8
20.5
Table 5.18 Summary of Goods Time of Case I), II), III), IV), V)
41
Daily Energy Consumption
Monetary Value (0.11$/kW)
Case I
72 goods/day
72.03 kW
7.92$
Deliveryman
222goods/day
70.45 kW
7.75$
Case II
72 goods/day
71.03 kW
7.81$
Instant
222goods/day
72.47 kW
7.97$
Case III
72 goods/day
70.47 kW
7.75$
Interval
222goods/day
70.54 kW
7.76$
Case IV
72 goods/day
75.40 kW
8.29 $
Overnight
222goods/day
78.60 kW
8.65 $
Case V
72 goods/day
95.36kW
10.49$
Separate
222goods/day
104.91kW
11.54$
Table 5.19 Summary of Energy Consumption of Case I), II), III), IV), V)
42
Chapter 6. Conclusions
6.1 Research Summary
In this work, a new delivery system is suggested. This system uses existing elevator to load goods and
deliver them automatically to the addressed floor. The paper focuses on different possible operations of the
elevator integrated delivery system using Strakosch residential traffic pattern. Five specific cases with two
different scenarios each have been investigated in terms of passenger and goods waiting time: Case I)
deliveryman, Case II) Instant delivery, Case III)
Interval delivery, Case IV) Overnight delivery and Case V)
Separate delivery object to 72 handovers or 222 handovers of goods per day. Three factors, which are
passenger‟s waiting time, goods‟ waiting time and energy consumption have been considered as an output.
In result, in terms of passenger‟s waiting time, Case II), Case IV) and Case V) showed the smallest
values, whereas in terms of goods, Case I), Case II), and Case V) is shown to be advantageous. In energy wise,
Case I), II) and III) show moderate values compared to the others, but without order. This means that
depending on situation, one can be better than the other, and one cannot state that one is better than the other
without simulating or observing the arrivals of passengers and goods.
All in all, if one operation mode is to be modeled, the most appropriate mode can be said to be the
automatic instant delivery of parcels. The instant delivery mode also uses the low amount of energy, have low
passenger and goods‟ waiting times compared to the others. This conclusion shows that the elevator integrated
delivery has advantages over the existing way of distributing parcels and passengers, therefore its feasibility is
verified. This paper has opened a new paradigm to the increasing demand of domestic deliveries, by suggesting
a new concept of delivery as well as by confirming its advantages feasibility by comparing it to the existing lift
technologies.
43
6.2 Research Limitation and Future Work
As the current work is a first study of the new elevator integrated delivery system, it shows many
limitations. One of the reasons why is because the system is yet conceptual, and has not been built nor operated
in real situation. Although its physical operation is attainable, its installation and operation fee, and side effects
can be only observable after its establishment. Perhaps passengers could avoid taking the elevator at certain
time during the day, knowing that the delivery takes place.
In this paper, the side effects of its installation is
unknown, therefore it is assumed that all elements stay the same with or without the intervention of the
automatic delivery system. Moreover, all results are dependent of assumptions presented, which are considered
as standards. Yet, if one of the subjects changes, for example, the choice of elevator size; it would mean that
different number of goods and passengers can be transported in one trip. Then all values could bring different
results. Also different scenarios, amount of goods arriving per day, their arrival rate could bring changes.
Although the author believes that the beneficial operation mode is most like to remain the same, it shall be
tested with many more scenarios, such as combination of two delivery operation programs, or with different
number of parcel arrival rate. For instance, the off-peak delivery shown in section 5.2 could be a possibility. In
addition, one of the most concerning limitation of this work is that the residential pattern is strictly assumed to
Strakosch‟s data. Nevertheless, one can easily find that Korean residential pattern differs much from it. In the
future, surveys on Korean residential pattern shall be made to provide the passengers pattern that is appropriate
to Korean culture. It is expected that there will be additional operation after 10 p.m. Moreover, a detailed
physical planning of the system shall be made, and possibly implemented in a building as a mock-up where
data can be regularly obtained. With deeper knowledge and research on the subject, ways to develop better the
delivery program could be introduced; rules for priority delivery and standard delivery could be made, as well
as simultaneous detection of good to deliver can be programmed depending on the passenger ‟s destination
floor considering altogether the lift energy consumption and eliminating the time waste of passengers.
44
Appendix
Table A.1. Values of H and S with Respect to Number of Passengers Carried in Car (P)
45
요 약 문
주거 건물 이용자를 고려한
신개념 엘레베이터 통합 택배 시스템에 관한 연구
인구대비 국토가 좁은 한국과 이와 비슷한 환경의 다른 국가들에서 고층 건물(아파트 포함)은
도시에서 흔히 볼 수 있는 일반적인 건물 형태이다. 이러한 건물의 고층화 및 인구 과밀화와
더불어, 인터넷의 발달로 인한 온라인 상업의 비약적인 성장은 국내 택배시장의 발전에 큰 영
향을 주었다. 현재 국내에서 경제활동 인구당 연간 약 40개의 소포를 받고 있는 것으로 조사되
고 있고, 이는 앞으로 더 증가할 것으로 보인다. 본 논문에서는 기존 엘리베이터에 자동 택배
배달 시스템이 결합된 새로운 유형의 엘레베이터를 제시하였다. 또한, 두개의 택배 물동량 시
나리오를 바탕으로 다섯가지 운영 시스템을 살펴보고 비교분석하여 이의 타당성과 혜택에 대해
고찰하였다. 기존 아파트에서 보이는 사용자 패턴을 기반으로하여, 사용자 대기시간과 택배 대
기시간 및 에너지 소비량을 분석하였다. 결과적으로, 신개념 엘리베이터 통합 택배 시스템은
현재 택배기사가 방문 배달하는 것에 비해 더 유리하고, 소포량이 많아질 경우 사용자 대기시
간 절약 측면에서 더 경제적임을 보여주었다. 특히 물건이 오는 즉시 엘레베이터로 자동 배달
될 때 가장 효율적인 것으로 예측되었다. 비록 아직은 이론적인 연구에 불과하지만, 시작 단계
로써 택배의 새로운 접근 방법을 제안하였고 그 가능성을 확인하였다.
46
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b) Liftshop. 2011, “Supermec”, [Online Image]:
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50