A multiobjective optimization model for sustainable reverse logistics

Journal of Cleaner Production 249 (2020) 119348
Contents lists available at ScienceDirect
Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro
A multiobjective optimization model for sustainable reverse logistics
in Indian E-commerce market
Pankaj Dutta a, *, Anurag Mishra a, Sachin Khandelwal b, Ibrahim Katthawala b
a
b
Shailesh J. Mehta School of Management, Indian Institute of Technology, Bombay, Powai, Mumbai, 400076, India
Flipkart Internet Pvt. Ltd., Embassy Tech Village, Devarabeesanahalli, Bellandur, Bengaluru, 560103, India
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 16 February 2019
Received in revised form
14 November 2019
Accepted 16 November 2019
Available online 18 November 2019
A cleaner and sustainable environment is becoming a topmost priority for both owners and stakeholders
involved in businesses. It could be achieved by adopting better sustainable practices like reduction in
waste through process of recycling, recovery and remanufacturing which helps to minimize both the cost
and environmental losses. With a recent surge in E-commerce market and online shopping in India there
is a need for a more efficient, sustainable and reliable reverse logistics design by including cost, environmental and social factors into consideration. With the factors considered above this paper proposes a
multi-objective logistics network model for the return products specifically pertaining to the Indian Ecommerce market. The components of this multi-echelon supply chain considered are Customer Markets, Warehouses, Delivery Hubs, Landfills, Incineration Centres and Recycling Centres. The multiobjective optimization is done on the three fronts of sustainability, namely, economical, represented
by cost, environmental, represented by environmental impact of different process, and social, which is
represented by work days created and lost due to harms at work. Different technologies are considered in
delivery hubs and mother warehouse which could result in a more efficient way of transferring and
processing products. Weighted goal programming (WGP) technique is used by weighing different objectives to minimize cost, environment impact and maximize the social responsibility. Finally, - model is
validated with a numerical example based on an online retail selling clothes. This study will help the
managers in deciding the number of facility stores and warehouses needed to open and operate, technology to be adopted for a more efficient way of transferring and processing products.
© 2019 Elsevier Ltd. All rights reserved.
Handling Editor: Kathleen Aviso
Keywords:
E-commerce
Reverse logistics
Sustainability
Multi-objective
India
1. Introduction
In the modern India, the value and importance of E-commerce is
growing day by the day as increasingly, consumers are starting to
prefer shopping from the comfort of their homes. The growth is
further fuelled by the large selection, transparency of prices across
retailers, availability of discounts, and rising internet penetration
across the Indian populace. The Indian e-commerce market is US$
50 Billion in size and expected to reach US$ 64 Billion by 2020 (Ecommerce sectoral report, 2018).
An important part of any Supply Chain (SC) is its Reverse
Logistics(RL). Reverse Logistics is “the process of planning,
* Corresponding author.
E-mail addresses: [email protected] (P. Dutta), [email protected]
(A. Mishra), sachin.k@flipkart.com (S. Khandelwal), Ibrahim.k@flipkart.com
(I. Katthawala).
https://doi.org/10.1016/j.jclepro.2019.119348
0959-6526/© 2019 Elsevier Ltd. All rights reserved.
implementing, and controlling the efficient, cost effective flow of
raw materials, in-process inventory, finished goods and related
information from the point of consumption to the point of origin for
recapturing value or proper disposal. Remanufacturing and refurbishing activities also may be included in the definition of reverse
logistics” (Hawks, 2006).
When it comes to e-commerce, the additional emphasis is on
returns management, a component of RL. According to Rogers et al.
“Returns management is defined as the process by which activities
associated with returns, gatekeeping, and avoidance that are
managed within the firm and across key members of the supply
chain” (Rogers et al., 2002). Returns management if handled efficiently and smoothly could make the difference in firms, addi€llecke
tionally it also helps to maintain the customer loyalty (Ro
et al., 2018; Ramanathan, 2011). The Worrisome thing is that still
RL or returns management is not being treated seriously by firms
and instead taken as a burden and unimportant for their growth
(Rogers et al., 2013).
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P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348
Another important objective that is looming over SC managers is
the issue of sustainability, firms are now being held responsible for
the environmental impact and social performance of the ecosystem
they create. The pressure is not only derived from the government
rules and regulations, but also from various stakeholders, employees, customers and NGOs. The 3 main elements of a sustainable
supply chain according to the Triple Bottom Line Approach are
(Nikolaou et al., 2013) e
1. Economic e This relates to the profitability of the supply chain.
The economic concept has been there before the concept of
sustainability and this is what most firms have been focusing on.
Profitability in the context of supply chain is essentially the
reduction of costs.
2. Environmental e The impact of all the different processes and
activities on the environment. It is certainly difficult to measure
the environment impact and less research has been done on this
front.
3. Social e The companies should not only restrict themselves to
seeing economic and environmental benefits but should push
themselves to achieve social benefits. This implies that companies, besides making profit, must think about compliance
with legal necessities, ethical principles, and esteem for people
and communities in all their activities (Pai et al., 2016).
Very few research has been carried out applicable to the
application of sustainability in E-commerce. Most research has only
focused on exploring the sustainable supply chain scenario and
strategy. The uniqueness of this paper lies that it applies the
concept of Sustainable Supply Chain Management to RL, and
especially to returns management in the context of Indian E-commerce scenario. This paper has used the triple bottom line approach
and considered the economic, environmental and social factors.
Fig. 1 describes the framework of the paper.
The remainder of this paper is organized as follows. In Section 2,
the literature in this area of research is reviewed. In Section 3, the
model related to the RL in E-commerce companies is formulated.
Section 4 deals with the solving methodology. Section 5 presents a
numerical example based on online retailer for cotton based
clothing. Section 6 includes the results obtained from solving the
numerical example using the WGP optimization method. Finally,
Section 7 presents the managerial implications and conclusion.
2. Literature review
This paper relates to the literature that considered RL-SC in
single channel structure and examines the location of delivery
centres, mother warehoused and recycling centres. It also considers
the technologies used in the various hubs and warehouses which
could help in increasing efficiency. We attempt to propose a model
for a RL network design problem, by considering the green and
sustainability issues and use a multi-objective Mixed Integer Linear
Programming (MILP) with goal programming to solve the model.
