Int. J. xxxxxxxxx xxxxxxxxxxxxxms, Vol. X, No. Y, xxxx
1
Title
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Abstract:
Keywords:
1
Introduction
2
Literature Review
a. E-commerce
In the past few years, e-commerce product delivery has grown
dramatically. The majority of deliveries made in e-commerce (also known
as "home shopping") involve packages, small items, and food [Manerba et
al., 2018]. The world is experiencing a digital growth spurt, impacting
almost every aspect of nature and human life. E-commerce and logistics
integration will be a successful endeavour. Customer service, tracking,
delivery, time effectiveness, and overall cost will all improve due to this
incorporation [Keerthy et al., 2022].
Through the creation of new industries, fields of production, and services
that were unthinkable even 30 years ago, the explosion of information
technology services is profoundly reshaping the economy and the business
world. E-commerce, in particular, has revolutionised consumer needs and
behaviour, directly impacting how businesses conduct themselves
[Archetti & Bertazzi, 2021].
The onset of the pandemic has sped up the process of consumers switching
their purchasing behaviour from conventional brick-and-mortar retail
stores to online e-commerce websites [Gupta et al., 2022]. The number of
deliveries of goods to customers' homes increased as the e-commerce
industry grew [Ranna et al., 2022]. Because they are closely related to
customer satisfaction, quantity and quality are essential for the
development of e-commerce in order to deliver products on time.
Copyright © 202x Inderscience Enterprises Ltd.
Author
Therefore, in e-commerce, product delivery requires speed [EscuderoSantana et al., 2022]. Due to the customer's absence from the delivery
location, there are an increasing number of first-time delivery failures as ecommerce continues to grow [Tilk et al., 2021].
Since ordered items must be delivered to specific customers, the
popularity of e-commerce in today's world poses a significant challenge
for last-mile delivery [Yuan et al., 2021]
b. Last-Mile Delivery
In order to reduce the cost of on-time delivery while maintaining quality,
this research will concentrate on last-mile delivery in the e-commerce
sector.
The express or last-mile delivery market has grown due to the quickly
developing e-commerce sector. Several activities and processes make up
last-mile delivery from the last transit point to the delivery destination
point, specifically at the B2C/end-user level[Ayu & Nahry, 2021].
The home delivery method is typically used for last-mile distribution in ecommerce businesses, where packages are delivered to customers' or end
users' homes. In the e-commerce industry, last-mile delivery performance
is crucial in the relationship between sellers and customers. This directly
influences the customer's decision to reorder. Delivering goods to
customers faster while lowering delivery costs is today's challenge for
logistics companies offering last-mile delivery services. Contrarily, lastmile delivery is regarded as the most pricy, ineffective, and
environmentally damaging link in the logistics chain. Last-mile deliveries
account for 53% of total shipping costs [Ranna et al., 2022]. As a result, ecommerce is growing, and logistics firms must deal with an increase in
parcel delivery, particularly in urban areas. Even though impressive ecommerce growth statistics have been provided, it has been noted that
distribution costs, particularly for the last mile, could reach 40% of the
price of a product [Mancini & Gansterer, 2021].
The customer must be present during the delivery process in attended
home delivery, and delivery window requirements typically define the
service. Deliveries may be made at a few collection locations (post offices,
reception/delivery boxes, banks) or, more commonly, at the customer's
home [Manerba et al., 2018]. According to research done, there can be two
types of deliveries identified [Manerba et al., 2018] are; Fast delivery (FD)
allows customers to place online orders up to a set deadline, after which
they will receive their products in a specified window of time (usually a
few hours). Such deadlines are set by retailers and are evenly spaced
Title
throughout the day (for example, six deadlines from 8 a.m. to 8 p.m., one
every two hours). As a result, the vehicles of the retailers must leave the
depot and return within a set number of time windows. This distribution
strategy's speed is its vital strength. The retailer might be forced to use
additional vehicles to serve customers within the allotted time.
