A decision support system of green inventory-routing problem
| Pages | 89-110 |
| Published date | 04 February 2019 |
| DOI | https://doi.org/10.1108/IMDS-11-2017-0533 |
| Date | 04 February 2019 |
| Author | Gia-Shie Liu,Kuo-Ping Lin |
| Subject Matter | Information & 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 decision support system of
green inventory-routing problem
Gia-Shie Liu
Department of Information Management,
Lunghwa University of Science and Technology, Taoyuan, Taiwan, and
Kuo-Ping Lin
Lunghwa University of Science and Technology, Taoyuan, Taiwan and
Institute of Innovation and Circular Economy, Asia University, Taichung, Taiwan
Abstract
Purpose –The purpose of this paper is to develop a decision support system to consider geographic
information, logistics information and greenhouse gas (GHG) emission information to solve the proposed
green inventory routing problem (GIRP) for a specific Taiwan publishing logistics firm.
Design/methodology/approach –A GIRP mathematical model is first constructed to help this specific
publishing logistics firm to approximate to the optimal distribution system design. Next, two modified
Heuristic-Tabu combination methods that combine savings approach, 2-opt and 1-1 λ-interchange heuristic
approach with two modified Tabu search methods are developed to determine the optimum solution.
Findings –Several examples are given to illustrate the optimum total inventory routing cost, the optimum
delivery routes, the economic order quantities, the optimum service levels, the reorder points, the optimum
common review interval and the optimum maximum inventory levels of all convenience stores in these
designed routes. Sensitivity analyses are conducted based on the parameters including truck loading
capacity, inventory carrying cost percentages, unit shortage costs, unit ordering costs and unit transport
costs to support optimal distribution system design regarding the total inventory routing cost and GHG
emission level.
Originality/value –The most important find ing is that GIRP model wi th reordering point inv entory
control policy should b e applied for the first replenishme nt and delivery run and GIRP model wit h periodic
review inventory contro l policy should be conduc ted for the remaining repl enishment and delivery ru ns
based on overall simulatio n results. The other very im portant finding concer ning the global warming
issue can help decision m akers of GIRP distribu tion system to select the appropriate type of tr uck to
deliver products to all r etail stores located in t he planned optimal deliv ery routes depending on G HG
emission consumptio ns.
Keywords Decision support system, 2-opt and 1-1 λ-interchange heuristic method,
Green inventory routing problem, Greenhouse gas emission, Heuristic-Tabu combination method,
Savings method
Paper type Research paper
Nomenclature
Decision variables
Quan
vgh
quantity of goods delivered from
convenience store gto convenience
store hby truck v
Rop
vgh
the inventory level of reorder point
for hproceeded by gon route v
T* the optimal common review
interval for all retail stores
Mn
ithe optimal maximum inventory
level for each retail store i
X
vgh
1, if convenience store g
immediately proceeds to
convenience store hon route v
(truck v). 0, otherwise
α
i
service level of convenience store i
Namount of convenience stores
Vamount of trucks
cap truck loading capacity
Ac allocated cost of truck purchasing
cost
Ftc truck fixed dispatch cost
Vtc variable transport cost per
kilometer
h
+
unit carrying-related cost
Industrial Management & Data
Systems
Vol. 119 No. 1, 2019
pp. 89-110
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-11-2017-0533
Received 14 November 2017
Revised 25 February 2018
12 March 2018
Accepted 24 March 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
89
A DSS of green
inventory-
routing
problem
h
s
unit shortage-related cost
Ounit ordering cost
FCdis fuel consumption per kilometer
(L/Km)
ghg greenhouse gas per liter of diesel
fuel (CO
2
-kg/L)
LT lead time
E
(z)
unit normal loss integrals
Dem
vgh
total volume of goods sent within
the defined period from
convenience store gto hby truck v
dem
gh
demand of goods for convenience
store hper day
Lad
kgh
the average demand of point hin
route kduring the lead time
S
d
standard deviation of demand
per day
Sd
0the lead time’s standard deviation
of demand (Sd
0¼Sdffiffiffiffiffiffiffi
LT
pfor
ROPIRP, sd
0¼Sdffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
LT þTn
p
for PRIRP)
Plikelihood of being not out of stock
during the lead time (service
level α)
Z
α
standard normal random variable
at service levelα
Dis
vgh
delivered distance from
convenience store gto convenience
store hby truck v
Y
ij
1 if convenience store jis assigned
to depot i; 0 otherwise
uone specific route in the proposed
IRP distribution system
uall other routes in the proposed
IRP distribution system
T_cost total inventory routing cost
1. Introduction
Transportation and inventory costs are inter-related and comprise the greatest part of
total logistics cost (Ballou, 2004). One of the renowned topics typically discussed in this
regard is the inventory-routing problem (IRP). In the case of publishing merchandise with
a high return percentage, such as magazines, books, greeting cards, newspapers and
catalogs (Langley et al., 2009), the inventory-related costs increase significantly due to
the return of out-of-date products. Clearly, precise prediction of the sale volumes of the
merchandiseiscritical.Unfortunately, these publishing logistics firms usually decide the
delivered quantity of products for each retail store and the related vehicle delivery routes
based on their past experiences. Accordingly, the IRP in this study will determine the
optimum delivery routes, the economic order quantities, the optimum service levels,
reorder points, review interval for all retail stores, and the related maximum level of
products for each convenience store located in those designed routes based on the
minimum total inventory routing cost criterion.
Global warming is regarded as one of the most difficult challenges of this century, thus
greenhouse gas (GHG) emissions are progressively turning into a main issue for the whole
world. To avoid global warming getting worse, several countries are following “green”
principles ruled by Kyoto Protocol in 2005. The growing globalized industrial outsourcing
has resulted in increased transportation operations and GHG emissions over past two
decades. Consequently, transportation operations become the major contributor to global
warming, causing the recent outspread of green logistics (Mirzapour Al-e-hashem and
Rekik, 2014). In Taiwan, transportation portion occupied nearly 13.9 percent of the total CO
2
emissions produced in 2011, of which, CO
2
emissions from road transport portion
constituted around 94.7 percent (Ministry of Transportation and Communication, 2012;
Tseng et al., 2014). Due to the increasing environmental consideration by the whole world,
this research tries to develop a mathematical model to help logistics firms to approximate to
the optimal design of their distribution systems by methodologically and practically dealing
with IRP and the produced GHG emission level simultaneously. It is called as a green
inventory routing problem (GIRP) in this study.
90
IMDS
119,1
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