Henceforth, the literature survey in this study is subdivided into
three sections: (1) RL for supply chain (SC); (2) RL in E-Commerce
SC; (3) Sustainability.
2.1. Modelling of RL in supply chain
In one of the first study carried out by Fleischmann et al. (1997)
studied various characteristics and thereby suggest improvements
in RL system. The study categorised the dimension of RL system into
four major parts: Reuse motivation, type of recovered items, form of
reuse and involved actors. Also elaborated different models of RL
that exist, elaborated the distribution management and inventory
Fig. 1. Framework of the paper.
management. Pishvaee et al. (2009) developed an integrated forward and reverse logistics network design. They formulated a MILP
with deterministic values and then a stochastic equivalent of the
model is developed using the scenario-based stochastic approach.
Lee and Chan (2009) aimed to find a relevant model for optimization of products that offered an RL network based on radiofrequency identification (RFID) technology. Das and Chowdhury
(2012) aimed to minimize the overall processing costs and they
suggested a recycling, logistics model for various electronic product
wastes. They had broken the recycling phase into four major phases
namely collection, separation, recycling and repair. Diabat et al.
(2013) had designed a multi-echelon Rl design to minimize the
total cost consisting of renting, inventory, carrying cost, material
handling and shipping cost. Further, Genetic algorithm and Artificial immune system was implemented to run the model and
compare the results. Godichaud and Amodeo (2015) had integrated
the process of return in the conventional SC and proposed ways in
which remanufacturing could be a profitable option by implementing a well-sought inventory control policy. The model deals
with the stochastic demands and stochastic returns. This model is
further tested on three inventory policies corresponding to
different decision making.
Alshamsi and Diabat (2015) proposed a generic reverse logistics
design where they used a MILP) to optimize a RL network, which
P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348
decides on the number of sites to open, the capacities of the inspection centres and remanufacturing facilities. Galvez et al. (2015)
designed a RL network for a biogas plant. Network characterization
was done by defining different processes involved, actors, variables
associated are identified followed by describing different scenarios
possible and where the equipment could be located, additionally
defining the logistics network route and translating into mathematical language using linear equations and then Analytic Hierarchy Process (AHP) was used to make a peer comparison.
Kumar et al. (2017) have proposed a multi-period, multi-echelon
closed loop supply chain model in there model they also address
the issue of vehicle routing in their model. The network flow was
such there were facility stores, distributors to receive goods directly
form the customer or the facility store, used goods directly collected
from the customer and distributed to the disassembly location then
gods for disposal are sent to the location of disposal and repaired to
the distribution center from where they could again be distributed
and recycled goods again to the facility location and from here they
again enter the supply chain. The products that were received from
the remanufacturing center and repairing center were sold to the
secondary markets. Then solved the case using Artificial immune
system and particle swarm optimization and compared the results
from both the algorithms. Bortolini et al. (2018) designed a biobjective fresh food supply chain networks by considering both
reusable and disposing packaging containers, they applied this
model in an industrial application in Italy and suggested a shipping
mix of both containers are required.
2.2. RL in E-Commerce SC
E-commerce is defined as sharing business information, maintaining business relationships, operating business negotiations,
settling and executing agreements by means of telecommunication
networks, often the Internet, to achieve business transactions.
Kokkinaki et al. (2000) talks about the relation between RL activities and the information technology to examine these in the field
of E-Commerce applications. Dekker et al. (2002) through their
panel discussion related to RL in E-Commerce focussed on issues
pertaining E-commerce and RL, ranging from operations for
collection, selection and decision making for the optimal recovery
option of post-retail or surplus products nearing their life cycle. Yan
et al. (2012) have designed a reverse network design for e-commerce by considering four participants: factories, online retailers,
the 3 PLs and markets and only one type of the product for the Ecommerce enterprise and used a MILP to minimize the logistical
cost of the whole system.
Guo et al. (2017a,b) have proposed a multi-period model for the
reverse network design of the apparel E-commerce enterprises and
have eight elements in their model ranging from customers to
secondary markets with an aim to minimize the total logistics cost
as well as the number of primary recycling and union recycling
centre. The model was validated on a shanghai apparel ecommerce
enterprises. Guo et al. (2017a,b) had designed a Closed loop supply
chain (CLSC) network with an aim to reduce the carbon emissions
for fresh food e-commerce enterprises with route planning the
model had dual objectives one to reduce the overall cost of the
system and the other is to reduce the routing cost. The model was
validated on a shanghai commerce enterprise.
2.3. Sustainability and multiple goals in a RL network
The Integration of the green concepts and sustainability is an
evolving area of research. Along with sustainability the modelling
of reverse logistics network design with multiple objectives is
gaining interest of the researchers. The optimization in cost
3
function of model when coupled with additional goals like social,
customer satisfaction, enhancing service performance helps to
have a more robust and sustainable network design. Chaabane et al.
(2012) proposed a model for sustainable supply chain with multiple
goals of minimizing the costs and also reducing the negative effects
on the environment.
Paksoy et al. (2010) suggested a multi objective model for a
CLSC. The model aims to minimize the raw material and transportation cost along with minimizing the CO2 emissions. Ramezani
et al. (2013) developed a stochastic multi objective model considering three echelons in forward direction and two echelons in
reverse supply chain. The paper aimed at maximizing the profit,
customer responsiveness and quality. Silva et al. (2013) had
developed a return-oriented packaging model to optimize the
waste generation and reduce the environmental impacts keeping in
mind the minimization the costs and use of resources. Nikolaou
et al. (2013) had developed a model which included both the social responsibility of companies and sustainability in the RL SC
system. Bing et al. (2014) designed a sustainable network for the
household plastic waste, here the authors addressed that efficiency
is not the only concern for recycling system, sustainability is also a
crucial issue. The paper dealt with plastic recycling in Netherlands,
where different scenarios were seen concerning the placement of
different facilities pertaining to separation, sorting and inspection
and compared with the base scenario. MILP was used to minimize
the transportation cost and environmental impact in the network.
Ramos et al. (2014) solved a multi-objective vehicle routing problem considering the sustainable aspect i.e. economic, environmental, and social aspects in designing the RL systems.
Godichaud and Amodeo (2015) tried to optimize inventory level
and service performance. Two types of inventory returned inventory and serviceable inventory were taken into consideration.