Moreover, another type is Lazy delivery (LD), The day before the
delivery; customers must place their orders in advance. The customer can
choose one of the time windows offered and will receive the delivery
within that window the following day (Manerba et al., 2018). There are
numerous last-mile delivery services available at the moment. Deliveries
to homes are the most typical. Customers wait for their packages at home.
Additionally, the delivery can be made to pick-up locations like stores or
designated lockers [Yuan et al., 2021]
The significant increase in requests for home deliveries has started to have
a considerable impact on last-mile delivery. Because there are so many
requests, businesses are forced to extend the delivery windows by several
hours to guarantee that the delivery will be made during the customers'
preferred time windows, typically at the beginning or end of the day. This
could lead to a drop in the perceived quality of the service and, as a result,
a decrease in customer satisfaction and loyalty. Additionally, efficiency
losses are being experienced by logistics providers as a result of multiple
visits to the exact locations caused by customers' absence. This harms
ecological goals and transportation costs due to increased traffic in urban
areas [Mancini & Gansterer, 2021].
Nevertheless, the ineffective distribution system for last-mile delivery is
the issue with increasing the volume of parcels delivered to customers'
homes. Due to this, more delivery vehicles are operating in cities, which
can add to the negative externalities of traffic, noise, and pollution, all of
which are harmful to human health. Failures during delivery to the
customer's home or end user are another issue that frequently arises from
last-mile delivery activities. Re-delivering the item to the customer's home
will result in higher costs [Ranna et al., 2022].
The success of the online retail industry is heavily dependent on home
delivery. However, last-mile delivery is currently regarded as one of the
supply chain's most pricy, ineffective, and environmentally damaging
parts [Manerba et al., 2018]. The rapid delivery of online orders creates
new problems that the traditional routing problems of the previous 30
years did not have to deal with [Archetti & Bertazzi, 2021].
Author
c. Vehicle Routing Problem with Time windows
The logistics system problem of vehicle routing with time windows
(VRPTW) has recently received much attention. The issue can be
summarised as selecting routes for a small fleet of vehicles to serve a
particular customer base within specific time intervals. Each vehicle has a
specific volume. It begins at the depot and ends there as well. Each client
should only receive one service. The VRPTW wants to keep the overall
cost of transportation as low as possible. Researchers have focused their
attention on the VRPTW, an essential variation of the VRP. The VRPTW
aims to satisfy the time frame limitations and identify the best possible
routes for a group of similar, restricted-capacity vehicles [Le et al., 2022].
The VRP with Time Windows (VRPTW), one of the wide varieties of
VRP, is most often studied. Each request in VRPTW has a time window
outlining the desired time frame for the service. Then, VRPTW seeks to
minimise the violation of the requests' time restrictions when designing
routes. Numerous efficient algorithms have been proposed to resolve
VRPTW, and substantial investigations have been conducted[JacobsenGrocott et al., n.d.].
Recent years have seen much research into three different kinds of
problem that considers dynamic customer wants: VRPTW with stochastic
customer demands, VRPTW with SCDs and DCDs, and VRPTW with
dynamic and stochastic customer demands. The subject of most studies is
the VRPTW with DCDs in a single depot logistics network. (38)
Numerous academics have researched the VRP extensively, notably the
vehicle routing problem with time windows (VRPTW). When tackling
difficulties, researchers frequently apply precise algorithms and
metaheuristics. In recent years, some academics have also suggested
specific techniques, like the branch-price and branch-and-cut algorithms,
to solve VRPTW successfully. Exact algorithms provide a distinct benefit
when solving issues of small size (fewer than 30 nodes), but when the
scale increases, using these algorithms results in an unnecessary
computational effort to solve the problem correctly and has a limited range
of applications. Compared to the exact algorithm, the metaheuristic
algorithm is better suited to solving complex issues since it conducts a
more thorough search, which can increase the solution's efficacy and be
used more broadly. The current approaches to overcoming these issues are
genetic algorithms, ant colony algorithms, simulated annealing algorithms
(SAA), and particle swarm algorithms [Xiang et al., n.d.].
