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Research ArticleA Collection-Distribution Center Location and AllocationOptimization Model in Closed-Loop Supply Chain for ChineseBeer Industry
Kai Kang, XiaoyuWang, and Yanfang Ma
School of Economics and Management, Hebei University of Technology, Tianjin 300401, China
Correspondence should be addressed to Yanfang Ma; [email protected]
Received 13 October 2016; Revised 4 March 2017; Accepted 9 March 2017; Published 1 May 2017
Academic Editor: Shuming Wang
Copyright ยฉ 2017 Kai Kang et al.This is an open access article distributed under the Creative CommonsAttribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Recycling waste products is an environmental-friendly activity that can result inmanufacturing cost saving and economic efficiencyimproving. In the beer industry, recycling bottles can reduce manufacturing cost and the industryโs carbon footprint. This paperpresents a model for a collection-distribution center location and allocation problem in a closed-loop supply chain for the beerindustry under a fuzzy random environment, in which the objectives are tominimize total costs and transportation pollution. Bothrandom and fuzzy uncertainties, for which return rate and disposal rate are considered fuzzy random variables, are jointly handledin this paper to ensure a more practical problem solution. A heuristic algorithm based on priority-based global-local-neighborparticle swarm optimization (pb-glnPSO) is applied to ensure reliable solutions for this NP-hard problem. A beer company casestudy is given to illustrate the application of the proposed model and to demonstrate the priority-based global-local-neighborparticle swarm optimization.
1. Introduction
Due to resource scarcity and environmental concerns, re-sponsible companies are beginning to pay attention to thefuture of the planet and the global environment. Recyclingused products for remanufacturing is, therefore, becomingof greater importance in supply chain management, a movethat can dramatically reduce carbon emissions [1]. Closed-loop supply chain (CLSC) combines the forward supply chainwith a reverse supply chain to cover the whole productlife cycle [2], with the manufacturing of new products andthe transportation to customers via distribution centers andretailers as the forward supply chain and recycling, sorting,disposal, and remanufacturing as the reverse supply chain. Inrecent years, the CLSC has received a great deal of academicand business attention because of the need to be sociallyresponsible, global environmental concerns, and governmentlegislation [3, 4], all of which have motivated companies topay more attention to recycling to reduce costs and lessentheir carbon footprint.
Facility location and allocation problems (FLAPs) havebeen widely studied. Subramanian et al. [5] developed
priority-based simulated annealing to solve a CLSC networkdesign problem, in which the distribution center (DC) andthe centralized return collection center (CC) were set. Aminand Zhang [6] presented facilities location model for man-ufacturing and remanufacturing plants and CLSC collectioncenters, which included demand and return uncertainties.Subulan et al. [7] developed a CLSC network design modelfor the lead/acid battery industry that considered bothfinancial and collection objectives. CLSC network design ina competitive environment with price-dependent demandwas examined by Rezapour et al. [4], in which the DCand CC were separately built. Zeballos et al. [8] proposed amodel for a multiperiod CLSC design and planning problemwith demand uncertainty that had ten echelons in whichthe DC and CC were considered. Wang et al. [9] developeda granular robust model for a two-stage waste-to-energyfeedstock flow planning problem with uncertain capacityexpansion costs. Tokhmehchi et al. [10] developed a hybridapproach to solve a closed-loop supply chain location andallocation problem that considered the minimization of totalcost. Vahdani and Mohammadi [11] proposed capacitatedbidirectional facilities for CLSC conduct distribution, in
HindawiMathematical Problems in EngineeringVolume 2017, Article ID 7863202, 15 pageshttps://doi.org/10.1155/2017/7863202
2 Mathematical Problems in Engineering
which a multipriority queuing system was studied. As agrowing number of companies are now engaging in recyclingactivities due to economic and environmental concerns,distribution and collection activities using the same vehiclehave been found to reduce carbon emissions and transporta-tion costs as empty loads can be avoided. In this paper, tobenefit company operation and reduce construction costs, adistribution center (DC) is combined with a collection center(CC) into a collection-distribution center (CDC). In practice,as the recycled product owners are usually at the samelocation as the potential new product buyer [12], a DC/CCcombination has lower construction and operating expensesand can significantly reduce environmental pollution.
Ramkumar et al. [13] developed a multiechelon, mul-tiperiod, multiproduct closed-loop supply chain networkmodel which was solved using a genetic algorithm with fixedvariables. Kaya and Urek [12] presented a facility location-inventory-pricing model without uncertainty to determineoptimal facilities locations. Barz [14] proposed an optimiza-tion model for a two-stage capacitated facility location andallocation problem with additive manufacturing, in whichall variables were certain. Jindal and Sangwan [15] devel-oped a multiobjective model for a CLSC network designproblem with the economic and environmental factors beingfuzzy uncertain variable and the DC and CC were separate.Ramezani et al. [16] conducted research into a CLSC networkdesign problem that only considered of fuzzy variables. Inrecent years, uncertainty has attracted more research atten-tion [17โ19]. Stochastic programing, robust optimization, andfuzzy set theory have been used to present uncertainty inFLAPs [20, 21]. Wang et al. [22] used prediction sets to solvean expansion planning problem for waste-to-energy (WtE)systems facing future waste supply uncertainty. Keyvan-shokooh et al. [23] proposed a novel hybrid robust-stochasticprograming (HRSP) approach to simultaneously model twodifferent types of uncertainties by using stochastic scenariosfor the transportation costs and polyhedral uncertainty setsfor the demand and returns. However, the DC and the CCwere separate and the collection disposal rate was a certainvariable.
Uncertainties exist in both forward and reverse supplychains. However, the uncertainties in the reverse flow arehigher than in the forward supply chain [7, 24, 25] asreturned product quantity is generally seen as uncertain [23,26]. Subjective uncertainties such as the decision makerโschoices and environmental coefficients can be dealt withusing fuzziness, while objective uncertainties such as unittransportation costs, product prices, and the quantity ofunusable products can be dealt with using randomness. Inthis paper, to reflect the study problem, the return rate anddisposal rate are considered fuzzy random variables, whichare concurrently handled using triangular fuzzy numbers [7].Based on the above, a model is formulated to determinethe optimum CDC number and location and the allocationstrategies for the different facility types.
As facilities location and allocation problems are seenas nonconvex, nondifferentiable, strongly NP-hard problems,a collection and distribution center location and allocationproblem (CDCLAP) in a closed-loop supply chain under
a fuzzy random environment is even more complicated. Sev-eral differentmethods have been used to solve NP-hard prob-lems [27โ29]. While particle swarm optimization (PSO) hasbeen found to be generally effective [30โ32], when the localoptimal solution is found, the particle behavior in a basic PSOis directly influenced and therefore frequently falls into a localoptimum [33โ35]. Because of this problem, several advancedPSOs have been developed to more accurately solve supplychain management problems. Ai and Kachitvichyanukul [36]proposed a global-local-neighbor PSO which was found tobe more effective. Based on this innovation, Xu et al. [33]proposed a fuzzy random simulation-based bilevel global-local-neighbor particle swarm optimization (frs-bglnPSO).In this paper, a priority-based global-local-neighbor particleswarm optimization (pb-glnPSO) is applied to solve theCDCLAP.
In summary, this paper proposes a mathematical modelto solve a collection-distribution center location and allo-cation problem in a closed-loop supply chain that con-siders economic and environmental factors and includesfuzzy random variables for return and disposal rates. Theremainder of this paper is organized as follows. Section 2presents the problem statement and model assumptions,after which a description of the model and its formula-tions is given in Section 3. The proposed hybrid solutionbased on the developed pb-glnPSO is described in Sec-tion 4 and case study is presented in Section 5 to illustratemodel formulation and the proposed method. Finally, Sec-tion 6 gives conclusions and indications for future researchextensions.
2. Research Problem Statement
In this paper, a company with factories in certain locationsand retailers in different customer zones is considered. Thecompany needs to decide the locations for their integratedcollection and distribution centers (CDCs), at which botha used product collection network and a new productdistribution network are to be jointly [12]. As CDCs reduceconstruction and transportation costs because the samevehicles are used for both distribution and recycling, in thispaper, only CDCs are considered.