Three policies were presented with returned products and then
simulation was carried on the first objective to optimize the cost
function. The second objective was to optimize the fill rate i.e. the
fraction of demands that could be immediately satisfied from hand.
Govindan et al. (2016) had developed a fuzzy multi-echelon, multiperiod, multi-objective model for a reverse logistics network
design. The multiple goals considered are minimize the net present
cost, minimize the environmental impacts and optimizing the social responsibility. The model was further validated in a medical
syringe industry. Zohal and soleimani (2016) proposed a multiobjective model in a closed green supply chain network which
had a seven-layer network with four echelons in the forward
supply chain and three echelons in the reverse supply chain. The
multiple objectives were to maximize the income and minimize the
CO2 emissions and cost. Gold industry real case study problem was
formulated and solved using Ant colony optimization.
Bal and Satoglu (2018) proposed a reverse network design for
Waste electric and electronic equipment (WEEE) with four goals
which are minimization of cost, minimization of environmental
effect, workforce balance and managing the legal targets. The
model is validated on WEEE of home appliances using goal programming. Zarbakhshnia et al. (2019) have suggested a multiperiod, multi objective model for a green closed supply chain
where the first objective is to minimize the operational cost, second
objective was to minimize the CO2 emissions and third objective
was to optimize the number of machines used and then model was
validated in a home appliance industry.
The main contributions differentiating our efforts from published papers on the subject are as follows.
Most of the above cited paper deal with the network design and
ignore the integration of both green and sustainable (such as
social issues). They have focused on the pure green and
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P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348
environmental issues. Therefore, it was considered to incorporate social issues besides economic and environmental aspects
into a model of decision making.
In all the studies, the economic objective is cost minimization
considering only the transportation aspect. Nevertheless, have
defined our economic objective function considering the
transportation and the processing costs as well.
Different technologies in delivery hubs and mother warehouses
was considered which could result in a more efficient way of
transferring and processing products.
On the whole, this is the first kind of work that proposed a
multi-objective optimization model for sustainable reverse logistics network design in E-commerce market.
3. Model formulation
In this research the attempt was to develop a model like that of
actual Indian large e-commerce firms. The importance of application of this model increases if considered the fact that such firms
maybe undergoing certain changes after the introduction of GST.
3.1. Motivation
The inspiration for our model from the way actual E-commerce
enterprises are operating in the present scenario, with regards to
the warehouse model, wherein the stock is purchased and kept in
retailer owned warehouses. The levels/echelons in the actual scenario are a step or 2 higher than in our model, but have taken 2
levels of warehouses, where warehouses will be owned by the firm
but the logistics be carried out by a third party. The idea having only
two levels in warehouse is not to further make the model complex
and also to ease on the mathematical formulation. since the
warehouse is just a part of our multi-echelon network design and
our main focus is here to optimize the whole network. The model is
illustrated in Fig. 2. The components of this multi-echelon supply
chain are Customer Markets, Fulfilment centres (Warehouses
owned by retailers), and Delivery Hubs, Disposal Centres (Landfills,
Incineration Centres and Recycling Centres are the three mediums
for disposal in this network).
There are only 3 disposal options for the product, namely,
recycling, incineration, and throwing in landfill
The warehouses will be owned and operated by the firm while
the logistics will be provided by third party
The distance between the customer market and delivery hub is
specified to be less than a certain distance.
The costs elements of the reverse logistics include and is limited
to transportation, processing at warehouse and fixed cost of
establishment of warehouse.
3.3. Model description
The forward logistics starts from the fulfilment centre and then
RL starts with returns management, when a customer requests
their order to be returned for reasons like product is defective or
want a size change for the item. Most products are disposed using
any of the three options i.e. Incineration, landfill and recycling
Centres.
Technology in Warehouses: The warehouses and delivery hubs
operated by different players often have different way of functioning owing to the different technologies used in them. Some of
them maybe highly automated, some partly automated.
Objective of the work: In designing the RL network, our objective
will be to optimize the three essentials for sustainability;
1. Minimizing the total costs incurred in the network
2. Minimizing the total environmental impact due to transportation and the different types of operations and recycling
processes e This impact will be quantified using the Eco Indicator 99 methodology, which is based on the Life Cycle
Assessment (LCA) methodology
3. Maximization of Social responsibility e To quantify this, the no.
of jobs created, and no. of work days lost due to harms at work
will be considered (Pishvaee and Razmi, 2012).
The indexes of the different parameters used, the decision variable, the objective functions, and constraints are explained below.
3.4. Mathematical formulation
3.2. Assumptions
Index
The list of assumptions considered while formulating the model
are.
f Index of incineration centres f ¼ 1; 2; 3…………f
h Index of Delivery Hubs h ¼ 1; 2; 3…………H
l Index of landfills l ¼ 1; 2; 3…………L
m Index of customer markets m ¼ 1; 2; 3…………M
n Index of recycling centres (NGOs) n ¼ 1,2,3 … … … … …. N
th Index of technology at delivery hubs th ¼ 1; 2; 3…………Th
tm Index of technology at FC tm ¼ 1; 2; 3…………Tm
w Index of Fulfilment Centres (FC) w ¼ 1; 2; 3…………W
The demand, overall, as well as for each customer market, is
deterministic
The sellers to the retailer have their own pre-established facilities and they supply to the online retailer using their own
logistics
There are only 2 levels of warehousing
Binary Decision Variables
Y th
h If delivery hub ‘h’ with technology th is to be opened or not
Yf If incineration centre ‘n’ is to be selected for disposal or not
Yl If Landfill ‘l’ is to be selected for disposal or not
Yn If RC ‘n’ is to be selected for disposal or not
Y tw
w If FC ‘w’ with technology tw is to be opened or not
Parameters
Fig. 2. E-Commerce reverse logistics model structure.
am Demand from customer market’
bm Returns from customer market ‘m’
bmh Returns from customer market ‘m’ to delivery hub ‘h’
P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348
ghl Amount of returns sent to landfill ‘l’ from delivery hub ‘h’
shn Amount of returns sent to recycling centre ‘n’ from delivery
from the Delivery Hubs to either the disposal centres or the FC.
