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An Optimization Model for Reverse Logistics Network under Stochastic Environment Using Genetic Algorithm

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International ournal of usiness and Social Science Vol No [Special Issue une 0] n Optimization Model for Reverse Logistics Network under Stochastic Environment Using Genetic lgorithm MostafaHosseinzadeh
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International ournal of usiness and Social Science Vol No [Special Issue une 0] n Optimization Model for Reverse Logistics Network under Stochastic Environment Using Genetic lgorithm MostafaHosseinzadeh Department of Industrial Engineering Islamic zad university rak ranch, Iran EmadRoghanian Department of Industrial Engineering haje Nasir Toosi University(NTU) Tehran, Iran bstract Recovery of used products has become increasingly important recently due to economic reasons and growing environmental or legislative concern Product recovery, which comprises reuse, remanufacturing and materials recycling, requires an efficient reverse logistic network One of the main characteristics of reverse logistics network problem is uncertainty that further amplifies the complexity of the problem The degree of uncertainty in terms of the capacities, demands and quantity of products exists in reverse logistics parameters With consideration of the factors noted above, this paper proposes a probabilistic mixed integer linear programming model for the design of a reverse logistics network This probabilistic model is first converted into an equivalent deterministic model In this paper we proposed multi-product, multi-stage reverse logistics network problem for the return products to determines not only the subsets of disassembly centers and processing centers to be opened, but also the transportation strategy that will satisfies demand imposed by manufacturing centers and recycling centers with minimum fixed opening cost and total shipping cost Then, we propose priority based genetic algorithm to find reverse logistics network to satisfy the demand imposed by manufacturing centers and recycling centers with minimum total cost under uncertainty condition Finally, we apply the proposed model to a numerical example eywords: Reverse logistics network, Genetic algorithm (G), Priority-based encoding, Stochastic programming Introduction and literature review Reverse logistics Increasing interest in reuse of products and materials is one of the consequences of growing environmental concern throughout the past decades Waste reduction has become a prime concern in industrialized countries []For a variety of economic, environmental or legislative reasons, companies have become more accountable for final products, after they sell those products Reverse logistics is the process of moving goods from their typical final destination to another point, for the purpose of capturing value otherwise unavailable, or for the proper disposal of the products [] ccording to the merican Reverse Logistics Executive Council, Reverse Logistics is defined as: The process of planning, 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 the purpose of recapturing value or proper disposal reverse logistics system comprises a series of activities, which form a continuous process to treat return-products until they are properly recovered or disposed of These activities include collection, cleaning, disassembly, test and sorting, storage, transport, and recovery operations The latter can also be represented as one or a combination of several main recovery options, like reuse, repair, refurbishing, remanufacturing, cannibalization and recycling [] Reverse logistics is practiced in many industries, including those producing steel, aircraft, computers, automobiles, chemicals, appliances and medical items The effective use of the reverse logistics can help a company to compete in its industry The Special Issue on Contemporary Research in rts and Social Science Centre for Promoting Ideas, US Reverse logistics has become increasingly important as a profitable and sustainable business strategy There are a number of situations for products to be placed in a reverse flow Normally, return flows are classified into commercial returns, warranty returns, end-of-use returns, reusable container returns and others [] Implementation of reverse logistics especially in product returns would allow not only for savings in inventory carrying cost, transportation cost, and waste disposal cost due to returned products, but also for the improvement of customer loyalty and futures sales [] Reverse logistic systems are more complex than forward logistic systems This complexity stems from a high degree of uncertainty due to the quantity and quality of the products [] Reverse logistics is receiving much attention recently due to growing environmental or legislative concern and economic opportunities for cost savings or revenues from returned products arros et al [] proposed a mixed integer linear programming (MILP) model based on a multi-level capacitated warehouse location problem for the sand and consider its optimization using heuristic procedures The model determined