A general illustration of the classical CDCLAP for aclosed-loop supply chain is shown in Figure 1, with the CLSCframework shown in Loop 1. The CLSC framework has fourechelons: factories, CDCs, retailers, and disposal centers [11].The forward supply chain begins with new production, afterwhich the finished products are transported from the facto-ries to the retailers via the CDCs. In the reverse supply chain,returned products are collected and transported to the CDCs,where the recycled products are inspected, consolidated, andsorted into those that are available for remanufacturing,which are sent to the factories, and those that are unsuitablefor remanufacturing, which are transported to the disposalcenters [23]. A CDC supplies products to multiple retailers;however, retailer demand is fulfilled by only one productionsite. A CDC can also handle products from different factoriesand dispatch returned products to multiple factories forremanufacturing.
Mathematical Problems in Engineering 3
Factory 1
Factory 2
Factory 3
Factory 4
Collection-distribution center 1
Disposal center
Collection-distribution center 2
Retailer 1
Retailer 2
Retailer 3
Retailer 4
Loop 1
Figure 1: The closed-loop supply chain network.
In the CLSC examined in this paper, the retailersโdemand is estimated based on preorders. However, thereturn rate is considered to be a fuzzy random variable ascustomers may not return the used product or the productmay be broken. Consequently, the availability of recycledproducts is unsure because of unsure transportation andcarrying losses. Another fuzzy random variable consideredin this paper is the returned product disposal rate, whichis dependent on the inspection and consolidation at theCDC.
The assumptions for the proposed problem investigationare as follows: (1) only one product in one period is consid-ered; (2) all alternative CDC locations have been identified;(3) recycling a used product costs less than manufacturing anew one; (4) the CDCs and factories have capacity limits [37โ39]. As considering incapacitated facilities is an unrealisticassumption in many LAPs, many researchers have assigneda maximum capacity level to facilities to model more realisticdecisions; (5) the factoriesโ, retailersโ, and disposal centersโlocations are known; (6) new product and returned productstorage are allowed at the CDCs [21].
The initial problem is decidingwhichCDCs to select fromthe candidate sites and which allocation strategies to selectto minimize total CDC costs: operating costs, transportationcosts, and transportation pollution costs, while also consid-ering flow constraints, capacity limits, and retailer demand.
3. Modelling
In this section, the mathematical formulations are given forthe CDCLAP in the CLSC and the notations are given in theNotations to facilitate the problem description.
3.1. Objective Functions. Based on the variables mentionedin the Notations, the objectives are to minimize total costs
and to minimize the environmental effects with the primaryobjective being minimizing total cost.
3.1.1. Economic Objective. In general, decisionmakers seek tominimize total costs, which are made up of transportationcosts, fixed costs, and operating costs. The minimizationobjective can be described as
min ๐น1
=๐ผ
โ๐=1
๐ฝ
โ๐=1
๐ถ๐๐๐๐๐๐ +๐ผ
โ๐=1
๐พ
โ๐=1
๐ถ๐๐๐๐๐๐ (1 + ๏ฟฝ๏ฟฝ๐)
+๐ผ
โ๐=1
๐
โ๐=1
๐พ
โ๐=1
๐ถ๐ค๐๐๐๐๏ฟฝ๏ฟฝ๐๐๐๐
+๐ผ
โ๐=1
๐ฝ
โ๐=1
๐พ
โ๐=1
๐
โ๐=1
๐ถ๐๐๐๏ฟฝ๏ฟฝ๐๐๐๐ (1 โ ๏ฟฝ๏ฟฝ๐) +๐ผ
โ๐=1
๐น๐๐๐๐
+๐ผ
โ๐=1
๐ฝ
โ๐=1
๐๐๐ ๐๐๐ +๐ผ
โ๐=1
๐พ
โ๐=1
RV๐๐ ๏ฟฝ๏ฟฝ๐.
(1)
Equation (1) calculates the total cost, in whichโ๐ผ๐=1โ๐ฝ๐=1 ๐ถ๐๐๐๐๐๐ is the new product transport costs fromthe factories to the CDC, โ๐ผ๐=1โ๐พ๐=1 ๐ถ๐๐๐๐๐๐(1 + ๏ฟฝ๏ฟฝ๐) is thetransport costs between the CDCs and the retailers, andโ๐ผ๐=1โ๐๐=1โ๐พ๐=1 ๐ถ๐ค๐๐๐๐๏ฟฝ๏ฟฝ๐๐๐๐ is the returned product deliverycosts from the CDCs to the disposal centers. The returnedproduct transportation costs from the CDCs to disposalcenters are measured asโ๐ผ๐=1โ๐๐=1โ๐พ๐=1โ๐๐=1 ๐ถ๐๐๐๏ฟฝ๏ฟฝ๐๐๐๐(1โ ๏ฟฝ๏ฟฝ๐).The fixed costs for opening a new CDC are โ๐ผ๐=1 ๐น๐๐๐๐.โ๐ผ๐=1โ๐ฝ๐=1 ๐๐๐ ๐๐๐ denotes the new product variable costs.
4 Mathematical Problems in Engineering
โ๐ผ๐=1โ๐พ๐=1 RV๐๐ ๏ฟฝ๏ฟฝ๐ calculates the returned product operatingcosts.
As it is very difficult to handle objective functions withfuzzy random factors, Kruse and Meyer [40] demonstratedthat the fuzzy expected value could be represented by a singlefuzzy number. Based on the theory proposed by Heilpern[41], without a loss of generality, the expected value operatoris used to convert the uncertain model into a deterministicmodel, which can then be used to transform the fuzzyrandom objective functions into their crisp equivalences, asshown in
min ๐น1
=๐ผ
โ๐=1
๐ฝ
โ๐=1
๐ถ๐๐๐๐๐๐ +๐ผ
โ๐=1
๐พ
โ๐=1
๐ถ๐๐๐๐๐๐ (1 + EV [๏ฟฝ๏ฟฝ๐])
+๐ผ
โ๐=1
๐
โ๐=1
๐พ
โ๐=1
๐ถ๐ค๐๐EV [๏ฟฝ๏ฟฝ๐]EV [๏ฟฝ๏ฟฝ๐]๐๐๐
+๐ผ
โ๐=1
๐ฝ
โ๐=1
๐พ
โ๐=1
๐
โ๐=1
๐ถ๐๐๐EV [๏ฟฝ๏ฟฝ๐]๐๐๐ (1 โ EV [๏ฟฝ๏ฟฝ๐])
+๐ผ
โ๐=1
๐น๐๐๐๐ +๐ผ
โ๐=1
๐ฝ
โ๐=1
๐๐๐ ๐๐๐ +๐ผ
โ๐=1
๐พ
โ๐=1
RV๐๐EV [๏ฟฝ๏ฟฝ๐] .
(2)
Note that EV[๏ฟฝ๏ฟฝ๐] or EV[๏ฟฝ๏ฟฝ๐] above represents two expectedvalues: the first is used to convert the fuzzy random variablesinto fuzzy numbers based on Kruse and Meyerโs 1987 theory,and the second is used to transform the fuzzy numbers intodeterministic numbers based on Heilpernโs 1992 theory.
3.1.2. Environmental Objective. The secondary objective isto minimize the transportation carbon emissions associatedwith the CLSC operations, an area that has attracted signifi-cant recent research attention [42]. The following expressionrepresents the transportation carbon emissions between theCDCs and the factories, the CDCs and the retailers, and theCDCs and the disposal centers.
min ๐น2
=๐ผ
โ๐=1
๐ฝ
โ๐=1
๐ฝ๐๐๐๐๐ +๐ผ
โ๐=1
๐พ
โ๐=1
๐ฝ๐๐๐๐๐ (1 + EV [๏ฟฝ๏ฟฝ๐])
+๐ผ
โ๐=1
๐
โ๐=1
๐พ
โ๐=1
๐ฝ๐๐EV [๏ฟฝ๏ฟฝ๐]EV [๏ฟฝ๏ฟฝ๐]๐๐๐
+๐ผ
โ๐=1
๐ฝ
โ๐=1
๐พ
โ๐=1
๐
โ๐=1
๐ฝ๐๐EV [๏ฟฝ๏ฟฝ๐]๐๐๐ (1 โ EV [๏ฟฝ๏ฟฝ๐]) .