Within the time between the earliest and latest timings, a vehicle serves
each customer exactly once. Each vehicle journey begins and ends at the
designated depot. Priority consumers' locations, demands, and time
Title
windows are known [Le et al., 2022]. An altered version of the Ant
Colony Optimization utilising K-Means Clustering has been used in this
research to solve the Vehicle Routing Problem with Time Window
Constraint [Keerthy et al., 2022].
This research study [Manerba et al., 2018] examined how the length of
time windows affects the distances the vehicles used in last-mile product
delivery covers. The reduction of travel lengths (measured in kilometres)
is a customary demand by retailers seeking to increase profits by cutting
costs. To evaluate this, formulate these two policies regarding variations
of the Vehicle Routing Problem (VRP). To be more exact, quick delivery
uses a VRP with distance/time constraints (DCVRP), whereas lazy
delivery uses a VRP with time windows (VRPTW). The proposed
formulations for the two problems can be solved using a commercial
Mixed Integer Linear Programming (MILP) solver since they are compact
(i.e., contain a polynomial number of constraints). Then, test and analyse
the issues using actual cases supplied by a shop that provided historical
data for two days structured as in rapid delivery. The same real-case facts
have been combined to create instances of the lazy delivery policy
[Manerba et al., 2018].
In [Aydınalp & Özgen, 2022]mathematical model was constructed by
adding time restrictions, capacity limitations, and maximum distance
travelled to the models to create a mixed-integer programming (MIP)
model based on the vehicle routing problem with time windows
(VRPTW). As the model uses the GUROBI® solver to find the best
solution for small issue sizes, metaheuristic techniques that imitate
annealing and adaptive large neighbourhood search algorithms are
proposed for solving significant problems. A real dataset was employed to
evaluate the efficiency of the metaheuristic algorithms. The simulated
annealing (SA) and adaptive extensive neighbourhood search (ALNS)
were assessed and contrasted with GUROBI® and one another through a
collection of actual problem examples.
HFVRPTW (Heterogeneous Fleet Vehicle Routing Problem with Time
Windows) refers to distribution system planning with many scenarios
solved by vehicle routing issues considering various vehicle modes and
time windows. VRP Spreadsheet Solver was used to address the VRP
problem in this study [Ranna et al., 2022]. In addition to the customer
Time Windows (VRPTW) variation, this research attempts to present a
novel agent-based optimisation model to capture the uniqueness of
vehicles in this routing problem. By centralising the solution search with a
metaheuristic algorithm, methodology gets beyond the localised greedy
solutions produced using decentralised and hybrid agent-based
Author
methodologies. Due to their rigidity in addressing combinatorial
optimisation problems like VRP, heuristics evolved into metaheuristics,
which is how centralised search has changed [Abu-Monshar & Al-Bazi,
2022].
In metropolitan locations, the Heterogeneous Fleet Vehicle Routing
Problem with Time Window (HFVRP-TW) model is used to optimise the
last-mile delivery route. The HFVRP-TW model is created and applied
using information from express delivery during route optimisation. The
information was obtained from an express delivery business, specifically
the Jakarta, Indonesia, same-day delivery service, which was withheld for
privacy reasons. The optimisation process was carried out through
simulation and the creation of several operational scenarios. The necessary
information comprises the location of the depot, the client's address,
latitude and longitude coordinates, the kind of vehicle, the number of
vehicles, the vehicle capacity, the service time, the earliest start time, and
the latest end time (time window). Each customer must be served on or
before the earliest time and before the latest time, which is known as the
time window's two sides [Ayu & Nahry, 2021].