the optimal number, capacities, and locations of the depots and cleaning facilities irkke et al [] presented an MILP model based on a multi-level uncapacitated warehouse location model They described a case study, dealing with a reverse logistics network for the returns, processing, and recovery of discarded copiers The model was used to determine the locations and capacities of the recovery facilities as well as the transportation links connecting various locations ayaraman et al [] proposed an MILP model to determine the optimal number and locations of distribution/remanufacturing facilities for electronic equipment ayaraman et al [] developed a mixed integer programming model and solution procedure for a reverse distribution problem focused on the strategic level The model determines whether each remanufacturing facility is open considering the product return flow Min et al [0] proposed a Lagrangian relaxation heuristics to design the multicommodity, multi-echelon reverse logistics network im et al [] proposed a general framework for remanufacturing environment and a mathematical model to maximize the total cost saving The model determines the quantity of products/parts processed in the remanufacturing facilities/subcontractors and the amount of parts purchased from the external suppliers while maximizing the total remanufacturing cost saving Min et al [] proposed a nonlinear mixed integer programming model and a genetic algorithm that can solve the reverse logistics problem involving product returns Their study proposes a mathematical model and G which aim to provide a minimum-cost solution for the reverse logistics network design problem involving product returns o and Evans [] presented a mixed integer nonlinear programming model for the design of a dynamic integrated distribution network to account for the integrated aspect of optimizing the forward and return network simultaneously They also proposed a genetic algorithm-based heuristic for solving this problem Lee et al [] proposed a multi-stage, multi-product, MILP model for minimizing the total of costs to reverse logistics shipping cost and fixed opening cost of facilities They also proposed a hybrid genetic algorithm for solving this problem Stochastic programming In most of the real life problems in mathematical programming, the parameters are considered as random variables The branch of mathematical programming which deals with the theory and methods for the solution of conditional extremum problems under incomplete information about the random parameters is called stochastic programming Most of the problems in applied mathematics may be considered as belonging to any one of the following classes []: Descriptive Problems, in which, with the help of mathematical methods, information is processed about the investigated event, some laws of the event being induced by others Optimization Problems in which from a set of feasible solutions, an optimal solution is chosen esides the above division of applied mathematics problems, they may be further classified as deterministic and stochastic problems In the process of the solution of the stochastic problem, several mathematical methods have been developed However, probabilistic methods were for a long time applied exclusively to the solution of the descriptive type of problems Research on the theoretical development of stochastic programming is going on for the last four decades To the several real life problems in management science, it has been applied successfully [] The chance constrained programming was first developed by Charnes and Cooper [] Subsequently, some researchers like Sengupta [], Contini [], Sullivan and Fitzsimmons [], Leclercq [0], Teghem et al [] and many others have established some theoretical results in the field of stochastic programming 0 International ournal of usiness and Social Science Vol No [Special Issue une 0] Stancu-Minasian and Wets [] have presented a review paper on stochastic programming with a single objective function Listes and Dekker [] proposed a multi product stochastic mixed integer programming for recycling of the sand in reverse logistics network Liu [] introduced the stochastic programming methodology to characterize the stochastic traffic for a multi-commodity network model Genetic algorithm G s are stochastic search techniques based on the mechanism of natural selection and natural genetics [] s one of the Evolutionary Computation (EC) techniques, the G has been receiving great attention and successfully applied for combinatorial optimization problems [] G is very useful when a large search space with little knowledge of how to solve the problem is presented It belongs to the class of heuristic optimization techniques, which include simulated annealing (S), Tabu search, and evolutionary strategies It has been with great success in providing optimal or near optimal solution for many diverse and difficult problems [] Representation is one of the important issues that affect the performance of Gs Usually different problems have different data structures or genetic representations Tree-based representation is known to be one way for representing network problems There are three ways of encoding