To calculate the environmental impact, the Eco-Indicator 99
methodology has been used. This is a damage-oriented method for
Life Cycle Assessment which is much shorter than the original LCA
methodology. It was commissioned by the Ministry of Housing,
Spatial Planning, and Environment, Netherlands.
hub ‘h’
mhf Amount of returns sent to incineration centre ‘f’ from delivery hub ‘h’
jhw Amount of returns sent to FC ‘w’ from delivery hub ‘h’
X X X
m
þ
h
h
X X X tw
X X tl
mh
eith
eipw þ eihw
eipl þ eihl
bmh þ
jhw þ
ph þ eic
c
c ghl
th
X X
n
5
eitlpl
þ
eihn
c
h
shn þ
w
X X
h
tw
eitlpl
þ
eihf
c
l
tl
(1)
shf
f
Ath
h Lost days due to harms at work at delivery hub ‘h’ using
technology ‘th’
Atw
w Lost days due to harms at work at FC ‘w’ using technology
‘tw’
C Transportation cost of item per km
Dhl Distance of delivery hub ‘h’ from landfill ‘l’
Dhn Aggregate distance of delivery hub ‘h’ from organization
cluster ‘n’ for recycling
Dhf Distance of delivery hub ‘h’ from incineration centre ’f’
Dlw Distance of delivery hub ‘h’ from FC ‘w’
Dmh Distance of market ‘m’ from delivery hub ‘h’
eimh
Environmental impact of transporting product from
c
customer market ‘m’ to delivery hub ‘h’
eihw
Environmental impact of transporting product from dec
livery hub ‘h’ to FC
eihl
c Environmental impact of transporting product delivery hub
‘h’ to landfill ‘l’
eihn
c Environmental impact of transporting product delivery hub
‘h’ to recycling centre ‘n’
eihf
c Environmental impact of transporting product delivery hub
‘h’ to incineration centre ‘f’
eitw
pw Environmental impact of processing item at FC ‘w’ with
technology tw
eith
ph Environmental impact of processing item at delivery hub ‘h’
with technology th
eipl Environmental impact of processing item at landfill ‘l’
eipn Environmental impact of processing item at recycling centre
‘n’
eipf Environmental impact of processing item at incineration
centre ’f’
F th
h Fixed cost of establishing delivery hub ‘h’ with technology ‘th’
F tw
w Fixed cost of establishing FC ‘w’ with technology ‘tw’
J th
h Job opportunity created at delivery hub ‘h’ using technology
‘th’
J tw
w Job opportunity created at FC ‘w’ using technology ‘tw’
Oth
h Capacity at delivery hub ‘h’ with technology ‘th’
Otw
w Capacity at FC ‘w’ with technology ‘tw’
P th
h Processing cost at delivery hub ‘h’ with technology ‘th’
P tw
w Processing cost at FC ‘w’ with technology ‘tw’
3.4.1. Objective functions
The proposed model has three objective functions i.e. Economic,
environmental, and social.
3.4.1.2. Social objective function. To look into the social aspects,
given two indices are considered for social responsibility:(1) the
number of created job positions; (2) the average lost days resulted
from harms at work.
X X
h
X X
th
tw
th
tw
J th
J tw
w Aw Y w
h Ah Y h þ þ
w
th
(2)
tw
3.4.1.3. Economical objective function. This function simply sums
up all the costs related to the reverse logistics supply chain. The
objective will be to minimize the costs.
X X
h
X X
th
tw F th
F tw
w Yw
h Yh þ
th
þ
X X X
m
h
th
h
w
tw
w
tw
C Dmh þ P th
h bmh
X X X
XX
þ
ðCDhl þ Pl Þghl
C Dhw þ P tw
w jhw þ
þ
XX
h
ðC Dhn þ Pn Þshn þ
n
h
X X
h
l
C Dhf þ Pf mhf
(3)
f
Subject to Constraints:
tm
Y th
h ; Y m ; Yl ; Yn ; Yf ε f0; 1g c h; l; w; n; f
(4)
bmh Oth
h c m; h; th
(5)
jhw Otw
w c h; w; tw
(6)
am bmh c m; h
(7)
bmh ¼ mhf þ jhw þ shn þ ghl
(8)
X
bmh ¼ bm c m; h
(9)
h
X
Y th
h 1 c h; th
(10)
th
X
Y tw
w 1 c w; tw
(11)
tw
3.4.1.1. Environmental objective function. The environmental
objective function is defined based on: -The environmental impact
due to transportation from the customer markets to Delivery Hubs,
bmh ; mhf ; jhw ; shn ; ghl 2I
(12)
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P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348
According to the equation (1) minimizes the environment
impact due to transportation from market to DH, DH to FC, DH to
landfill, DH to RC, DH to IC along with the impact caused due to
processing at DH, FC, landfill, RC and IC. (2) optimizes the social
impact due to establishment of DH and FC. (3) Minimizes the
economic cost which includes fixed cost of setting up DH and MW,
transportation and processing cost at DH, MW, RC, IC, RC and
landfills. (4) ensures the binary constraint for decision variables.
(5)e(6) are the capacity constraints for DH and FC. (7) makes sure
that demand is greater than the returns. (8) maintains the volume
flow constraint where the total returns received is either sent to the
disposal centres or FCs. (9) maintains the volume flow constraint
for the demand. (10)e(11) maintains the technology constraint that
only one technology be selected for DH and MW. (12) enforces that
the demand and return be integer values.
4. Methodology
To solve the proposed multi-objective model, a WGP optimization method is used. Goal Programming (GP) is tool for multicriteria decision making problem, which can be thought of as being an extension to linear programming to handle objectives that
generally opposing to each other. It was first introduced by Charnes
et al. in (1955), more explicitly defined by the same authors in 1961
(Charnes and Cooper, 1962) and further developed by Ijiri (1965)
during the 1960’s. Lee (1972) introduced the lexicographic goal
programming variant where the priority level was added, it is also
termed as ‘pre-emptive’ goal programming. Flavell (1976) introduced Chebyshev goal programming where maximum deviation
from any goal was calculated. Charnes and cooper (1977) stated the
weighted goal programming method. Schniederjans and Hoffman
(1992) introduced the 0e1 goal programming. Further, Romero
(2001) extended the lexicographic goal programming. Though
there are several MODM techniques like goal attainment (Gembicki
&Haimes, 1975), Parametric Lp (Adler and Monterio, 1992), Lp
metric (Lee, 1980), fuzzy method (Tiwari et al., 1987), in this paper
we have used WGP to solve the proposed model. WGP helps the
decision maker whenever information relating to trade-offs between different objectives is needed. It also helps to make direct
comparisons between objectives. (Tamiz et al., 1998).