This research [da Silva Júnior et al., 2021]conducts a theoretical
investigation, develops a novel method for a solution, and assesses the
performance of optimisation algorithms (meta-heuristics) for the dynamic
vehicle routing issue with time windows (DVRPTW). The two key
contributions of this study are the framework for solving the DVRPTW
and the proposed seven novel algorithms, as well as the successful
outcomes in reducing the number of unsatisfied consumers. To solve the
DVRPTW, had to offer seven algorithmic variations. Take into account
both continuous and periodic re-optimisation.
This research discusses the Electric VRP with Time Windows
(EVRPTW), a variation of the well-known VRP with Time Windows
(VRPTW). The EVRPTW manages a homogeneous fleet of EVs
dispatched from a single depot to serve a group of clients with defined
demands and time windows. The EVs depart from the depot to begin their
routes, serving each customer exactly once before making their way back
to the depot at the end of the day. They can be recharged at charging
points along the way because they have a constrained driving range. The
stations are few but need to be fully occupied. The goal is to travel as little
as possible overall [Duman et al., 2021].
In order to accurately represent the nature of systems like the home
visitation or home healthcare (HHC) systems, this study [Saksuriya &
Likasiri, 2022] uses a heuristic for solving vehicle routing problems with
time windows (VRPTW) with generic compatibility-matching between
Title
customer/patient and server/caretaker requirements. Any variant of
VRPTW is more complex than standard VRP. Hence the solution requires
a specific, tailored heuristic. The heuristic developed in this study is a
successful combination of the inexperienced Local Search (LS), Ruin and
Recreate method (R&R), and Particle Swarm Optimization (PSO). The
suggested LS serves as the initial solution finder and the search engine for
a practical/local optimal. The R&R component permits the solution to be
over-optimised, and LS moves the solution back to the feasible side, while
PSO aids in going from the current best solution to the next best solution.
They solved 56 benchmark situations with 25, 50, and 100 clients to
evaluate our heuristic and discovered that it could find 52, 21, and 18 ideal
cases, respectively. Changed the benchmark instances to incorporate
compatibility requirements to examine the effectiveness of our heuristic
more thoroughly. The findings demonstrate that the heuristic can find the
best answers in 5 out of 56 cases [Saksuriya & Likasiri, 2022].
d. Cluster-Based approach
VRPTW can be addressed using a cluster-based algorithm to cluster
constraints. There are few types of research done combined with vehicle
routing problems with time windows.
The objectives of this study are to establish the fleet size and reduce
overall logistics expenses. Labor, gasoline and refrigeration costs are
included in the logistical costs. The MIP model represents the 39-node
product distribution network. The proposed MIP model is resolved using
the clustering approach. According to the findings, 39 clients are served
by 11 trucks, and the vehicle utilisation efficiency is greater than 70%.
These ideal outcomes are discovered in an average of 0.36 seconds.
Introduce the cluster-first-route-second strategy to address large-scale
issues. This hierarchical strategy was applied on a big scale using a precise
algorithm in a fair amount of time. A clustering algorithm is used in the
first part of the procedure to create the clusters. Then, by modifying
cluster members, each cluster's capacity is managed. In the second phase,
the branch and bound algorithm is used to solve each subproblem for each
cluster. Customers are grouped using the k-means clustering algorithm and
the capacitated k-means clustering technique [Le et al., 2022].
In this study (Yuan et al., 2021), Instead of choosing just one delivery
location while making an online purchase, a client can suggest a set of
delivery destinations (home, pick-up spots, and car trunk) with the
corresponding time limits. All these delivery services can be integrated
and offered to customers. The courier must pick one of the addresses the
customer has provided to deliver a product to a specific consumer.
