Framework for vehicle routing planning system based-on the multipass simulation for inbound logistics in LCD manufacturing
Kiyoul Lee1, Hyunbo Cho2
Department of Industrial and Management Engineering
POSTECH (Pohang University of Science and Technology)
Hyoja San 31, Pohang 790-784, Korea
Email: point@postech.ac.kr1
hcho@postech.ac.kr2
and Mooyoung Jung †
School of Technology Management
UNIST (Ulsan National Institute of Science and Technology)
Banyeon-ri 100, Ulsan 689-798, Korea
Email: myjung@unist.ac.kr
Abstract - A careful vehicle operation planning for inbound logistics is required to resolve inventory and
quality problems arising when LCD manufacturing adopts a just-in-time raw material delivery philosophy.
Dynamic environments that disturb the system’s flexibility and robustness should be considered, such as
regulation of stocks, distance of vendors, time limits of delivery, and changes of production plan. In this
research, a multi-pass simulation approach (MPS) is proposed to support a dynamic and real-time nature of
vehicle planning necessary to synchronize with production schedules. The MPS is a well-known method for
solving real-time planning and its success depends largely on selecting the best decision-making rule quickly
and effectively. In this paper, the framework and operational procedure of the proposed approach are
investigated to satisfy various requirements existing in manufacturing LCD products. The proposed system
consists of a simulation driven planning system and a real-time vehicle scheduling system, where the former
develops a projected plan using the existing production plan and various static data. During this process, the
system uses a job packing process to generate a truckload job plan and sends it to the real-time vehicle
scheduling system. Then, the real-time vehicle scheduling system generates the vehicle routing schedule
through the dispatching process by considering the projected plan with real-time environmental factors. The
details of simulation and the effectiveness of the proposed method will be demonstrated through a case study.
Keywords: Supply Chain Control, Real-time Scheduling, Multi-pass Approach
1. INTRODUCTION
A supply chain is an integrated business process i
n which various businesses work together to (1) acquir
e raw materials, (2) convert these raw materials into s
pecified final products, and (3) deliver these final prod
________________________________________
†: Corresponding Author
ucts to retailers [Beamon, 1998]. In recent trends, the
rapid development of new products is the foundation o
f time-based competition strategies that have been adop
ted by many manufacturing enterprises in the dynamic
production environment. Accordingly, supply chain ma
nagement (SCM) is one of the most important challen
ges in improving enterprises' profitability, so many diff
erent methods for optimizing supply chains have been
proposed. However, to achieve high quality products at
low cost, while maintaining lower inventory and high
levels of performance in these global supply chains, s
upply chain scheduling and planning (SCSP) very com
plex and must occur within the enterprise and across t
he entire supply chain [Moon et al., 2008]. From the
logistical point of view, SCM can be classified to inbo
und logistics, intercompany logistics, and outbound logi
stics.
The SCSP also can be divided in the same way,
and outbound flow was well-investigated. However, inb
ound logistics is more important because it affects the
entire supply networks. In real situation, inbound sup
ply chain is more complicate to execute because of a
number of constraints, especially in logistics and at ma
nufacturing site. Many factors can vary, including dista
nce between vendors, delivery-time constraints, regulati
on of stocks and changes of production plans. As a re
sult, many researches about inbound supply chain are f
ocused on the risk analysis or risk models. The manu
facturers could reduce the effect of expected environm
ental uncertainty; however, they cannot mitigate the eff
ect of unexpected risk in practice. Therefore, enterprise
s must employ more flexible, coordinated and integrate
d planning and scheduling systems to efficiently provid
e a best solution to the context.
Multi-pass scheduling (MPS) is a kind of rule-bas
ed simulation approach; MPS ranks strategies and selec
ts the best one by forecasting using simulation of mult
iple courses of action before actual execution [Cho &
Wysk, 1993; Wu & Wysk, 1988]. MPS for a real time
supply chain must provide speedy evaluation of rule
combinations, and this speed depends on the number o
f simulation trials required to evaluate the rule combin
ations before ranking them.
In this paper, MPS is adopted and applied to vehi
cle routing planning system for inbound logistics to co
ntrol networks' robustness, monitor the system's perfor
mance, and sense emergencies occurrence. The framew
ork consists of (1) a simulation-driven planning system
and (2) a real-time vehicle scheduling system. The si
mulation-driven planning system schedules estimated ve
hicles routing plans of many vehicles using informatio
n such as distance of vendor, condition of vehicle, and
production plan. The real-time vehicle scheduling syst
em executes the plan produced by the simulation drive
n planning system, adjusts the plan by considering ext
ernal factors, and monitors the system’s condition in re
al-time.