(3)
โ๐ผ๐=1โ๐ฝ๐=1 ๐ฝ๐๐๐๐๐ is the environmental pollution causedby the transportation activities from the factories to theCDCs. โ๐ผ๐=1โ๐พ๐=1 ๐ฝ๐๐๐๐๐(1 + EV[๏ฟฝ๏ฟฝ๐]) is the summation ofthe carbon footprints for transporting products between theCDCs and retailers. โ๐ผ๐=1โ๐๐=1โ๐พ๐=1 ๐ฝ๐๐EV[๏ฟฝ๏ฟฝ๐]EV[๏ฟฝ๏ฟฝ๐]๐๐๐ is
the total carbon footprint from the CDCs to the disposalcenters, andโ๐ผ๐=1โ๐๐=1โ๐พ๐=1โ๐๐=1 ๐ฝ๐๐EV[๏ฟฝ๏ฟฝ๐]๐๐๐(1 โ EV[๏ฟฝ๏ฟฝ๐]) isthe carbon footprint from the CDCs to factories for returnedproducts.
3.2. Constraints. Note that as each CDC has its own capacitylimit, it is unable to service goods beyond capacity; therefore,the capacity limit restriction can be written as follows:
๐พ
โ๐=1
EV [๏ฟฝ๏ฟฝ๐]๐๐๐ +๐ฝ
โ๐=1
๐๐๐ โค ๐ผ๐ โ๐ โ ฮฉ. (4)
๏ฟฝ๏ฟฝ๐๐ is a fuzzy random variable indicating the used productreturn rate transported from the retailer ๐ to CDC ๐. ๐๐๐indicates the product quantity from CDC ๐ to retailer ๐. ๐๐๐indicates the product quantity transported from factory ๐ toCDC ๐. ๐ผ๐ is the capacity of CDC ๐.
Within the capacity constraint, the factory is able tomanufacture new products to meet retailer needs as well asdeal with the returned products from the CDCs.
๐พ
โ๐=1
๐ท๐ +๐ผ
โ๐=1
๐พ
โ๐=1
๐
โ๐=1
๐ถ๐๐๐EV [๏ฟฝ๏ฟฝ๐]๐๐๐ (1 โ EV [๏ฟฝ๏ฟฝ๐])
โค๐ฝ
โ๐=1
๐พ๐.(5)
๐ท๐ is the demand from retailer ๐ and ๐พ๐ is the capacity of
factory ๐.โ๐ผ๐=1โ๐พ๐=1โ๐๐=1 ๐ถ๐๐๐EV[๏ฟฝ๏ฟฝ๐]๐๐๐(1โEV[๏ฟฝ๏ฟฝ๐]) calculatesthe returned product quantity transported to factory ๐ forremanufacturing.
All products for the retailers are processed through theCDCs. The recycled product quantity is always less than theproduct quantity transported from the factory to the CDC,which can be described as follows:
๐ฝ
โ๐=1
๐๐๐ โฅ๐พ
โ๐=1
EV [๏ฟฝ๏ฟฝ๐]๐๐๐. (6)
๐๐๐ is a variable indicating the new product quantity trans-ported from factory ๐ to CDC ๐. ๏ฟฝ๏ฟฝ๐๐๐๐ refers to the returnedproduct quantity transported from retailer ๐ to CDC ๐.
The products provided to the retailers should meet theirdemand.
๐ผ
โ๐=1
๐๐๐ โฅ๐พ
โ๐=1
๐ท๐. (7)
๐๐๐ is the product quantity CDC ๐ sends to retailer ๐. Thestochastic variable ๐ท๐ is the retailer ๐โs demand based onorder quantity.
The returned product quantity transported to the CDCsis more than the product quantity transported to disposalcenters.
๐พ
โ๐=1
EV [๏ฟฝ๏ฟฝ๐]๐๐๐ โฅ๐
โ๐=1
๐พ
โ๐=1
EV [๏ฟฝ๏ฟฝ๐]EV [๏ฟฝ๏ฟฝ๐]๐๐๐. (8)
Mathematical Problems in Engineering 5
EV[๏ฟฝ๏ฟฝ๐]๐๐๐ is the expression for the returned product quantityfrom retailer ๐ to CDC ๐, and EV[๏ฟฝ๏ฟฝ๐]EV[๏ฟฝ๏ฟฝ๐]๐๐๐ is the productquantity transported from CDC ๐ to retailer ๐.
There should be at least one CDC and there should be nomore CDCs than the specified upper limit.
1 โค๐ผ
โ๐=1
๐ฅ๐, (9)
๐ผ
โ๐=1
๐ฅ๐ โค ๐. (10)
๐ is the upper limit for the number of CDCs, which isdependent on demand, returned product quantity, and fixedcapacity constraints.
Each retailer must be serviced by only one CDC.
๐ผ
โ๐=1
๐ฆ๐๐ = 1. (11)
Since ๐ฅ๐ and ๐ฆ๐๐ are binary variables, the followingconstraints are required:
๐ฅ๐ = {0, 1} , โ๐ โ ฮฉ,๐ฆ๐๐ = {0, 1} , โ๐ โ ฮฉ, โ๐ โ ฮฆ. (12)
๐ฅ๐ is a binary variable indicatingwhether aCDC is openedat point ๐. If location ๐ is chosen to open a CDC, then ๐ฅ๐;otherwise, ๐ฅ๐ = 0. ๐ฆ๐๐ is a binary variable indicating whetherretailer ๐ is serviced by CDC ๐. If ๐ฆ๐๐ = 1, then retailer ๐ isserviced by CDC ๐; otherwise, ๐ฆ๐๐ = 0.
3.3. Global Model. From the formulation above, the modelfor the CDCLAPwith capacity, flow, and quantity constraintsis developed with the aims of minimizing total costs and totaltransportation pollution with the primary objective of min-imizing the total cost. In the CLSC, both new and returnedproducts are considered. The product can be reproduced tosave raw materials and reduce waste and pollution. In ourmodel, all costs involved in the CDCLAP are considered aswell as the influence of the transportation activity pollution.Fuzzy random theory is used to deal with the real worldcomplex uncertainties and ensure more scientific decisions.Therefore, this CDC situation is closer to the real situation asit can deal with complicated practical problems. Finally, theglobal model is given:
min ๐น1
=๐ผ
โ๐=1
๐ฝ
โ๐=1
๐ถ๐๐๐๐๐๐ +๐ผ
โ๐=1
๐พ
โ๐=1
๐ถ๐๐๐๐๐๐ (1 + EV [๏ฟฝ๏ฟฝ๐])
+๐ผ
โ๐=1
๐
โ๐=1
๐พ
โ๐=1
๐ถ๐ค๐๐EV [๏ฟฝ๏ฟฝ๐]EV [๏ฟฝ๏ฟฝ๐]๐๐๐
+๐ผ
โ๐=1
๐ฝ
โ๐=1
๐พ
โ๐=1
๐
โ๐=1
๐ถ๐๐๐EV [๏ฟฝ๏ฟฝ๐]๐๐๐ (1 โ EV [๏ฟฝ๏ฟฝ๐])
+๐ผ
โ๐=1
๐น๐๐๐๐ +๐ผ
โ๐=1
๐ฝ
โ๐=1
๐๐๐ ๐๐๐ +๐ผ
โ๐=1
๐พ
โ๐=1
RV๐๐EV [๏ฟฝ๏ฟฝ๐]
min ๐น2
=๐ผ
โ๐=1
๐ฝ
โ๐=1
๐ฝ๐๐๐๐๐ +๐ผ
โ๐=1
๐พ
โ๐=1
๐ฝ๐๐๐๐๐ (1 + EV [๏ฟฝ๏ฟฝ๐])
+๐ผ
โ๐=1
๐
โ๐=1
๐พ
โ๐=1
๐ฝ๐๐EV [๏ฟฝ๏ฟฝ๐]EV [๏ฟฝ๏ฟฝ๐]๐๐๐
+๐ผ
โ๐=1
๐ฝ
โ๐=1
๐พ
โ๐=1
๐
โ๐=1
๐ฝ๐๐EV [๏ฟฝ๏ฟฝ๐]๐๐๐ (1 โ EV [๏ฟฝ๏ฟฝ๐])
s.t.๐พ
โ๐=1
EV [๏ฟฝ๏ฟฝ๐]๐๐๐ +๐ฝ
โ๐=1
๐๐๐ โค ๐ผ๐๐ฅ๐
โ๐ โ ฮฉ โ๐ โ ฮจ โ๐ โ ฮฆ๐พ
โ๐=1
๐ท๐ +๐ผ
โ๐=1
๐พ
โ๐=1
๐
โ๐=1
๐ถ๐๐๐EV [๏ฟฝ๏ฟฝ๐]๐๐๐ (1 โ EV [๏ฟฝ๏ฟฝ๐])
โค๐ฝ
โ๐=1
๐พ๐ โ๐ โ ฮจ โ๐ โ ฮฆ โ๐ โ ฮฅ
๐ฝ
โ๐=1
๐๐๐ โฅ๐พ
โ๐=1
EV [๏ฟฝ๏ฟฝ๐] โ๐ โ ฮฉ โ๐ โ ฮจ โ๐ โ ฮฆ
๐พ
โ๐=1
EV [๏ฟฝ๏ฟฝ๐]๐๐๐ โฅ๐
โ๐=1
๐พ
โ๐=1
EV [๏ฟฝ๏ฟฝ๐]EV [๏ฟฝ๏ฟฝ๐]๐๐๐
โ๐ โ ฮฉ โ๐ โ ฮฆ โ๐ โ ฮฅ๐ผ
โ๐=1
๐๐๐ โฅ๐พ
โ๐=1
๐ท๐ โ๐ โ ฮฉ โ๐ โ ฮฆ
1 โค๐ผ
โ๐=1
๐ฅ๐ โ๐ โ ฮฉ
๐ผ
โ๐=1
๐ฅ๐ โค ๐ โ๐ โ ฮฉ
๐ฆ๐๐ โค ๐ฅ๐ โ๐ โ ฮฉ, โ๐ โ ฮฆ๐ผ
โ๐=1
๐ฆ๐๐ = 1 โ๐ โ ฮฉ โ๐ โ ฮฆ
๐ฅ๐ = {0, 1} โ๐ โ ฮฉ๐ฆ๐๐ = {0, 1} โ๐ โ ฮฉ, โ๐ โ ฮฆ.