Author
Customers gain from additional freedom following their preferences. It
also lowers delivery costs and raises the percentage of successful first-time
deliveries. The Generalized Vehicle Routing Problem with Time Windows
can be used to mimic the underlying routing issue in the application
discussed above (GVRPTW). The vertex set of a directed graph with
clustered vertex sets is where the GVRPTW is defined. One cluster merely
has the depot, where a uniform fleet of vehicles with fixed capacities is
housed. The other clusters stand in for clients. The customer's potential
delivery sites are represented by a cluster's vertices, each of which has a
time frame during which the visit must occur if the vertex is visited. Each
client has a demand attached to them. The goal is to identify a set of routes
that minimises overall travel expenses, visits exactly one vertex per
cluster, and abides by all capacity and time restrictions [Yuan et al., 2021].
In this research study [Dutta et al., 2022] proposed problem is to consider
the closest cities or clients together in a cluster so that one truck can
service them. This is done by using a cluster primary-route secondary
strategy. As a result, it decreases the number of vehicles used and the
carbon emissions those vehicles emit. The clusters are located using a
modified distance-based k-means algorithm, and a collection of alternative
pathways connected to each cluster is located using SPEA2. In order to
choose the option that best fits the Decision Maker's preferences, apply the
VIKOR approach.
[Keerthy et al., 2022] propose transporting goods from the depot to the
customer on time or earlier. This paper will explore how Ant Colony
Optimization with K-Means Clustering (ACO-K-Means) has been used to
save costs. The mathematical model described in this study will address
the distribution, e-logistics, and retail network concerns.
e. Literature overview
This section summarises the literature on the VRPTW and includes a
comprehensive descriptive analysis of papers published between 2017 and
2022. A list of research gaps that need to be filled and a literature
summary round out the section. The VRPTW versions used in the prior
literature are listed in Table 3.1, along with a synopsis of the problemsolving methods, objective functions, and kinds of algorithms employed.
The selected articles were summarised after literature from the most recent
six-year period, from 2017 to 2022, was analysed.
Abbreviations for solution Methods:
Title
Meta Heuristic (MH), Heuristic (H), Mix Integer Programming (MIP),
Mix Integer Linear Programming (MILP)
Abbreviations for Characteristics:
Vehicle Routing Problem with Time Windows (VRTW), Generalized
Vehicle Routing Problem With Time Windows (GVRPTW), Travelling
Salesman Problem With Time Windows For The Last Mile Delivery In
Online Shopping (TSPTWLMDOS), Full Truckload Multi-Depot Vehicle
Routing Problem With Time Windows (SFTMDVRPTW), Dynamic
Vehicle Routing Problem with Time Windows (DVRPTW), Collaborative
Multi depot Vehicle Routing Problem With Dynamic Customer Demands
And Time Windows (CMVRPDCDTW), Open Vehicle Routing Problem
(OVRP), Constrained Clustering for the Capacitated Vehicle Routing
Problem (CC-CVRP), Electric Location Routing Problem With Time
Windows (E-LR
Table 2.1. Literature Review Summary Table
Author
Reference
[Wu & Wu, 2022]
[Aydınalp &
Özgen, 2022]
[Gupta et al.,
2022]
[Ranna et al.,
2022]
Solution
Method
characteristics
Fleet of
vehicles
Depot
Product
MH
VRPTW
Homogenous
S
M
MH
VRPTW
Homogenous
S
M
VRPTW
Heterogenous
S
M
VRPTW
Heterogenous
S
M
VRPTW
Heterogenous
M
M
VRPTW
Homogenous
S
M
GVRPTW
Homogenous
S
M
VRPTW
Homogenous
S
M
VRPTW
Heterogenous
S
M
VRPTW
Heterogenous
S
M
TSPTWLMDOS
Heterogenous
M
M
SFTMDVRPTW
Homogenous
M
M
DVRPTW
Homogenous
S
M
CMVRPDCDTW Heterogenous
M
M
OVRP
Homogenous
M
M
CC-CVRP
Homogenous
S
M
VRPTW
Homogenous
S
M
E-LRPTW
Homogenous
S
M
VRPTW
Homogenous
S
M
Deep-RL
model
VRP
Spreadsheet
Solver
[Dondo & Cerdá,
H
2007]
[Escudero-Santana
MH
et al., 2022]
[Yuan et al., 2021] H / cluster
MIP /
[Le et al., 2022]
cluster
[Abu-Monshar &
agent-based
Al-Bazi, 2022]
MH
VRP
[Ayu & Nahry,
Spreadsheet
2021]
Solver
[Jiang et al., 2020] H
[el Bouyahyiouy
MIP/
& Bellabdaoui,
Generic
n.d.]