Larbani and Aouni (2011) in their GP model introduced a new
approach for generating efficient solutions. Sen and Nandi (2012)
have applied the GP in the field of planation management. Silva
et al. (2013) has applied multi-choice mixed integer goal programming for the sugar and ethanol milling company. Huang et al.
(2018) developed a GP based model system to study the dynamic
analysis of community energy flow. Bal and Satoglu (2018) have
used a GP in their triple bottom line approach model for the WEEE
items. In the model deterministic values are assumed and a WGP
optimization method is applied by weighing all the three objectives
of our model iteratively and then finding a suitable combination
fulfilling the set goals.
Algebraically, a WGP has the following structure:
k
P
Minimize a ¼
ðai ni þ bi pi Þ, (13) where ai and bi are the
i¼1
weights for the deviational variables
subject to fi(x)þni-Pi ¼ bi, i ¼ 1. . .. m,
(14)
To deal with the three objectives in our example problem via GP,
we need to introduce additional variables. These variables will
handle the deviations from the “goal” for each objective. Setting a
numeric goal for each objective is up to the user.
Then the new equation linking our objective function (Z) and its
goals with the new variables will be
Zi - bþ
i þ bi ¼ Gi i¼1,2,3..
(15)
Where i is the no. of goals for that objective function. Zi here describes the function f(x) that we are looking to minimize or maximize, Gi is the target value for the objective, bþ
i represents the
positive deviation from the target value and b-i is the negative deviation from the target value.
For our 3 objectives we will have 3 such equations
Environmental: Z1i - bþ
1i þ b1i ¼ G1i
(16)
Social: Z2i - bþ
2i þ b2i ¼ G2i
(17)
Economical: Z3i - bþ
3i þ b3i ¼ G3i
(18)
The new combined objective function would require both the
bþ, b terms as our functions are both minimization and maximization functions. There are 2 ways to generate a single objective
function from these equations
þ
þ
Non-weighted: Min Z0 ¼ bþ
1i þ b2i þ b3i
(19)
þ
þ
Weighted Approach: Min Z0 ¼ q1bþ
1i þ q2b2i þ q3b3i
(20)
Where q1, q2, q3 are the weights assigned to objectives Z1, Z2, and Z3
respectively.
5. Numerical example
To validate the model, the proposed datasets are hypothesized.
The data within these has been gathered from formal and informal
surveys, meeting, and telephonic conversations with industry experts and some essential data has been taken from a Mumbai based
e-commerce company. The scales, sizes, and all approximations
used have been inspired from literature as well as experts from the
online retail industry. Detailed investigation has been done and
experience been gathered to formulate information for testing the
model. Below is a proposed hypothetical case study based on online
retailer for cotton based clothing (see Table 1).
5.1. Background
An online retailer of clothing items is considered. Since each
type of textile clothing has different waste disposal and recycling
procedure therefore for simplicity purposes, have taken a retailer
that sells all kinds of clothing item made of only cotton. While using
eco indicators it was important to be careful as each indicator
signifies different waste processing methods. Hence, choosing
cotton will help to take a uniform single environment impact value
vide the Eco-Indicator 99 set of values. The retailer serves in four
major markets as listed in Table 2. After outsourcing all the logistics
for so long, the firm has finally decided to invest in their own logistics in terms of warehouses to make way for reduced costs and
increasing volume of business. They will still be using third party
logistics providers for their transportation needs. They plan to set
up optimum delivery hubs nearest to any of these four markets. All
these cities have their own disposal centres in the form a major
landfill or several Non-governmental organizations(NGOs) that are
spread throughout the city. They also must set up two fulfilment
centres with much larger capacities, that will serve these Delivery
Hubs. An analysis of their materials flow has suggested that it
would beneficial that they set up these fulfilment centres in any
two out of the four cities given in Table 2.
P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348
7
Table 1
Summary of literature review.
Researchers
Reverse Logistics
Sustainability
Economic
Fleischmann et al. (1997)
Lee and Chan (2009)
Millet (2011)
Das and Chowdhury (2012)
Huang et al. (2018)
Das and Chowdhury (2012)
Chaabane et al. (2012)
Pishvaee and Razmi (2012)
Nikolaou et al. (2013)
Ardeshirilajimi and Azadivar (2015)
Mohanty and Prakash (2014)
Ramos et al. (2014)
Roghanian and Pazhoheshfar (2014)
Guo et al. (2017a,b)
Govindan et al. (2016)
Bal and Satoglu (2018)
Zarbakhshnia et al. (2019)
Our Paper
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Social
Objective
Single
Multiple
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Table 2
Index of customer markets, fulfilment centres, and technology.
Index Customer
Markets
Index Fulfilment
Center
Index Technology
1
2
3
4
1
A
1
2
B
2
W
X
Y
Z
E-commerce
Environmental
Traditional NonAutomated
High Level of
Automation
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
The warehouses that they have to setup can either be labour
operated or involve considerable extend of automation. In deciding
the two options, there is a trade-off in establishing fixed costs
versus operating costs. Also, if they decide to establish a traditional
warehouse, they would be employing more personnel, thus
creating more jobs/work-days, on the other hand the number of
harms to individuals in such a setting would be more causing loss in
work days. Alternatively, an automated warehouse would employ
less people but there would be less harms due to work in this
scenario.
Fig. 3 represents how the actual problem would be represented
on a physical map.
The Indexes and locations for Delivery Hubs, Landfills, and
Recycling centres are same as that of Customer Markets.
5.2. Fixed cost derivation
The data about fixed costs is represented in Table 3. The distance
between delivery hubs and fulfilment centres is presented in
Table 4. The report was also able to provide with the aggregate
distance of the warehouse from the city centres, or the customer
markets, in the mentioned case. Same is mentioned in Table 5. The
distance between delivery hubs and landfill is presented in Table 6
and the distance between delivery hubs and recycling centres is
presented in Table 7.