(13)
3.4.Model Transformation. The two objectives are believed tobe some similar ones to some extent.Theminimization of theenvironmental objective requires low transportation carbonemissions which could be similar to the transportation costs
6 Mathematical Problems in Engineering
of the economic objective, which are mainly affected bytransportation distance. Based on previous research [33, 43,44], the secondary environmental impact reduction objectivecan be transformed into a constraint to reduce the complexityof themultiple-objectivemodel (13). By the acceptable carbonemissions level, the decision makers are concerned muchmore about the total cost, namely, the primary objective.Suppose that there is a maximum average environmentalimpact level ๐ which is acceptable to the decision maker;therefore, the secondary objective can be transformed to bea constraint as follows:
๐น2 =๐ผ
โ๐=1
๐ฝ
โ๐=1
๐ฝ๐๐๐๐๐ +๐ผ
โ๐=1
๐พ
โ๐=1
๐ฝ๐๐๐๐๐ (1 + EV [๏ฟฝ๏ฟฝ๐])
+๐ผ
โ๐=1
๐
โ๐=1
๐พ
โ๐=1
๐ฝ๐๐EV [๏ฟฝ๏ฟฝ๐]EV [๏ฟฝ๏ฟฝ๐]๐๐๐
+๐ผ
โ๐=1
๐ฝ
โ๐=1
๐พ
โ๐=1
๐
โ๐=1
๐ฝ๐๐EV [๏ฟฝ๏ฟฝ๐]๐๐๐ (1 โ EV [๏ฟฝ๏ฟฝ๐])
โค ๐.
(14)
Namely,
๐น2 โค ๐. (15)
As the economic costs for the primary objective andthe secondary environmental objective are transformed intoa constraint, the global model can be transformed into itscorresponding equivalent model as follows:
min ๐น1
=๐ผ
โ๐=1
๐ฝ
โ๐=1
๐ถ๐๐๐๐๐๐ +๐ผ
โ๐=1
๐พ
โ๐=1
๐ถ๐๐๐๐๐๐ (1 + EV [๏ฟฝ๏ฟฝ๐])
+๐ผ
โ๐=1
๐
โ๐=1
๐พ
โ๐=1
๐ถ๐ค๐๐EV [๏ฟฝ๏ฟฝ๐]EV [๏ฟฝ๏ฟฝ๐]๐๐๐
+๐ผ
โ๐=1
๐ฝ
โ๐=1
๐พ
โ๐=1
๐
โ๐=1
๐ถ๐๐๐EV [๏ฟฝ๏ฟฝ๐]๐๐๐ (1 โ EV [๏ฟฝ๏ฟฝ๐])
+๐ผ
โ๐=1
๐น๐๐๐๐ +๐ผ
โ๐=1
๐ฝ
โ๐=1
๐๐๐ ๐๐๐ +๐ผ
โ๐=1
๐พ
โ๐=1
RV๐๐EV [๏ฟฝ๏ฟฝ๐]
s.t. ๐น2 โค ๐๐พ
โ๐=1
EV [๏ฟฝ๏ฟฝ๐]๐๐๐ +๐ฝ
โ๐=1
๐๐๐ โค ๐ผ๐๐ฅ๐
โ๐ โ ฮฉ โ๐ โ ฮจ โ๐ โ ฮฆ๐พ
โ๐=1
๐ท๐ +๐ผ
โ๐=1
๐พ
โ๐=1
๐
โ๐=1
๐ถ๐๐๐EV [๏ฟฝ๏ฟฝ๐]๐๐๐ (1 โ EV [๏ฟฝ๏ฟฝ๐])
โค๐ฝ
โ๐=1
๐พ๐ โ๐ โ ฮจ โ๐ โ ฮฆ โ๐ โ ฮฅ
๐ฝ
โ๐=1
๐๐๐ โฅ๐พ
โ๐=1
EV [๐๐] โ๐ โ ฮฉ โ๐ โ ฮจ โ๐ โ ฮฆ
๐พ
โ๐=1
EV [๏ฟฝ๏ฟฝ๐]๐๐๐ โฅ๐
โ๐=1
๐พ
โ๐=1
EV [๏ฟฝ๏ฟฝ๐]EV [๏ฟฝ๏ฟฝ๐]๐๐๐
โ๐ โ ฮฉ โ๐ โ ฮฆ โ๐ โ ฮฅ๐ผ
โ๐=1
๐๐๐ โฅ๐พ
โ๐=1
๐ท๐ โ๐ โ ฮฉ โ๐ โ ฮฆ
1 โค๐ผ
โ๐=1
๐ฅ๐ โ๐ โ ฮฉ
๐ผ
โ๐=1
๐ฅ๐ โค ๐ โ๐ โ ฮฉ
๐ฆ๐๐ โค ๐ฅ๐ โ๐ โ ฮฉ, โ๐ โ ฮฆ๐ผ
โ๐=1
๐ฆ๐๐ = 1 โ๐ โ ฮฉ โ๐ โ ฮฆ
๐ฅ๐ = {0, 1} โ๐ โ ฮฉ๐ฆ๐๐ = {0, 1} โ๐ โ ฮฉ, โ๐ โ ฮฆ.
(16)
4. The Heuristic AlgorithmsBased on Pgln-PSO
Particle swarm optimization (PSO) is an evolutionary algo-rithm which simulates social behavior such as birds flockingand fish schooling [45]. Using a fixed population of individ-uals, the PSO searches the feasible zone to seek solutions,which are then updated to achieve an optimal solution. Theparticles [46], characterized by their position and velocity,are decided on by their flying experience, their discoveries,or the discoveries of their companions. They fly through theproblem spaces following the currently optimum particlesto find the best solution between the populations and thebest solution for each population. Even though the PSOhas been widely used to solve NP-hard problems [45, 47],in the basic PSO, as the particles in the swarm are weak,they tend to cluster rapidly toward the global best particle[31]. However, the global-local-neighbor particle swarm opti-mization (glnPSO) developed by Ai and Kachitvichyanukul[36] has been found to improve the weakness in the basicPSO. Xu et al. [43] proposed an even more advanced global-local-neighbor particle swarm optimization with exchange-able particles (GLNPSO-ep). In this section, a priority-based global-local-neighbor particle swarm optimization(pb-glnPSO) is proposed to solve the CDCLAP in theCLSC.