The remainder of the paper is organized as follow
s: Section II provides an overview of inbound supply
chain and the concept of simulation-based MPS. Sectio
n III explains the constraints in application of SCSP, t
he details of proposed system architecture, and Section
IV provides the operating procedure of this system.
Section V describes a case study using this system. Se
ction VI presents a conclusion and outlines future wor
k.
2. RELATED WORKs
2.1 Inbound Logistics
The area of risk analysis of was a well-researched
topic. However, much of the research on risk and the
physical flow of goods in a supply chain have focuse
d more in the outbound flow rather than inbound supp
ly risk analysis [Zsidisin et al., 2000]. However, inbou
nd logistics is an important issue, especially, in the ma
nufacture site. And inbound supply risk is a well-know
n research issue. Zsidisin and Ellram (2003) state that
a supplier's failure to deliver inbound goods or service
s can have a detrimental effect throughout the purchasi
ng firm and subsequently through the supply chain, ult
imately reaching the customers. Generally, inbound risk
analysis is accomplished by 4 procedures; 1) risk clas
sification, 2) risk identification, 3) risk calculation, 4) i
mplementation/validation.
Table 1: Risk models for inbound flow [Wu et al., 20
06]
Risk
Risk
Risk
Classification
Identification
Calculation
Kraljic
Chain
Product-
(2001)
dependent
focused
Steele &
Chain
Product-
Court(1996)
dependent
focused
Zsidisin &
Chain
Product-
Highest
Ellram(1999)
dependent
focused
factorial risk
Finnman
Chain
Supplier-
(2002)
dependent
focused
Zsidisin
Chain
Product-
(2003)
dependent
focused
Validation
No
No
Weight sum
No
Yes
AHP
Yes
No
Yes
Table 1 briefly shows the previous researches on t
he risk factors of inbound supply chain. Kraljic (2001)
discusses the need to recognize the extent of supply
weakness and treats the weaknesses with a 4-phase str
ategy to manage supply. The strategy presents key risk
factors such as availability, number of suppliers, com
petitive demand, and make-buy opportunity. Steele and
Court (1996) address vulnerability management which
deal with an overall supply risk analysis procedure in
cluding risk identification and calculation. Finnman (20
02) proposes a supply risk management framework and
methodology to evaluate risks to help supplier risk m
anagement framework and a methodology to evaluate r
isks to help supplier selection process. Zsidisin and Ell
ram (1999) propose a 10-step approach for risk assess
ment by giving equal importance for 8 identified risk
factors and using a 5-point nominal risk scale.
2.2 Simulation-based Multi-pass Scheduling
Multi-pass approach is one methodology for rule
selection which is to combine a rule-based expert system to
select several scheduling rules in a real-time environment
through the test which scheduling rule is the best. Wu and
Wysk (1988) developed a simulator control mechanism that
evaluates the candidate dispatching rules and uses various
criteria to select the best control strategy for a given time
period. Wu and Wysk (1989)] presented an MPS algorithm
that includes a mechanism controller and a flexible
simulator. MPS algorithms are defined as scheduling
algorithms that solve the scheduling problem of selecting
the best dispatching rule among a set of rules.
Combinations of dispatching rules over a system's
production cycle can produce higher throughput than a
single rule alone [Drake et al., 1955; Wu & Wysk, 1988].
Cho and Wysk (1993) used neural networks to recommend
candidate rules for MPS, and Jones et al. (1995) used
inductive learning and genetic algorithms to build the
relationship between shop conditions and rules; some
modified techniques using neural networks [Kim & Kim,
1994] and production rules have also been used for
identifying this relationship.
MPS frameworks consist of five components: (1)
recommendation of rules for each problem type, (2)
generation of all rule combinations, (3) simulation, (4)
evaluation and ranking of rule combinations and (5)
scheduling [Cho & Wysk, 1993]. Whenever the decisionmaking rules need to be changed, a set of promising rules
for resolving each problem type is recommended. A few
different types of scheduling problem may occur during the
next scheduling period, so all rule combinations must be
generated, and each must be evaluated using simulation.
