Last-mile distribution planning for fruit-and-vegetable cold chains
DOI | https://doi.org/10.1108/IJLM-01-2017-0002 |
Pages | 862-886 |
Published date | 19 June 2018 |
Date | 19 June 2018 |
Author | Yu-Hsiang Hsiao,Mu-Chen Chen,Kuan-Yu Lu,Cheng-Lin Chin |
Subject Matter | Logistics,Management science & operations |
Last-mile distribution planning
for fruit-and-vegetable cold chains
Yu-Hsiang Hsiao
Department of Business Administration,
National Taipei University, Taipei, Taiwan, and
Mu-Chen Chen, Kuan-Yu Lu and Cheng-Lin Chin
Department of Transportation and Logistics Management,
National Chiao Tung University, Taipei, Taiwan
Abstract
Purpose –The purpose of this paper is to formulate and solve a last-mile distribution plan problem with
concern for the quality of fruits and vegetables in cold chains.
Design/methodology/approach –The vehicle routing problem with time windows (VRPTW) is extended
based on the characteristics of fruit-and-vegetable cold chains. The properties of multiple perishable foods,
continuing decline in quality, various requirements for quality levels and optimal temperature settings during
vehicle transportation are considered in the VRPTW. The product quality level is defined by the estimationof
residual shelf life, which changes with temperature, and is characterized by a stepped decrease during the
transportation process as time goes on. A genetic algorithm (GA) is adapted to solve the problem because of
its convincing ability to solve VRPTW-related problems. For this purpose, solution encoding, a fitness
function and evolution operators are designed to deal with the complicated problem herein.
Findings –A distribution plan including required fleet size, vehicle routing sequence and what quality level
should be shipped out to account for the quality degradation during vehicle transportation is generated. The
results indicate that the fulfillment of various requirements of different customers for various fruits and
vegetables and quality levels can be ensured with cost considerations.
Originality/value –This study presents a problem for last-mile delivery of fresh fruits and vegetables which
considers multiple practical scenarios not studied previously. A solution algorithm based on a GA is developed to
address this problem. The proposed model is easily applied to other types of perishable products.
Keywords Genetic algorithm, Cold chain, Asia, Mixed method, Vehicle routing problem,
Last-mile distribution, Quality deterioration, Shelf life
Paper type Research paper
Nomenclature
Sets
ISet of customer nodes: I¼
{1, 2, …,r}
DNode of distribution center
I
U
Set of all nodes: I
U
¼{D,1,2,…,r}
ASet of arcs: A¼{(i,j): i,j∈I
U
,i≠j}
TSet of storage temperatures:
T¼{0, 1, …,s}
KSet of product categories:
K¼{1, 2, …,n}
QSet of product quality levels:
Q¼{1, 2, …,v}
q
1
Quality level requested by
customer: q
1
∈Q
q
2
Quality level used to substitute
for q
1
:q
2
∈Q,q
2
Wq
1
Decision variables
Xkiq1A0fg[NQuantity of product kwith q
1
level shipped to customer i
Xkiq2A0fg[NQuantity of product kwith q
2
level shipped to customer i
SXki A0fg[NShortage amount of product kto
customer i
Z
ij
¼{0, 1} Z
ij
¼1 if and only if arc (i,j)is
traveled
FijA0fg[NLoad of vehicle when traveling
on arc (i,j)
The International Journal of
Logistics Management
Vol. 29 No. 3, 2018
pp. 862-886
© Emerald PublishingLimited
0957-4093
DOI 10.1108/IJLM-01-2017-0002
Received 8 January 2017
Revised 9 August 2017
28 January 2018
Accepted 7 March 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0957-4093.htm
The authors gratefully acknowledge the support from the Ministry of Science and Technology of
Taiwan (Grant No. MOST 103-2410-H-009-029-MY3). This paper forms part of a special section
“Next-generation cold supply chain management: research, applications and challenges”.
862
IJLM
29,3
Yt
k¼0;1fgYt
k¼1 if and only if product kis
delivered at storage temperature t
MT
t
¼{0, 1} MT
t
¼1 if and only if there is a
storage space with temperature t
in vehicle
Nondecision variables and parameters
a
i
Time of arrival at customer i
C
T
Fuel cost of traveling vehicle
by time
C
I
Fuel cost of idling vehicle by time
C
M
Deliveryman cost by time
Q
C
Maximum capacity of vehicle
LNumber of vehicles
u
ij
Travel time on arc (i,j)
fBasic cost of storing one unit of
product in vehicle
gVariable cost of changing the
vehicle storage temperature by
one degree for on e unit of product
T
max
Highest storage temperature of
vehicle
L
T
Number of storage spaces in a
vehicle
s
i
Service time for customer i
e
i
Earliest time to begin service for
customer i
w
i
Latest time to begin service for
customer i
Dkjq1Amount of product kwith q
1
quality level requested by
customer j
P
kq
Selling price of q-level product k
SCkq1Shortage cost of q
1
-level product k
F
T
Amount of fuel consumed by
traveling vehicle by time (liters)
F
I
Amount of fuel consumed by
idling vehicle by time (liters)
εCarbon emission coefficient
(kg/liter)
K
c
Carbon emission cost per kg
MA large positive constant
1. Introduction
Fresh fruits and vegetables are temperature sensitive and perishable. The difficulty in
preserving the nutritional, hygienic and esthetic characteristics of fresh foods during
transportation presents a direct problem where the perishability needs to be handled in
ways not necessarily consistent with the traditional view of ambient-temperature
operations. The deterioration of fresh foods is generally caused by physical, chemical and
biological changes that occur throughout the food chain, which might over time compromise
nutritional, microbiological or sensory quality, and diminish the shelf life of food products
(Giménez et al., 2012). To improve food safety and quality but also to reduce the cost of
operations, one must ensure an intact cold chain from the production facility all the way to
consumers (Sahin et al., 2007). In particular, the interest in food cold-chain logistics has
increased in the last few years due to important, emerging societal issues, such as
globalization, quality alerts, and increased quality and safety demands (Sahin et al., 2007;
Kuo and Chen, 2010; Rong et al., 2011; Zou and Xie, 2013; Zhang and Chen, 2014; Chang et al.,
2015; Fredriksson and Liljestrand, 2015; Saif and Elhedhli, 2016; Mercier et al., 2017;
Hariga et al., 2017).
Last-mile distribution is the last part of the supply chain logistics process, which
involves a set of activities that are necessary for the delivery process from the last transit
point to the final drop point. Last-mile distribution is critical because it is responsible for the
delivery of products to end customers and is currently regarded as one of the most
expensive, least efficient and most polluting sections of the entire logistics chain.
(Boyer et al., 2009; Gevaers et al., 2011; Greasley and Assi, 2012; Aized and Srai, 2014;
Wang et al., 2016). The combination of various issues occurring in last-mile delivery
contributes to the complexity of obtaining an effective and efficient last-mile distribution
plan. For example, in order to reduce the delivery failure due to customers not being at
designated delivery points such as home, the delivery should meet the customer-given time
window; however, this inevitably compromises efficiency. The lack of critical mass in a
863
Fruit-and-
vegetable
cold chains
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