4.1. Notations for the pb-glnPSO. The basic elements ofthe PSO are particles, population, velocity, inertia weight,individual best, and global best. The notations needed for thepb-glnPSO are shown in the Notations.
Mathematical Problems in Engineering 7
4.2. Encoding andDecoding Algorithm. Thedecoding processis based on the priority-based encoding developed by Genand Cheng and the priority-based decoding and encodingproposed by Gen et al. [48]. The priorities for the CDCsand the retailers are equal to the total number of retailersand CDCs. At each step, the CDC (retailer) with the highestpriority is selected and connected to a retailer (CDC) under aminimum transportation cost constraint. Procedure 1 showsthe decoding algorithm for the priority-based encodingand its trace table, with the priority-based encoding beingrandom. The CDCLAP is solved in two stages [14]. In thefirst stage, the CDC location is chosen and the transportationbetween the CDCs and retailers calculated. In the secondstage, the allocations between the factories and CDCs aredealt with.
4.3. The Fitness Value Function. The CDCLAP consideredin this paper has a primary objective, the minimizationof total costs, and a secondary objective, the minimizationof environmental pollution, with the fitness value of eachparticle reflecting the objective value of the total cost. Asthe second objective has been transformed into a constraint,when the transportation carbon emissions are beyond theacceptable level [33, 43], a penalty function is assignedaccording to the actual situation. If the transportation carbonemissions are within the acceptable level ๐น2 โค ๐, the fitnessvalue function in the pb-glnPSO is as follows:
Fitness = min๐ผ
โ๐=1
๐ฝ
โ๐=1
๐ถ๐๐๐๐๐๐ +๐ผ
โ๐=1
๐พ
โ๐=1
๐ถ๐๐๐๐๐๐ (1 + EV [๏ฟฝ๏ฟฝ๐])
+๐ผ
โ๐=1
๐
โ๐=1
๐พ
โ๐=1
๐ถ๐ค๐๐EV [๏ฟฝ๏ฟฝ๐]EV [๏ฟฝ๏ฟฝ๐]๐๐๐
+๐ผ
โ๐=1
๐ฝ
โ๐=1
๐พ
โ๐=1
๐
โ๐=1
๐ถ๐๐๐EV [๏ฟฝ๏ฟฝ๐]๐๐๐ (1 โ EV [๏ฟฝ๏ฟฝ๐])
+๐ผ
โ๐=1
๐น๐๐๐๐ +๐ผ
โ๐=1
๐ฝ
โ๐=1
๐๐๐ ๐๐๐ +๐ผ
โ๐=1
๐พ
โ๐=1
RV๐๐EV [๏ฟฝ๏ฟฝ๐] .
(17)
If the transportation carbon emissions are beyond theacceptable level, a penalty function is assigned and the fitnessfunction is
Fitness = min๐ผ
โ๐=1
๐ฝ
โ๐=1
๐ถ๐๐๐๐๐๐ +๐ผ
โ๐=1
๐พ
โ๐=1
๐ถ๐๐๐๐๐๐ (1 + EV [๏ฟฝ๏ฟฝ๐])
+๐ผ
โ๐=1
๐
โ๐=1
๐พ
โ๐=1
๐ถ๐ค๐๐EV [๏ฟฝ๏ฟฝ๐]EV [๏ฟฝ๏ฟฝ๐]๐๐๐
+๐ผ
โ๐=1
๐ฝ
โ๐=1
๐พ
โ๐=1
๐
โ๐=1
๐ถ๐๐๐EV [๏ฟฝ๏ฟฝ๐]๐๐๐ (1 โ EV [๏ฟฝ๏ฟฝ๐])
+๐ผ
โ๐=1
๐น๐๐๐๐ +๐ผ
โ๐=1
๐ฝ
โ๐=1
๐๐๐ ๐๐๐ +๐ผ
โ๐=1
๐พ
โ๐=1
RV๐๐EV [๏ฟฝ๏ฟฝ๐]
+ ๐ถ (๐ โ ๐น2)
(18)
in which ๐ถ is a large enough penalty factor.
4.4. Update. Based on the notations of pb-glnPSO men-tioned in the Notations and the glnPSO proposed by Aiand Kachitvichyanukul [36], the inertia weight, velocity, andposition are updated using the following equation:
๐ (๐) = ๐ (๐) + ๐ โ ๐1 โ ๐ [๐ (1) โ ๐ (๐)] , (19)
V๐๐ (๐ + 1) = ๐ (๐) V๐๐ (๐) + ๐๐๐1 [๐best๐๐ (๐) โ ๐๐๐ (๐)]
+ ๐๐๐2 [๐best๐๐ (๐) โ ๐๐๐ (๐)]
+ ๐๐๐3 [๐best๐๐ (๐) โ ๐๐๐ (๐)]
+ ๐๐๐4 [๐best๐๐ (๐) โ ๐๐๐ (๐)] ,
(20)
๐๐๐ (๐ + 1) = ๐๐๐ (๐) + V๐๐ (๐ + 1) . (21)
TheglnPSOhas beenwidely used in solvingNP-hard facilitieslocation and allocation problems.
4.5. Overall Process of the pb-glnPSO. In this paper, theglnPSO presented in Procedure 1 is used to solve a locationand allocation problem. Due to uncertainties and envi-ronmental changes, a priority-based global-local-neighborparticle swarm optimization (pb-glnPSO) is proposed tosolve this model. As the company pays close attention toeconomic costs, the environmental factor is dealt with as aconstraint that has upper limits. The algorithmic details areas follows.
Step 1. Initialize ๐ particles as a swarm: ๐ = 1, 2, . . . , ๐ฟ (theparticle is the priority).
Step 2 (constraints check). If in the feasible region, go toStep 3; otherwise, return to Step 1.
Step 3. Calculate the fitness according to the decoding algo-rithm in Procedure 1.
Step 4. Update the particle positions and velocities.
Step 4.1. Acquire the expected value for ๐ from the abovealgorithm.
Step 4.2. For ๐ = 1, 2, . . . , ๐ฟ, decode each particle to aninstallment group. Calculate the fitness value of each particleand set the position of the ๐th particle as its personal best.The global best position is chosen from these personal bestpositions.
Step 4.3 (update ๐best). For ๐ = 1, 2, . . . , ๐ฟ, if Fitness(๐๐) <Fitness(๐best
๐ ), ๐best๐ = ๐๐.
Step 4.4 (update ๐best). For ๐ = 1, 2, . . . , ๐ฟ, if Fitness(๐๐) <Fitness(๐best
๐ ), ๐best๐ = ๐๐.
Step 4.5 (update ๐best). For ๐ = 1, 2, . . . , ๐ฟ, among all ๐bestof๐ neighbors around the ๐th particle, set the personal bestwhich has the best fitness value as ๐๐ฟbest๐ .
8 Mathematical Problems in Engineering
Input. ฮฉ: set of CDCs, ๐ โ ฮฉ = {1, 2, 3, . . . , ๐ผ}; ฮฆ: set of retailers, and ๐ โ ฮฆ = {1, 2, 3, . . . , ๐พ}๐ท๐: demand of retailer ๐, ๐ โ ฮฆ,๐ผ๐: the capability of the CDCs ๐, ๐ โ ฮฉ; EV[๏ฟฝ๏ฟฝ๐]: the return rate of retailer ๐, ๐ โ ฮฆ๐ถ๐๐๐: unit transportation cost between the CDC ๐ and retailer ๐, ๐ โ ฮฉ, ๐ โ ฮฆ;๐(๐ + ๐): the priority settled, ๐ โ ฮฉ = {1, 2, 3, . . . , ๐ผ}, ๐ โ ฮฆ = {1, 2, 3, . . . , ๐พ}
Output. ๐๐๐: the product quantity transported from CDC ๐ to the retailer ๐.๐๐๐: the product quantity transported from the retailer ๐ to CDC ๐.