5.3. Volume and distances
The analysis here is being executed for a single period, i.e. one
month. The complete demand for a month is appx. 10 million units.
The returns that the firm must manage from the above markets is to
the tune of 1 mil. (industry average of ~10%). As per past data,
majority of the returns, i.e. ~60% are due to a change in the size of
the clothing item. These items are simply sent back to the fulfilment
centre from the delivery Hub. The remaining 40% of the items are
defective and cannot be resold.
5.4. Processing and transportation costs
Fig. 3. Illustration of options for Delivery Hubs/Fulfilment Centre.
The wage is taken to be an approximate of 20,000 for each
worker, and the unit cost of electricity is taken 10 Rs, which is close
to the commercial cost of one unit of electricity, although it varies
8
P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348
Table 3
Fixed Cost of establishing
Technologies(Rs).
Delivery
Hubs
and
Fulfilment
Centres
with
Delivery Hub options
Fixed Cost with Tech1
Fixed Cost Tech2
W
X
Y
Z
17340000
17100000
16300000
16900000
22340000
22100000
21300000
21900000
Fulfilment Centre Options
Fixed Cost with Tech1
Fixed Cost Tech2
A
B
43350000
36375000
55850000
48875000
by states and cities. The units consumed are taken in proportion to
the volume handled by each hub. Note that there will be two
processing costs for each technology that is employed, that is
because the electricity consumed in case of automated warehouse
will be higher leading to higher processing cost per unit. The
various potential processing at the delivery hubs and warehouses
are given in Table 8.
Although, the cost should vary with geographies due to differing
transporter services as well different diesel rates, it comes out to be
roughly around Rs. 1. Per km per unit, and thus, we will take the
same for simplicity of calculations.
5.5. Metrics for social and environmental objective
Table 4
Distance between delivery hub options and fulfilment Centres(km).
FULFILMENT CENTRES
DELIVERY HUBS
W
X
Y
Z
A
B
20
1400
1450
2200
2100
1000
1850
350
Table 5
Distance between customer markets and delivery Hubs(km).
DELIVERY HUBS
CUSTOMER MARKETS
W
X
Y
Z
W
X
Y
Z
1
1400
1450
2200
1400
1
2000
1300
1450
2000
1
1650
2200
1300
1650
1
Table 6
Distance between delivery hubs and Landfill(km).
The social objective functions need only two data sets. The
number of labourers employed at each of the possible warehouses
and the number of harms at work that would occur due to the
different technologies. The latter, again simplified by taking as 10%
for non-automated warehouses and 2% for automated warehouses.
Both units are measured in work days that are utilised or lost. So,
the no. of jobs must be multiplied by 30, considering our period is
one month, and the harms would set each worker on an average by
2 work days, so they will be multiplied by 2. The data about jobs
created and harms at work is given in Table 9.
The environmental impact for processing at the warehouses is
directly proportional to the electricity consumer per unit processed. The same is mentioned in Table 10.
6. Experimental results
Microsoft Excel’s Solver Tool was used to solve the above case.
By using this it was clearly identified which delivery centres,
mother warehouses, landfills and recycling centres are selected to
reduce all the three costs i.e. economic, environmental and social.
The results obtained are plotted in the form of graphs. Graphs
are plotted while varying one objective goal value and keeping the
other two as constants. Following are the results obtained.
LANDFILL
DELIVERY HUBS
W
X
Y
Z
W
X
Y
Z
10
1400
1450
2200
1400
10
2000
1300
1450
2000
10
1650
2200
1300
1650
10
Table 7
Distance between delivery hubs and recycling Centres(km).
RECYCLING CENTRE
DELIVERY HUBS
W
X
Y
Z
W
X
Y
Z
50
1400
1450
2200
1400
50
2000
1300
1450
2000
50
1650
2200
1300
1650
50
6.1. Varying Economic Objective Goal
On keeping the environmental and social objective goals to be
constant and varying the Economic objective goal, below results
were observed. The values of environmental and social goal were
kept constant were taken from the iteration 1 of Table 11. The
economical goal was changed regularly to see what changes it will
create in opening of facilities. Fig. 4 shows the results that were
simulated for delivery hubs, mother warehouses, landfills and
recycling centres. For e.g. for economic goal of 67,00,00,000 it can
be seen that for delivery hub H1T1, H2T1, H4T2 are too be opened as
delivery hub with technology, W1T2 and W2T1 are opened as
mother warehouse with technology. No landfills destination that
are too be opened and N2, N3 are the recycling center that too be
opened. It can be see observed in case of delivery hubs, delivery hub
2 with the second technology i.e. H2T2 and delivery hub 3 with the
Table 8
Processing Cost per Unit for Delivery Hubs with both Technologies.
Delivery Hub options
W
X
Y
Z
Tech 1
Tech 1
Wages(Rs)
Electricity Consumed(Kwh)
Processing Cost per Unit(Rs)
Wages(Rs)
Electricity Consumed(Kwh)
Processing Cost per Unit(Rs)
1800000
1800000
1250000
1250000
150000
150000
125000
125000
13
13
11
11
1125000
1125000
781250
781250
225000
225000
187500
187500
9
9
7.75
7.75
P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348
9
Table 9
Jobs created, and harm accrued in work days.
Delivery Hub options
Jobs (Work Days) T1
Jobs (Work Days) T2
Harms (Work Days) T1
Harms (Work Days) T2
W
X
Y
Z
1800
1800
1500
1500
1125
1125
937.5
937.5
12
12
10
10
2.4
2.4
2
2
Table 10
Environmental Impact of processes and transportation.
Clothes considered in 1 kg
2
Environmental
Environmental
Environmental
Environmental
Environmental
Environmental
Environmental
Environmental
Environmental
Environmental
Environmental
140
0.07
34
0.017
4.2
2.1
8.2
4.1
100
60
.6
impact
impact
impact
impact
impact
impact
impact
impact
impact
Impact
Impact
in transportation to DH, LF and RC per ton
in transportation to DH, LF and RC per item
in transportation to FC per km
in transportation to FC per item
in Processing at Landfill per kg
in Processing at Landfill per item
in Processing at Recycling Centre per kg
in Processing at Recycling Centre per item
in Clothes processed per Kwh electricity consumed
due to electricity per Kwh
per cloth processes
Table 11
Total costs and selection of hubs, warehouses, landfill and recycling centres.