The best rule combination is chosen and conveyed to the
scheduling component. One method of MPS uses a nested
partitioning (NP) method and an optimal computing budget
allocation (OCBA) method to reduce the computational
load without the loss of throughput [Yoo et al., 2004].
3. FRAMEWORK OF PROPOSED SYSTEM
3.1 Constraints of inbound supply chain planning
In this section, constraints of inbound supply chai
n planning are considered. Several constraints interrupt
application of inbound supply chain planning in the p
ractice:
Regulation of stock; through the adaptation of jus
t-in-time (JIT) philosophy (or lean-manufacturing syste
m), most manufacturers pursue a low-stock level to co
pe with the risk of dynamic environmental uncertaintie
s without the stock-out. As a result, they could mini
mize the inventory cost and adapt more flexibly to the
change of environment, however it would hire more
vehicles to meet the requirements of JIT.
Time limits of delivery; to comply with the produ
ction plan under the JIT policy, vehicles should be mo
re frequently traveled their routes with less-than-full lo
ads within a limited time. This point reduces the effici
ency of vehicle's operation. Furthermore, traffic congest
ion or natural disasters may affect travel time. Therefo
re, this factor should be considered during the inbound
supply chain planning and execution steps.
Changes of production plan; production plans cou
ld be changed due to order changes, part shortage, or
production line breakdown. In this case, all plans and
schedules must be changed immediately.
Distance of vendors; the inbound supply chain co
nsists of many participants such as manufacturers, part
s assemblers, and suppliers. These participants may be
widely separated due to the globalized suppliers. Thes
e factors could be impact on other constraints such as
delivery time and stock level.
Disproportionate order quantity; because of produ
ct diversity, some parts may be supplied by more than
one supplier. Disproportionate orders may occur biase
d vehicles operation and then increase the number of
vehicles required to transport it.
Break time; in the factory operation, workers are
given breaks. During the break, all vehicles and produ
ction facilities must be stop; these also disturb operatio
n of the plan and the schedule.
3.2 Simulation Driven Planning System
The simulation-driven planning system develops a
projected plan using the production plan and set-up dat
a which include vendor information, vehicle informatio
n, travel time and part cart information. The details ar
e as follows:
Travel time means the time required for a vehicle to
move among suppliers. At first, these data are collecte
d through the survey and then should be continuously
improved by monitoring.
Vendor information includes basic information about v
endors such as the vendor name, the distance from ma
nufacturer, and waiting time for loading and unloading.
Vehicle information describes vehicle identification, ca
pacity, and size.
Part cart information is composed of cart size, capacit
y, and data attached to vendor.
Figure 1: Simulation-driven planning system
With the aforementioned data, the simulation drive
n planning system generates a modified production pla
n to assign work to each vehicle (Figure 1). During th
is process, the system uses simulation to generate a nu
mber of solutions, then sends a best solution with give
n constraints to the real-time vehicle scheduling system
. Figure 2 shows the detail process of simulation-drive
n planning system. During the packing process, the sys
tem generate diverse projected plan by rearrangement o
f production plan based on the vehicle's departure time
through the production start time and travel time, cal
culation of the amount of carts using the production pl
an and part-cart information.
3.3 Real-time Vehicle Scheduling System
The real-time vehicle scheduling system (Figure 3)
generates the vehicle routing schedule by combining t
he projected plan provided by the simulation-driven pla
nning system with information which provided by the
monitoring modules; this system include:
Traffic information, which includes traffic reports
and estimated travel time of each travel course. This
module has a strong effect on travel time.
Level of stocks, which directly affects production
flow, so it is very important. Shortage of stock means
loss of business opportunity. Vehicles, in the overage,
cannot perform the next schedule before unloading pr
ocess so the efficiency of vehicles is decreased.
Real-time vehicle's condition, which provides the
vehicle's location, whether it is loading or unloading, a
nd the items being loaded. It is needed to assign work
to the appropriate vehicle at the re-planning.
Monitoring Module, which tracks various data to
estimate the throughput of the generated vehicle routin
g schedule. It senses the changes of production plan a
nd other unexpected occurrences to adjust the generate
d schedule and determine whether re-planning process
is necessary. It is supported by other technology like
RFID, which can measure travel time and waiting time
automatically.
Figure 3: Real-time vehicle scheduling system
4. Operational Procedure
In this section, operational procedure of proposed
system will be explained. The procedure is as follows:
Figure 2: Flow diagram of packing process
Step 1: Information gathering and sharing
 A major manufacturer generates a production plan
using a customer's order. The firm shares the pla
n with its suppliers, so they could prepare their p

arts at the appropriate time.