Step 1. ๐๐๐ โ 0, ๐ โ ฮฉ, ๐ โ ฮฆ, ๐๐๐ โ 0, ๐ โ ฮฉ, ๐ โ ฮฆ,Step 2. ๐ก โ argmax๐(๐), ๐ โ |ฮฉ| + |ฮฆ|; select a nodeStep 3. If ๐ก โ ฮฉ, then ๐โ โ ๐ก; select a CDC,
๐โ โ argmin๐ถ๐๐๐ | ๐(๐) = 0, ๐ โ ฮฆ; select a retailer with the lowest costelse, ๐โ๐; select a retailer๐โ โ argmin๐ถ๐๐๐ | ๐(๐) = 0, ๐ โ ฮฆ; select a CDC with the lowest cost
Step 4. ๐๐๐ โ min๐ท๐(1 + EV[๏ฟฝ๏ฟฝ๐]), ๐ผ๐: assign the available amount of unitsUpdate the availabilities on CDC (๐โ) and retailer (๐โ)๐ท๐โ = ๐ท๐โ โ ๐๐โ๐โ , ๐ผ๐ = ๐ผ๐ โ ๐๐โ๐โ
Step 5. If๐ท๐โ = 0 then ๐๐โ = 0If ๐ผ๐โ = 0 then ๐๐โ = 0
Step 6. If ๐(|๐| + ๐) = 0, ๐ โ ฮฆ, then calculate transportation cost, find the chosen CDC and return, else go to Step 1.
Procedure 1: Decoding of the priority for the location and allocation problem.
Step 4.6 (generate ๐best). For ๐ = 1, 2, . . . , ๐ฟ and ๐ =1, 2, . . . , ๐ท, find ๐o๐ ensuring that the FDR takes a maximumvalue, and set ๐๐๐ as ๐๐ฟbest๐ .
Step 4.7. Update the position and the velocity of each ๐thparticle using (20) and (21).
Step 4.8. Check whether the particles are beyond the mark.If ๐๐๐ > ๐max, ๐๐๐ = ๐max; otherwise, if ๐๐๐ < ๐min, then๐๐๐ = ๐min.
Step 5. Based on the above calculation, replace the rankingvector using the new numbers.
Step 6. If the stopping criterion is met, stop; otherwise, ๐ =๐ + 1 and return to Step 2.
The overall process can be clearly seen in Figure 2.
5. Case Study
5.1. Case Presentation. This model is based on a beer com-pany in a developing country that bottles beer in glass bottles.The supply chain allows customers to return empty bottlesto the retailers, which are then sent to the CDCs wherethey are inspected, consolidated, and sorted. After processingand disinfecting, the bottles are refilled and sold again. Thecompany is now considering the construction of severalCDCs to allow for bottle recycling as producing new bottlesis far more expensive than recycling used bottles.
To illustrate the validity of the model and the usefulnessof the solutionmethod, the data needed to examine the CLSCperformance for the four echelons is presented here. Basedon the market analysis, ten alternative CDC coordinates aresuggested, which are to be assessed based on location, capac-ity, fixed costs, new product variable costs (NPV cost), and
recycled product variable costs (RPV cost). The assessmentsfor the ten possible CDCs are shown in Table 1. Supermarketsand restaurants are considered to be the beer retailers withflexible demand. Table 2 presents the information regardingthe retailers, factories, and disposal centers. It can be seenfrom Table 2 that ๐1 to ๐30 represent 30 different retailers, ๐1to ๐4 are the 4 different factories in different locations, each ofwhich has a different capacity, and ๐1 indicates the locationand capacity of the disposal center. Therefore, 30 retailers,4 factories, and 1 waste disposal center are considered inthis study. The unit transportation costs and pollution arerelated to the distances between the facilities. The retailersโreturn rates, which are fuzzy random variables, are shown inTable 3.
5.2. Maximum Generation and Population Size. With anincrease in the maximum iterations, computer time mayincrease or the optimal result may improve. For the pb-glnPSO in this case study, the maximum generation is set at๐ and the population size is set at๐; therefore, the maximumiteration is ๐ ร ๐. In the test, ๐ is set from 20 to 60 with astep-length of 10, and ๐ is set from 100 to 500 with a step-length of 100, while ๐๐ = ๐๐ = ๐๐ = ๐๐ = 2, from which 25different groups are obtained, and then pb-glnPSO was run20 times for each group.
Figures 3 and 4 show the test results, the computing time,and the optimal results. For the horizontal ordinate ๐๐ (e.g.,โ0โ5โ represents five different groups) [49], when๐ = 20, ๐increases from 100 to 500 with a step-length of 100, with theremainder following the same analogy. From Figure 3, it canbe seen that the maximum iterations significantly influencedthe computing time. When the population size ๐ was thesame, there was a positive correlation between the maximumgeneration and the computing time. For the optimal result,the maximum generation had an obvious influence on theresult, as shown in Figure 4. As can be seen, the results
Mathematical Problems in Engineering 9
Table 1: CDCs information (unit: 1 ร 102 RMB).
Node Location Capability Fixed cost NPV cost RPV cost ๏ฟฝ๏ฟฝ๐ Parameters ๐1 (23, 23) 900 12300 0.01 0.05 (0.18, ๐1, 0.25) ๐1 โผ ๐(0.21, 0.02)2 (25, 35) 550 12100 0.02 0.06 (0.23, ๐2, 0.28) ๐2 โผ ๐(0.25, 0.02)3 (34, 29) 1050 15600 0.01 0.05 (0.14, ๐3, 0.24) ๐3 โผ ๐(0.18, 0.04)4 (32, 25) 650 11300 0.01 0.07 (0.16, ๐4, 0.22) ๐4 โผ ๐(0.18, 0.03)5 (35, 37) 1050 17800 0.01 0.05 (0.25, ๐5, 0.30) ๐5 โผ ๐(0.28, 0.02)6 (36, 31) 1050 22400 0.01 0.06 (0.17, ๐6, 0.26) ๐6 โผ ๐(0.22, 0.03)7 (29, 28) 1050 16300 0.02 0.07 (0.15, ๐7, 0.23) ๐7 โผ ๐(0.20, 0.02)8 (18, 21) 800 14900 0.01 0.06 (0.19, ๐8, 0.28) ๐8 โผ ๐(0.24, 0.03)9 (29, 23) 1100 26500 0.01 0.06 (0.12, ๐9, 0.22) ๐9 โผ ๐(0.17, 0.04)10 (35, 26) 1050 2200 0.02 0.05 (0.17, ๐10, 0.23) ๐10 โผ ๐(0.20, 0.02)
No
No
No
Yes
Yes
Yes
Start
Initialize L
Encoding
Decoding
Calculate F1
Calculate F2
๐ = ๐ + 1
Fitness (P1) < Fitness (Pbest1 )
Fitness (P1) < Fitness (Pbestg )
Pbest1 = P1
Pbestg = P1
Update PLbest1 and generate PNbest
1d
๐ > T
End
Figure 2: The heuristic algorithms based on pb-glnPSO.
were better when ๐ was from 300 to 500 and ๐ was from40 to 60. Thus, for the above ๐ and ๐ group, the optimalresults, the standard deviation of 20 optimal results, andthe average computing time have been given in Table 4 forbetter analysis. From Table 4, it can be seen that the resultwas stable and reliable and that any further increase in themaximum iterations resulted in a longer computing time butno improvement in the optimal result, with the best resultbeingwhen๐= 50 and๐= 400.Therefore, the suitable valuesfor ๐ and๐ in the pb-glnPSO for this case were found to be400 and 50.
5.3. Sensitivity Analysis on the Parameters. To find the bestsolution to the proposed model, a series of experiments wereconducted, all of which were performed using MATLAB 7.0
on a workstation with an Intel(R) Core(TM)i7, a 2.59GHzclock pulse with 8GB memory, and a Windows 10 operatingsystem. A sensitivity analysis was performed to examinethe effectiveness and behavior of the proposed algorithm,as shown in Table 5. Several parameters were changed: thepopulation size ๐, the maximum generation ๐, and theacceleration constants ๐๐, ๐๐, ๐๐, and ๐๐. After trying variousvalues for the population size andmaximum generations, theresults were found to be better when ๐ was from 300 to 500and๐was from40 to 60.The different fitness values obtainedusing the pb-glnPSO with the different parameters ๐, ๐, ๐1,and ๐2 are shown in Table 5.