-
Iteration 1
Iteration 2
Iteration 3
Iteration 4
Iteration 5
Iteration 6
Iteration 7
Iteration 8
Economic Goal
Environmental Goal
Social Goal
Economic Cost
Environmental impact
Social Cost
Delivery Hubs Opened
670000000
27000000
10000
670000000
23173066
10223
H1T1, H2T1,H3T2,
H4T2
W1T2, W2T1
660000000
26000000
9500
659999852
19367352
9557.6
H1T1, H2T2,H3T2,
H4T2
W1T2, W2T1
650000000
25000000
9000
629305242
25000020.2
9140.7
H1T2, H2T2,
H4T2
W1T1, W2T1
640000000
24000000
8500
638942809
23999998.4
8953.6
H1T2, H3T2,
H4T2
W1T1, W2T1
630000000
23000000
7500
598768560
22999998.2
7956.7
H1T2, H2T2,
H4T2
W1T1, W2T2
620000000
22000000
7000
606989662
22000000.7
7769.6
H1T2, H3T2,
H4T2
W1T2, W2T1
610000000
21000000
6500
609995405
20999950
6585.6
H1T2, H3T2,
H4T2
W1T2, W2T2
600000000
20000000
6000
533630033
19999997.4
8103.5
H1T1, H2T1,
H4T2
W1T2, W2T2
e
N3,N4
e
N3,N4
e
N1,N4
L1,L3,L4
e
L3
N1,N2
L3,L4
N1
L1
N3,N4
L1,L2,L4
e
Mother Warehouse
Opened
Landfills
Recycling Center
first technology i.e. H3T1 were opened in most number of the cases.
In case of the mother warehouses it could be stated that mother
warehouse with first technology was opened in all cases except
one. With changing economic goals, the landfills were less
preferred compared to the recycling centres. In case for the recycling centres N3, N4 are opened in most cases for most of the
economic goals.
6.2. Varying Environmental Objective Goal
Fig. 4. Varying economic objective goal.
In this example by keeping the economic and social objective
goals to be constant and varying the environmental objective goal,
following results were found. Fig. 5 shows the results that were
simulated for delivery hubs, mother warehouses, landfills and
recycling centres.
The values of economic and social goal were kept constant were
taken from the iteration 2 of Table 11. The environmental goal was
changed regularly to see what changes it will create in opening of
facilities. In Fig. 5 for example for environmental goal of
27,00,00,000 it can be seen that for delivery hub H1T1, H2T2, H3T1
are opened as delivery hub with technology, W1T1 and W2T1 are
opened as mother warehouse with technology. L1 as the sole
landfill destination that too be opened, with N2, N3 are the recycling center that too be opened. It can clearly be seen that in case of
delivery hubs, delivery hub 1 with the first technology i.e. H1T1 and
10
P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348
Fig. 5. Varying environmental objective goal.
delivery hub 2 with the first technology i.e. H2T1 were preferable in
most of the environmental goal varying.
In case of the mother warehouses it is seen that mother warehouse two with technology i.e. W2T1 was opened in all cases
irrespective of the environmental goal. Landfill L1, L4 was preferred
landfills that were opened. Recycling centres N3 and N4 are
selected more as compared to the other two centres.
6.3. Varying Social Objective Goal
As shown in Fig. 6, - the economic and environmental objective
goals were kept constant and the social objective goal was varied.
The social goal was changed regularly to see what changes it will
create in opening of facilities. In Fig. 6 for example for social goal of
10,000 with the rest values of economic and environmental goal is
taken from iteration 10 of Table 11. It can be seen that for delivery
hub H1T1, H2T2, H3T1 and H4T2 are opened as delivery hub with
technology, W1T2 and W2T2 are opened as mother warehouse
with technology. No landfills are too being opened and N2, N3, N4
Fig. 6. Varying social objective goal.
are the recycling center that was too be opened. It is found that
delivery hub 4 with technology 2 in all cases irrespective of social
goal. Among the mother warehouses W2T21 was selected in most
of the cases. Landfill L3 was the only landfill that too be opened
with changing social goals, while N4 was the recycling centre
opened in all cases.
Weights were also assigned to all the three objective goals to
check whether it would influence selecting the hubs, warehouses,
landfills and recycling centres. Where W1 is the weight assigned to
the Economic goal, W2 is the weight assigned to the environmental
objective goal and W3 is the weight assigned to the Social goal. The
values were taken from the iteration 1 of Table 11. Fig. 7 shows the
results that were obtained on assigning different weights to the
objective functions, the different weights were run on the goal
values of 1st iteration from Table 11. The figure depicts how varying
the weights could impact the decision of opening different
facilities.
Fig. 7. Varying weights for objective functions.
680000000
12000
660000000
10000
640000000
8000
620000000
6000
11
Social Goal
Economic Goal
P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348
600000000
4000
580000000
400000000
2000
600000000
0
6000
Econmic Cost
7000
Social Cost
8000
9000
10000 11000
Fig. 8. The effect of (a) Economic goal Vs Economic Cost (b) Social Goal Vs Social Cost.
The Table 11 indicates how varying the costs and goals is
influencing the closing and opening of the delivery hubs, warehouse, and recycling centres. While in all iterations the no of delivery hubs remained either two or three, the no of mother
warehouse to be opened remained constant with two warehouses
in each iteration. The only changes apart from the total cost
incurred was the varying the no of landfilling centre and the
recycling centres to be used. In iteration 5 there is a one landfill
center and two recycling centres while in iteration 8 there are only
landfill and in iteration1 there are only recycling centres which has
led to the steep rise in the overall cost. There are trade-offs in each
iteration among the three costs and a perfect balance is to be
decided by the manager by taking the future environmental impacts and social responsibility into account. The Fig. 8(a) shows
Economic goal vs economic cost and with increasing goal the
economic cost also increases steadily then after a point the rise in
economic cost slows down with increase in economic goal. Fig. 8(b)
shows Social goal versus social cost and both the goal and cost are
increasing together, henceforth then economic goal decreases with
the rising social goal to accommodate the target social target and to
accommodate a small increase in the social goal requires a high
increase in the cost once the social target is reached.
minimum probability of ai and the constrained (9) can be defined
as follows.