algorithm
[da Silva Júnior et
H
al., 2021]
[Wang et al.,
IMOPSO2022]
DIS
Cluster[Dutta et al.,
based
2022]
algorithm
Cluster[Alesiani et al.,
based
2022]
algorithm
MIP/ K[Keerthy et al.,
Means
2022]
Clustering
MILP/ K[Sánchez et al.,
Means
2022]
Clustering
[Villalba & la
H / k-means
Rotta, 2022]
clustering
Title
[Manerba et al.,
2018]
MILP
VRPTW
Homogenous
S
M
Proposed Study
H/
Clustering
Algorithm
VRPTW (based
on Receiver)
Heterogenous S
M
Abbreviations for Deport and Product:
Single(S) , Multiple(M)
After analysing all literature, the Vehicle routing problem with time
windows-based papers used mostly meta-heuristic or heuristic-based
algorithms to solve VRPTW. According to past literature cluster-based
approach to VRPTW is limited and only used for unique scenarios, and
most of every paper looked at VRPTW from the senders’ point of view
and applied solution but not the point of view of the Receiver.
Table 2.2. Literature review summary on objectives
Reference
[Wu & Wu, 2022]
[Aydınalp & Özgen,
2022]
[Gupta et al., 2022]
[Ranna et al., 2022]
[Dondo & Cerdá, 2007]
[Escudero-Santana et al.,
2022]
[Yuan et al., 2021]
[Le et al., 2022]
[Abu-Monshar & AlBazi, 2022]
[Ayu & Nahry, 2021]
[Jiang et al., 2020]
[el Bouyahyiouy &
Bellabdaoui, n.d.]
[da Silva Júnior et al.,
CM
X
Objective Functions
CS OLM RNV RTD
X
X
X
X
X
X
MCE
Industry
Agri – Ecommerce
Pharmaceutical
Industry
E-commerce
E-commerce
Logistic
X
E-commerce
X
X
E-commerce
Logistic
X
X
Logistic
X
E-commerce
Parcel Delivery
X
X
X
X
Truck Delivery
E-commerce
Author
2021]
[Wang et al., 2022]
[Dutta et al., 2022]
X
X
X
X
[Alesiani et al., 2022]
[Keerthy et al., 2022]
X
X
[Sánchez et al., 2022]
[Villalba & la Rotta,
2022]
[Manerba et al., 2018]
Proposed Study
X
X
X
X
X
Logistic
E-commerce
Postal ecommerce
Transportation
Electric Vehicle
Transport
X
Logistic
X
X
E-commerce
E-commerce
As of Table 2.2, after analysing objective functions and Industry, most
papers done cost minimisation and minimisation of carbon emission but
not for the VRPTW from the viewpoint of the Receiver but from the
sender. Moreover, Last-mile delivery in the e-commerce sector has been
affected chiefly in the last few years due to covid-19 and the economic
crisis. However, it had both positive and negative impacts on last-mile
delivery.
Considering all those factors, we have selected VRPTW from the point of
view of receiver address using heuristic and cluster-based algorithms to
Heterogenous vehicle fleets, single deport and Multiple products aiming to
reduce travel distance and cost for last-mile delivery in the e-commerce
industry.
Abbreviations for Objective Functions:
Cost minimisation (CM), Customer Satisfaction (CS), Reduce Number of
Vehicles (RNV), Reduce Travel Distance (RTD), and Minimise Carbon
Emission (MCE)
Title
3
Methodology
4
Basic model
5