As can be seen from Table 5, when the parameters ๐๐,๐๐, ๐๐, and ๐๐ increased, the fitness value improved exceptfor ๐๐ = ๐๐ = ๐๐ = ๐๐ = 2.5 with the same generation
10 Mathematical Problems in Engineering
Table 2: Retailers, factories, and disposal center.
Node Location Demand๐1 (27, 28) 50๐2 (30, 19) 60๐3 (32, 22) 40๐4 (37, 16) 80๐5 (23, 29) 30๐6 (27, 17) 40๐1 (13, 22) 920๐7 (33, 26) 80๐8 (34, 32) 40๐9 (37, 22) 100๐10 (17, 22) 90๐11 (26, 39) 60๐12 (38, 26) 40๐2 (31, 44) 530๐13 (38, 34) 50๐14 (36, 25) 70๐15 (41, 19) 40๐16 (27, 33) 30๐17 (25, 39) 20๐18 (38, 37) 40๐3 (32, 15) 850๐19 (36,27) 50๐20 (39, 28) 60๐21 (25, 31) 70๐22 (29, 35) 90๐23 (18, 29) 50๐24 (18, 14) 60๐4 (42, 31) 940๐25 (35, 11) 80๐26 (23, 33) 50๐27 (36, 37) 60๐28 (28, 26) 40๐29 (25, 24) 30๐30 (32, 19) 80๐1 (18, 47) 800
and population size, with the fitness value increased from๐๐ = ๐๐ = ๐๐ = ๐๐ = 2 to ๐๐ = ๐๐ = ๐๐ = ๐๐ = 2.5.Therefore, when ๐๐ = ๐๐ = ๐๐ = ๐๐ = 2, the result wasfound to be optimal. For๐, given the same ๐๐, ๐๐, ๐๐, and ๐๐ andpopulation size, it was found that when ๐was 400, the fitnessvalue was better than for any other generation. Finally, for๐,the results improved as the population size increased and itwas found to be optimal when ๐ is 50. The most effectiveand efficient results were gained with ๐ at 400, ๐ at 50, and๐๐ = ๐๐ = ๐๐ = ๐๐ = 2.5.4. Result Analysis. In this section, the pb-glnPSO is con-ducted to solve the model using the above data. The param-eters for the problem were set as follows: population size:popsize = 50; maximum generation: maxGen = 400; inertiaweight: ๐(1) = 1 and ๐(๐) = 0.1; acceleration constant:
1
2
3
4
5
6
7
8
9
10
11
The c
ompu
ting
time
5 10 15 20 250
TN
Figure 3: The computing time.
Table 3: Retailersโ return rate.
Node ๏ฟฝ๏ฟฝ๐ Parameters ๐๐1 (0.28, ๐1, 0.33) ๐1 โผ ๐(0.31, 0.02)๐2 (0.52, ๐2, 0.62) ๐2 โผ ๐(0.56, 0.02)๐3 (0.32, ๐3, 0.38) ๐3 โผ ๐(0.36, 0.03)๐4 (0.62, ๐4, 0.73) ๐4 โผ ๐(0.67, 0.04)๐5 (0.55, ๐5, 0.62) ๐5 โผ ๐(0.58, 0.02)๐6 (0.65, ๐6, 0.72) ๐6 โผ ๐(0.69, 0.02)๐7 (0.73, ๐7, 0.81) ๐7 โผ ๐(0.78, 0.03)๐8 (0.72, ๐8, 0.78) ๐8 โผ ๐(0.75, 0.03)๐9 (0.75, ๐9, 0.82) ๐9 โผ ๐(0.80, 0.04)๐10 (0.34, ๐10, 0.38) ๐10 โผ ๐(0.36, 0.02)๐11 (0.42, ๐11, 0.48) ๐11 โผ ๐(0.46, 0.02)๐12 (0.46, ๐12, 0.50) ๐12 โผ ๐(0.48, 0.04)๐13 (0.65, ๐13, 0.70) ๐13 โผ ๐(0.67, 0.04)๐14 (0.62, ๐14, 0.68) ๐14 โผ ๐(0.64, 0.03)๐15 (0.72, ๐15, 0.78) ๐15 โผ ๐(0.75, 0.04)๐16 (0.74, ๐16, 0.79) ๐16 โผ ๐(0.77, 0.04)๐17 (0.70, ๐17, 0.75) ๐17 โผ ๐(0.73, 0.04)๐18 (0.75, ๐18, 0.80) ๐18 โผ ๐(0.78, 0.02)๐19 (0.68, ๐19, 0.75) ๐19 โผ ๐(0.72, 0.03)๐20 (0.55, ๐20, 0.65) ๐20 โผ ๐(0.61, 0.02)๐21 (0.35, ๐21, 0.45) ๐21 โผ ๐(0.39, 0.04)๐22 (0.35, ๐22, 0.42) ๐22 โผ ๐(0.38, 0.03)๐23 (0.55, ๐23, 0.60) ๐23 โผ ๐(0.58, 0.03)๐24 (0.52, ๐24, 0.62) ๐24 โผ ๐(0.56, 0.04)๐25 (0.62, ๐25, 0.72) ๐25 โผ ๐(0.66, 0.04)๐26 (0.69, ๐26, 0.75) ๐26 โผ ๐(0.72, 0.03)๐27 (0.29, ๐27, 0.35) ๐27 โผ ๐(0.32, 0.03)๐28 (0.40, ๐28, 0.46) ๐28 โผ ๐(0.43, 0.02)๐29 (0.42, ๐29, 0.48) ๐29 โผ ๐(0.45, 0.03)๐30 (0.50, ๐30, 0.58) ๐30 โผ ๐(0.54, 0.04)
๐๐ = ๐๐ = ๐๐ = ๐๐ = 2. After running the program 20times, the most satisfactory solution was found. Figure 6
Mathematical Problems in Engineering 11
Table 4: pb-glnPSO result (unit: 1 ร 102 RMB).
๐ = 40 ๐ = 50 ๐ = 60๐ = 300 ๐ = 400 ๐ = 500 ๐ = 300 ๐ = 400 ๐ = 500 ๐ = 300 ๐ = 400 ๐ = 500
Optimal result 82589.8 82434.4 82669.5 82102.4 81920.1 82634.9 82434.4 82189.2 82308.9Standard deviation 512.485 627.973 407.732 565.617 306.345 527.466 543.704 526.525 678.494Computing time 5.600 5.938 6.699 6.623 8.384 9.228 8.929 9.855 10.690
Table 5: Sensitivity analysis (unit: 1 ร 102 RMB).
๐1 = ๐2 ๐ = 40 ๐ = 50 ๐ = 60๐ = 300 ๐ = 400 ๐ = 500 ๐ = 300 ๐ = 400 ๐ = 500 ๐ = 300 ๐ = 400 ๐ = 500
0.5 84449.3 83447.3 83243.5 82889.4 82766.2 83859.6 82879.9 82830.6 83233.81 83256.4 83125.5 83128.3 82679.3 82459.0 83199.5 82596.8 82448.8 82614.41.5 83201.8 82765.3 82815.6 82486.5 82564.3 83041.9 82498.6 82257.8 82430.62 82589.8 82434.4 82669.5 82102.4 81920.1 82634.9 82434.4 82189.2 82308.92.5 83844.7 83102.9 82782.4 82370.5 82198.5 82792.7 82448.8 82469.9 82377.3
8.15
8.2
8.25
8.3
8.35
8.4
8.45
The o
ptim
al re
sult
5 10 15 20 250
TN
ร104
Figure 4: The optimal result.
shows the specific objective values found by the pb-glnPSO indifferent iterations and shows the reductions in the total costs.The results are presented in Figure 5. From the calculations,at least 3 CDCs could satisfy all markets. Alternative CDCpositions 4, 5, and 8 should be chosen. CDC 4 can sendproducts to markets 2, 3, 4, 6, 7, 9, 14, 15, 25, and 30. CDC5 can transport products for 1, 8, 11, 12, 13, 16, 17, 18, 19, 20,21, 22, and 27 and retailers 5, 10, 23, 24, 26, 28, and 29 canbe serviced by CDC 8. The total cost was found to be 8.192million RMB, in which the fixed costs were 4.4 million RMB,the transportation costs were 3.776 million RMB, and theoperating costs were 0.016 million RMB.
