A green vehicle routing model based on modified particle swarm optimization for cold chain logistics

Publication Date08 Apr 2019
Pages473-494
DOIhttps://doi.org/10.1108/IMDS-07-2018-0314
AuthorYan Li,Ming K. Lim,Ming-Lang Tseng
subjectMatterInformation & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems
A green vehicle routing model
basedonmodifiedparticleswarm
optimization for cold chain logistics
Yan Li
Centre for Industrial Innovation for Competitiveness,
Chongqing University, Chongqing, China
Ming K. Lim
Centre for Industrial Innovation for Competitiveness,
Chongqing University, Chongqing, China and
Centre for Business in Society, Coventry University, Coventry, UK, and
Ming-Lang Tseng
Institute of Innovation and Circular Economy, Asia University, Taichung, Taiwan and
Lunghwwa University of Science and Technology, Taoyuan, Taiwan
Abstract
Purpose This paper studies green vehicle routing problems of cold chain logistics with the consideration of
the full set of greenhouse gas (GHG) emissions and an optimization model of green vehicle routing for cold
chain logistics (with an acronym of GVRPCCL) is developed. The purpose of this paper is to minimize the total
costs, which include vehicle operating cost, quality loss cost, product freshness cost,penalty cost, energy cost
and GHG emissions cost. In addition, this research also investigates the effect of changing the vehicle
maximum load in relation to cost and GHG emissions.
Design/methodology/approach This study develops a mathematical optimization model, considering
the total cost and GHG emission. The standard particle swarm optimization and modified particle swarm
optimization (MPSO), based on an intelligent optimization algorithm, are applied in this study to solve the
routing problem of a real case.
Findings The results of this study show the extend of the proposed MPSO performing better in achieving
green-focussed vehicle routing and that considering the full set of GHG costs in the objective functions will
reduce the total costs and environmental-diminishing emissions of GHG through the comparative analysis.
The research outputs also evaluated the effect of different enterprisesconditions (e.g. customerslocations
and demand patterns) for better distribution routes planning.
Research limitations/implications There are some limitations in the proposed model. This study
assumes that the vehicle is at a constant speed and it does not consider uncertainties, such as weather
conditions and road conditions.
Originality/value Prior studies, particularly in green cold chain logistics vehicle routing problem, are
fairly limited. The prior works revolved around GHG emissions problem have not considered methane
and nitrous oxides. This study takes into account the characteristics of cold chain logistics and the full set
of GHGs.
Keywords Particle swarm optimization, Cold chain logistics, Green vehicle routing
Paper type Research paper
Nomenclature
Xnumber of customers.
Ynumber of refrigerated trucks.
FC vehiclesconsumption of fossil fuels.
Eamount of greenhouse gas emissions.
E
CO2
amount of carbon dioxide emissions.
E
CH4
amount of methane emissions.
E
N2O
amount of nitrous oxides emissions.
Input variables
F
1
cost of vehicle operating.
F
2
unit price of cold chain product.
F
3
unit cost of refrigeration during
transportation.
F
4
unit cost of refrigeration during
unloading.
Industrial Management & Data
Systems
Vol. 119 No. 3, 2019
pp. 473-494
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-07-2018-0314
Received 23 July 2018
Revised 17 August 2018
Accepted 29 August 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
473
Green vehicle
routing model
F
5
unit cost of waiting when the
vehicle arrives early.
F
6
unit cost of punishment when the
vehicle arrives lately.
F
7
unit price of fuel.
F
8
unit price of greenhouse gas
emissions.
q
i
number of products that customer i
needs.
q
j
number of products that customer j
needs.
ty
0time when the vehicle ydeparts
from the distribution center.
xh speed of discharge.
d
ij
distance between customer iand
customer j.
θsensitivity factor of cold chain
products.
K
1
constant value of cold chain
product during transportation.
K
2
constant value of cold chain
product during unloading.
α
1
refrigeration equipment
consumption of fuel in unit time
during transportation.
α
2
refrigeration equipment
consumption of fuel in unit time
during unloading.
Q
*
maximum load of the vehicle.
U
0
fuel consumption when vehicle
empty.
U
1
fuel consumption when vehicle
full load.
EF
CH4
methane emission factor.
EF
N2O
nitrous oxides emission factor.
GWP
CH4
global warming trend of methane.
GWP
N2O
global warming trend of nitrous
oxides.
Decision variables
Q
i
quantity of products left on the
truck when the vehicle yleft the
customer i.
ty
itime when the vehicle yarrives at
customer i.
ty
jtime when the vehicle yarrives at
customer j.
tdy
ij time of refrigerated truck yfrom
customer ito customer j.
a
y
0,1 variable, when refrigerated
vehicle ycar is used, a
y
¼1,
otherwise a
y
¼0.
by
i0,1 variable, when refrigerated
truck yis delivered to customer i,
by
i¼1, otherwise by
i¼0.
cy
ij 0,1 variable, when refrigerated
truck ytransports goods to
customer jthrough customer i,
cy
ij ¼1, otherwise cy
ij ¼0.
1. Introduction
Global warming has become more and more severe in recent years, and meteorological
scientists claimedthat the increase of greenhouse gas (GHG)concentration in the atmosphere
is the main cause of global warming (Montoya et al., 2016). ChinasTwelfth Five-Year Pl an
traffic planningreport pointed out that by 2020, unit energy consumption of vehicles need to
be reduced by 10 per cent, of which the freight unit energy consumption be reduced by
12 per cent to achievesustainable economic development (Zhang andChen, 2014). Hence, this
is vital to ensure that GHG emissions are minimized to achieve a win-win situation for the
economy as well as the environment, especially in the high-energy-consuming cold chain
logistics industry which is the research domain.
Cold chain logistics is a supply chain system that requires the maintenance of a low
temperature environment that consumes more fuels to maintain the low temperature in
comparison to ordinary logistics, and the GHG emissions that are linearly related to fuel
consumption from cargo transportation accounted for 5.5 per cent of global GHG emissions
(Hou et al., 2015; Piecyk and Mckinnon, 2010). The refrigerated trucks have 30 per cent more
tailpipe emissions compared to ordinary trucks in cargo transportation (Wang and
Liu, 2011). The purpose of this study is to reduce energy consumption and environmental
pollution. Prior studies are demonst rated that route optimization reduces energy
474
IMDS
119,3

To continue reading

Request your trial

VLEX uses login cookies to provide you with a better browsing experience. If you click on 'Accept' or continue browsing this site we consider that you accept our cookie policy. ACCEPT