The manufacturer defines and collects the set-up d
ata needed to simulate the simulation driven plann
ing system.
Step 2: Set the manufacturer's goal
 The manufacturer determines the major objectives
concerning the levels of stocks, the number of ve
hicles, and the minimum idle time of vehicle.
Step 3: Generate projected production plan
 Simulate the simulation driven planning system by
using production plan and set-up data.
 Calculate the departure time of resources from ve
ndor to manufacturing site considering time allowa
nce of just-in-time, waiting time, loading and unlo
ading time, and break time.
 Pack the resources to their appropriate carts accor
ding to cart capacity.
 Combine the appropriate number of carts in the s
ame time slot to make one truckload, that is a jo
b.
 Generate a projected plan by considering the jobs,
departure time, and production plan in the order
of time slot.
Step 4: Create vehicle routing plan and execution
 Use the real-time vehicle scheduling system to loa
d the projected plan.
 Set the vehicle routing schedule using the projecte
d plan, traffic information, vehicle's condition, and
level of stocks. Execute the schedule.
 If a vehicle is idle, then vehicle will be assigned
the job.
 Else if no idle vehicle is available, select an appr
opriate vehicle according to system operating polic
y.
 If too few vehicles are available to execute the sc
hedule, go to step 3 and take another projected pl
an for scheduling (re-planning). If there are no ap
propriated projected plan, go to step 2 and add m
ore vehicles or change the quantity of stock.
Step 5: Evaluate the performance index
 The monitoring module continuously collects vario
us data concerned with planning and scheduling, u
pdates that information, and evaluates the perform
ance index of the vehicle routing schedule.
 If the index value does not satisfy the set up ind
ex, send a promised message to the simulation dri
ven planning system, and go to step 3.
 If an unexpected event such as change of product
ion plan or breakdown of production line occurs,
send a promised message to the simulation driven
planning system, and go to step 3.
Using this procedure, the proposed system can ge
nerate a vehicle routing plan that is applied in practice
by constantly adapting it to changes in the environme
nt. If a new vendor or a new vehicle is added, the sy
stem operator must insert the set-up information related
to that component. Using Step 3, various solutions ca
n be generated, allowing for existing constraints. Howe
ver, the goal presented in Step 2 helps in selecting the
best solution. In Step 4, the vehicle assignment proce
ss can vary with the operation policy and the firm's g
oal such as minimization of levels of stocks, of transp
ortation cost, or of idle time of vehicles. Step 5 allow
s the proposed system to evolve continuously to fit th
e dynamics of environment.
5. The Case Study
To validate efficiency and utility of the developed
system, two case studies are performed; the first is to
determine the important factors related with the comp
any's policy, the second is to verify the performance o
f the developed system. In these case studies, the com
pany has 3 manufacture sites and 9 vendors, and vend
ors provide two major sub-products. We collected vario
us set-up data related with vendors, vehicles, cart data,
part data, travel time, and correlation of these data in
the field, and we generated 1,000 days production pla
ns based on 30 days real data given by the company.
The company has 16 production lines and produces ab
out 14,000 products/day with 200 kinds of products. T
he system was coded in C++ language and was run o
n Core(TM) 2 Quad computer with 2.33GHz processor
and 4.00GB of RAM. The simulation for generation
of 1day's schedule takes about 5 minutes, the first cas
e was repeated 10 times and the second was 2 times.
5.1 Simulation Case #1
To determine the appropriate values for (1) the time
allowance of JIT (30min.~120min.), (2) the stock for next
day (30min.~120min.), so 28 simulations was executed
(Table 2) by using 5days production plans.
Table 2: Simulation case #1
Stock
for
next
30 min.
60 min.
90 min.
120 min.
30 min.
1
2
3
4
45 min.
5
6
7
8
60 min.
9
10
11
12
75 min.
13
14
15
16
90 min.
17
18
19
20
JIT allowance
day
105 min.
21
22
23
24
120 min.
25
26
27
28
5.2 Simulation Case #2
Based on the result of simulation case #1 (1hour time
Figure 4 illustrates simulation result for 28 cases (Table
1) based on total number of vehicle, average stock level,
and average idle time of vehicles. As shown Figure 4-(a)
and (b), 2 hour ahead delivery performs better than others.