6.4. Uncertain Return Market
This study has significant prospects and implications for SC
Managers and even the leadership of online retailing companies.
Online retailing is still a relatively new industry in India and
managers are yet to explore the many distinct opportunities that
are present to optimize costs and other factors. This study is first of
its kind where the network designed in the context of e-commerce
and numerical example which is based on online retailer for cotton
based clothing is specific to the Indian context from where the
managers can take a clue of implementing it to their firm by varying
the goals as per their firm need and expanding the model as when
required, by keeping the multiple objectives as per the model. The
In real situation, the return product market may be uncertain
and in such scenario, the exact amount of return products may not
be known with certainty. In order to capture this uncertainty in the
model, it was assumed that the return products capacity in the
b Þ with specified
“Return Market” follows normal distribution ( b
m
mean and standard deviation (10% of its mean). Thus the associated
constrained of expected market return would become stochastic in
nature. For this-chance-constrained programming (Charnes and
Cooper, 1959) was used where the constraint i is realized with a
P
X
bmh bb m am
c m; h
(21)
h
The following Table 12 provides the results of different values
for economic cost, environmental cost and social cost for different
values of goals with varying chances or probability of realization of
the constraints. Then compared the uncertain case with certain
market return and it was found that the percentage changes in
social cost is quite high whereas the changes in environmental cost
is insignificant. The Fig. 9 compares the cost with different probabilities for the chance constraints in certain vs uncertain market,
viz.; (a) Economic costs in uncertain case was higher in most of the
cases, (b) Environmental cost showed a varying trend while with
the first set of goal the environmental cost in uncertain market was
higher or equal to the certain market while with the second set of
goals certain market cost was higher and (c) Social cost in uncertain
market was always higher than the certain market in all the
scenarios.
7. Managerial implications and conclusions
Table 12
Comparing costs in Uncertain Vs Certain Market.
Uncertain Market return
Costs
EC
EVC
Certain Market return
SC
% change in costs due to uncertainty
EC
EVC
SC
% Eco. Change
%Env. Change
%S ocial change
659999852
659999852
659999852
659999852
19367352
19367352
19367352
19367352
9557.6
9557.6
9557.6
9557.6
0.8766419
0.293556
0.0011398
0.000268
1.0049469
0.0625489
0.0533603
0.074881
30.95337742
30.95337742
30.95337742
30.95337742
629305242
629305242
629305242
629305242
25000020
25000020
25000020
25000020
9140.7
9140.7
9140.7
9140.7
3.2884373
4.5696649
3.2885755
4.8772578
0.40922
0.829462
0.871984
0.973594
18.63424027
18.63424027
18.63424027
18.63424027
1-a
Goals: Eco-660000000,Env.-26000000, Social:-9500
0.8
665785687
26000000
12516
0.9
658062383
19780174
12516
0.95
660007375
19719530
12516
0.99
659998081
18873139
12516
Goals: Eco-650000000,Env.-25000000, Social:-9000
0.8
649999550
22424775
10844
0.9
658062383
19780174
10844
0.95
650000420
19512579
10844
0.99
659998081
18873139
10844
12
P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348
A model to set up reverse logistics supply chain is formulated in
this paper. This paper specifically caters to the existing and upcoming e-commerce firms aid in decision making towards setting
up of their RL. The novelty of this paper lies in that it combines the
concepts and ideas of Sustainability, e-commerce in the Indian
context, and RL, with a focus on returns management. The SC
structure used here is a compact and combined version of the one
being used by existing Indian e-commerce firms and sections
derived from literature covering concepts relating to the triple
bottom line approach.
GP was used to solve this model. A numerical example was
formulated, the data for which was determined using inputs from
experts from the online retailing industry and a specific firm in
Mumbai, dealing in clothing accessories like t-shirts. The solution was
obtained using Microsoft Excel. The solution was tested by varying the
goals for each objective vehemently and changing weights for
different goals to get the full extent of the flexibility of the model.
implementation of this will help managers optimize their supply
chain to be sustainable as firms are coming under pressure to
reduce their carbon footprint, increase their social deliverance to
the society and hence by adopting the important triple bottom line
approach, striking the right chord by balancing the economic,
environmental and social costs as while optimizing the total cost he
has to keep in mind the environmental impacts and social responsibility into account. This study will help the managers in
deciding the number of facility stores and warehouses needed to
open and operate, technology to be adopted for a more efficient
way of transferring and processing products. The strategic long
term decisions could then be taken relating to the opening of new
warehouses and which technology to be adopted for the respective
delivery hub and mother warehouse. The numerical example will
help to identify the different weights to give to different objectives
while minimizing the costs, maximizing the social responsibility
and achieving the set goals.
Uncertain Vs Certain Market
670000000
660000000
650000000
640000000
630000000
620000000
610000000
0.8
0.9
0.95
0.99
0.8
Uncertain EC
0.9
0.95
0.99
Certain EC
(a)
Uncertain Vs Certain Market
30000000
25000000
20000000
15000000
10000000
5000000
0
0.8
0.9
0.95
0.99
Uncertain EnvC
0.8
0.9
0.95
0.99
Certain EnvC
(b)
Uncertain Vs Certain Market
15000
10000
5000
0
0.8
0.9
0.95
0.99
0.8
Uncertain SC
0.9
0.95
0.99
Certain SC
(c)
Fig. 9. Cost comparison with different probabilities for the chance constraints in certain vs uncertain market: a) Economic costs b) Environmental costs c) Social costs.
P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348
There exists plenty of scope for further research on this topic.
Studies could be done firstly, combining forward and reverse logistics to obtain a complete and definitive model. Next, a more
varied range of products could be used to test out the model.
Additionally, this could be made a multi-period model and to
duplicate life-like scenarios, certain factors like total and individual
demands of the customer market could be made uncertain in
nature.
Author contribution
Pankaj Dutta: Conceptualization, Writing- Original draft preparation and Supervision.
Anurag Mishra. Methodology, Formal analysis and WritingReviewing.
Sachin Khandelwal: Software, Validation, Data curation.
Ibrahim Katthawala: Editing, Visualization.
Declaration of competing interest
The authors declare that they have no known competing
financial interests or personal relationships that could have
appeared to influence the work reported in this paper.
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