5.5. Algorithm Comparison. To better illustrate the effective-ness of the proposed algorithm, a brief comparison betweenthe pb-glnPSO, glnPSO, and an immune algorithm (IM) isgiven in this section. The glnPSO is a well-respected evolu-tionary algorithm that has been successfully implemented in
10 15 20 25 30 35 40 45
RetailerCDC
FactoryDisposal center
10
15
20
25
30
35
40
45
Figure 5: The distribution strategy.
a variety of engineering and combinatorial problems.The IMis also being widely used to solve facilities location problems.
To establish the solution quality for the pb-glnPSO, it wascompared with the glnPSO and the IM. Each run time forthe pb-glnPSO, glnPSO, and the IM was around 10 s. Thepb-glnPSO, glnPSO, and IM were run 20 times using thesame data. For a fair comparison between the groups, thepopulation size was set at 50 and the maximum generation at400. In the glnPSO, the acceleration constant was designed as๐๐ = ๐๐ = ๐๐ = ๐๐ = 2 and the inertia weight was ๐(1) = 1 and๐(๐) = 0.1. For the IM algorithm, the crossover probabilitywas 1 and the mutation probability was 0.1.
From Figure 7, it can be seen that the pb-glnPSO out-performed both the glnPSO and the IM, and, as the glnPSOconverged faster, it had a better result than the IM. Thisdemonstrated that a better solution can be obtained using theglnPSO, and an even better result can be obtained using the
12 Mathematical Problems in Engineering
50 100 150 200 250 300 350 4000Iteration
8
8.5
9
9.5
10
10.5
11
11.5
Fitn
ess v
alue
func
tion
ร104
Figure 6: The Pgln-PSO iterative process.
pb-glnPSOglnPSOIM
50 100 150 200 250 300 350 4000Iteration
8
8.5
9
9.5
10
10.5
11
11.5
Fitn
ess v
alue
func
tion
ร104
Figure 7: The iterative process of pb-glnPSO, glnPSO, and IM.
pb-glnPSO. The blue profile shows the convergence for thebest in history for the pb-glnPSO. It can be seen fromFigure 7that, as the programs ran, the results become stable for the pb-glnPSO and glnPSO after about the 150th generation, whilethe IM became stable after the 175th generation. As shown inFigure 7, the best solution for the pb-glnPSO was superiorto, more stable than, and had the smallest CPU run timecompared to the other algorithms (Table 6), with the IMhaving the highest run time.
6. Conclusion
Economic development has resulted in many environmentalpollution problems, the seriousness of which has encouraged
Table 6: Results of the pb-glnPSO, glnPSO, and IM.
Item pb-glnPSO glnPSO IMBest result 81920.1 82884.5 83315.6Worst result 82434.9 83826.6 84585.5Average result 82231.1 83467.6 83852.8Difference between the bestand the worst 514.81 942.10 1269.90
Difference between theaverage and the best 510.986 992.734 864.315
Standard deviation 203.82 583.08 537.27CPU time 8.6796 10.9764 14.5000
people to recycle and reuse products. To examine this prob-lem and seek appropriate solutions, a mathematical modelfor a collection-distribution center location and allocationproblem in a closed-loop supply chain under a fuzzy randomenvironment was presented for the beer industry in China.For this problem, a new model was formulated, in whichthe decision makers sought to minimize costs and pollutionunder capacity and quantity constraints. To more accuratelyrepresent actual production situations, the return rate anddisposal rate were considered to be fuzzy random variables.A heuristic algorithm, the pb-glnPSO, was then appliedto solve the problem. Based on the proposed priority, thedistribution and collection activity were shown to satisfyretailer demand and reduce costs and pollution after theCDCs start operations. After calculation, the best solutionwas determined and the advantages of the algorithm wereillustrated. The proposed model and method can be appliedto the location and allocation of CDCs in the beer industryto improve supply chain management.Themodel was shownto assist in generating retailer demand and dealing with thereturned products, which could benefit company recyclingand reuse policies. At the same time, the transportation costsand pollution were reduced because of the reductions inlosses from empty loads.
Notations
Sets
ฮฉ: Set of CDCs,ฮฉ = {1, 2, 3, . . . , ๐ผ}ฮจ: Set of factories, ฮจ = {1, 2, 3, . . . , ๐ฝ}ฮฆ: Set of retailers,ฮฆ = {1, 2, 3, . . . , ๐พ}ฮฅ: Set of disposal centers, ฮฅ = {1, 2, 3, . . . , ๐}
Indices and Parameters
๐: Alternative location position for theCDCs, ๐ โ ฮฉ = {1, 2, 3, . . . , ๐ผ}
๐: Known position of the factories,๐ โ ฮจ = {1, 2, 3, . . . , ๐ฝ}
๐: Known position of the retailers,๐ โ ฮฆ = {1, 2, 3, . . . , ๐พ}
๐: Known disposal center,๐ โ ฮฅ = {1, 2, 3, . . . , ๐}
Mathematical Problems in Engineering 13
๐: The upper limit of the CDCs๐ท๐: The demand of retailer ๐๐ผ๐: The capability of CDC ๐๐พ๐: The capability of factory ๐๐๐๐: Product quantity from factory ๐ to CDC ๐๐๐๐: Product quantity from CDC ๐ to retailer ๐๏ฟฝ๏ฟฝ๐: The product return rate from retailer ๐๏ฟฝ๏ฟฝ๐: The product disposal rate at CDC ๐๐น๐๐ : The fixed costs of the CDC ๐๐๐๐ : The variable costs of the CDC ๐ for a new
product unitRV๐๐ : The variable cost of the CDC ๐ triage for a
returned product unit๐ถ๐๐๐: Unit transportation cost between CDC ๐ and
factory ๐๐ถ๐๐๐: Unit transportation cost between CDC ๐ and
retailer ๐๐ถ๐ค๐๐: Unit transportation cost between CDC ๐ and
disposal center ๐๐ฝ๐๐: Environmental impact of transportation
between CDC ๐ and factory ๐๐ฝ๐๐: Environmental impact of transportation
between CDC ๐ and retailer ๐๐ฝ๐๐: Environmental impact of transportation
between CDC ๐ and disposal center ๐๐: The environmental impact level accepted by
decision makers.
Decision Variables
๐ฅ๐: A binary variable indicating whether point๐ is chosen. If point ๐ is chosen, then๐ฅ๐ = 1; else, ๐ฅ๐ = 0๐ฆ๐๐: It indicates whether retailer ๐ is served byCDC ๐. If ๐ is chosen, then ๐ฆ๐๐ = 1; else,๐ฆ๐๐ = 0
Notations for pb-glnPSO
๐: Iteration index, ๐ = 1, 2, . . . , ๐๐: Particle index, ๐ = 1, 2, . . . , ๐ฟV๐๐(๐): Velocity of the ๐th particle at the ๐th
dimension in the ๐th iteration๐best๐๐ : Personal best position๐๐ฟbest๐๐ : Local best position๐๐: Personal best position acceleration
constant๐๐: Local best position acceleration constant๐max: Maximum position value๐๐: Velocity vector of ๐th particle๐best๐ : Vector personal best position of ๐th
particle๐๐ฟbest๐ : Vector local best position of ๐th particleFitness(๐๐): Fitness value of ๐๐๐: Dimension index, ๐ = 1, 2, . . . , ๐ท๐๐: Inertia weight in ๐th iteration๐๐๐(๐): Position of the ๐th particle at the ๐th
dimension in the ๐th iteration
๐best๐๐ : Global best position๐๐best๐๐ : Near-neighbor best position๐๐: Global best position acceleration constant๐๐: Near-neighbor best position acceleration
constant๐min: Minimum position value๐๐: Position vector of ๐th particle๐best๐ : Vector global personal best position๐1, ๐2, ๐3, ๐4: Uniform distributed random number within
[0, 1].
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Acknowledgments
This research was supported by National Natural ScienceFoundation of China (Grants no. 71640013 and no. 71501137),National Planning Office of Philosophy and Social Science(Grant no. 14BGL055), and System Science and EnterpriseDevelopment Research Center (Grant no. Xq16C10).
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