To minimize the idle time (Fig. 4-(c)), 30 min. ahead
delivery and 2 hour allowance for JIT is the best condition.
However, details of 30 min. and 1hour ahead delivery
schedule show that these plans can not apply because of the
too early departure of vehicle. And these conditions are not
appropriate for applying in practice due to the number of
vehicle and the level of max stock. In this case study, the
object is minimization of stock level (stock level limitation:
180) and minimization of vehicle number (limitation:
17/day). Therefore, simulation case 12 (1 hour allowance
for JIT and 2 hour ahead delivery for next day production)
was selected.
Stock for
next day
Number of vehicles
allowance of JIT, 2hour ahead delivery for next day), we
repeat the simulation with 500 days production plan
considering lunch time, dinner time, and 3 break times.
Figure 5 illustrates result of simulation case #2. Average
requirement of vehicles is 19.89 vehicles a day (Max: 25,
Min: 15), and average idle time is 4:26 per day (Max: 9:07,
Min: 0:57). And average stock level is 135.1 lots (Max:
179, Min: 96).
Figure 5: Result of simulation case #2
(a)
Stock for
next day
In case study, implemented simulation-driven planning
system provides various simulation result considering
diverse constraints and real-time vehicle routing scheduling
system determines the required number of vehicles, stock
level, and idle time of vehicles. And proposed system has
flexibility and robustness because it collects and updates
set-up data in the monitoring module. The system
continuously generates suitable plans and schedules based
on simulations that consider all constraints and conditions
(b)
Stock for
next day
6. CONCLUSION
(c)
Figure 4: Result of simulation case #1
In this paper, the multi-pass concept is adopted an
d applied to a planning system for inbound supply cha
in execution. The proposed system consists of a simul
ation-driven planning system and a real-time vehicle sc
heduling system. This system considers many constrain
ts that occur in the real world that impede utilization
of the schedule in practice. The simulation-driven plan
ning system generates an appropriate projected plan usi
ng production plan, travel time, vendor information, ve
hicle information, and part-cart information. This syste
m can generate diverse projected plans by simulation,
and can select a best solution by considering the firm'
s goals. The real-time vehicle scheduling system gener
ates a vehicle routing schedule and assigns jobs to veh
icles. Furthermore, the monitoring module gathers diver
se information related to system operation, and updates
existing information continuously. Therefore, generated
schedule is becoming more and more accurate. Finall
y, the monitoring module evaluates the performance in
dex using collected data, and determines the re-plannin
g process by comparing the performance index and a
standard index. These procedures can guarantee system'
s flexibility and robustness.
For further study, a mathematical goal model for
decision-making should be developed. Diverse rules to
relax an impact of sudden event have to be investigate
d and considered.
ACKNOWLEDGMENT
This research was supported by Basic Science Res
earch Program through the National Research Foundati
on of Korea (NRF) funded by the Ministry of Educati
on, Science and Technology (2009-0077660).
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AUTHOR BIOGRAPHIES
Kiyoul Lee is a Ph.D. candidate at the Department of
Industrial & Management Engineering, Pohang University
of Science & Technology (POSTECH), Korea. He received
his BS degree in Industrial & Management Engineering
from POSTECH in 2005. His research interests include
supply chain planning and scheduling, simulation based
multi-pass scheduling, and supply chain of energy industry.
<point@postech.ac.kr>
Hyunbo Cho is a professor of the Department of Industrial
and Management Engineering at the Pohang University of
Science and Technology (POSTECH), Korea. He received
his B.S. and M.S. degrees in Industrial Engineering from
Seoul National University in 1986 and 1988, respectively,
and his Ph.D. in Industrial Engineering with a
specialization in Manufacturing Systems Engineering from
Texas A&M University in 1993. He was a recipient of the
SME’s 1997 outstanding young manufacturing engineer
award. His areas of expertise include business models
creation, manufacturing management & strategy, and
supply chain Integration. He is an active member of IIE and
SME. < hcho@postech.ac.kr>
Mooyoung Jung is a professor of the School of
Technology Management at UNIST (Ulsan National
Institute of Science and Technology), Korea. He received
his Ph. D. from Department of Industrial & Manufacturing
Engineering at Kansas State University in 1984. He has
published more than 200 research papers in the fields of
Technology Management and Manufacturing Systems. His
current research interests include Technology Strategy and
Management for industry. <myjung@unist.ac.kr>