tree: () edge-based encoding, () vertex-based encoding and () edge-and-vertex encoding [] Michalewicz et al [] used matrix-based representation G which belongs to edge-based encoding for solving linear and non-linear transportation/distribution problems When m and n are the number of sources and depots, respectively, the dimension of matrix will be m*n lthough representation is very simple, this approach needs special crossover and mutation operators for obtaining feasible solutions Gen and Cheng [] introduced spanning tree G (st-g) for solving network problems They used Prüfer number representation for solving transportation problems and developed feasibility criteria for Prüfer number to be decoded into a spanning tree Syarif et al [] proposed spanning tree-based genetic algorithm by using prüfer number representation for solving a single product, three stage supply chain network (SCN) problem Xu et al [0] applied spanning tree-based genetic algorithm (st-g) by the Prüfer number representation to find the SCN to satisfy the demand imposed by customers with minimum total cost and maximum customer services for multi objective SCN design problem lthough Prüfer number developed to encode of spanning trees, had been successfully applied to transportation problems, it needs some repair mechanisms to obtain feasible solutions after classical genetic operators In this study, to escape from these repair mechanisms in the search process of G, we adopt at here the prioritybased encoding method Gen et al [] used priority-based encoding for a single-product, two-stage transportation problem ltiparmak et al [] applied priority-based representation to a single- product, singlesource, and three-stage SCN problem, ltiparmak et al [] proposed this encoding to a single-source, multiproduct, multi-stage SCN problem Lee et al [] proposed a hybrid genetic algorithm with priority-based encoding method One of the main characteristics of reverse logistics network problem is uncertainty that further amplifies the complexity of the problem The degree of uncertainty in terms of the capacities, demands and quantity of products exists in reverse logistics parameters n important issue, when manufacturing centers demand and recycling centers demand are random variables in reverse logistics network design problem, is to find the network strategy that can achieve the objective of minimization of total shipping cost and fixed opening costs of the disassembly centers and the processing centers With consideration of the factors noted above, this paper proposes a probabilistic mixed integer linear programming model for the design of a reverse logistics network This probabilistic model is first converted into an equivalent deterministic model In this paper we propose multiproduct, multi-stage reverse logistics network problem which consider the minimizing of total shipping cost and fixed opening costs of the disassembly centers and the processing centers in reverse logistics In fact, this type of network design problem belongs to the class of NP-hard problems, so that priority based genetic algorithm will be presented in order to solve large size problem Finally, we apply the proposed model to an example problem and show the numerical results This paper is organized as follows: in section, the stochastic constraint is explained and we present an approach to convert it into a deterministic for special case (normal distribution) The mathematical model of the reverse logistics network is introduced in section The Special Issue on Contemporary Research in rts and Social Science Centre for Promoting Ideas, US In section, the priority-based G approach is explained in order to solve this problem n illustrative numerical example is given in section Finally, concluding remarks are outlined in section Stochastic constraint If X~n μ, σ, its density function is f x = e (x μ σ )/ ; x + π σ Z = X μ transforms X to Z that has following properties: σ Z~n 0, f z = π e z / ; z + P Z z α = α, P Z z α = α n For example if j = X j is a constraint in a mathematical programming problem and ~n μ, σ then n P( X j ) α is equivalent to: j = P μ σ n j = X j μ σ α or P Z n j = X j μ σ α that resulted in n j = X j μ σ Z α or σ Z α + μ Therefore: n n P j= X j α j = X j σ Z α + μ () n j = X j Mathematical formulation In this section, we present a reverse logistics network problem for the return products to determines not only the subsets of disassembly centers and processing centers to be opened, but also the transportation strategy that will satisfies demand imposed by manufacturing centers and recycling centers with minimum fixed opening cost and total shipping cost However, in reverse logistics network design problem, it is hard to describe these problem parameters as known variables because there are not sufficient enough data to analyze The degree of uncertainty in terms of the capacities, demands and quantity of products exists in reverse logistics parameters With consideration of the factors noted above, in this section, we present a probabilistic mixed integer linear programming model for the design of a reverse logistics network In the remanufacturing process, after dismantling products to parts, reusable parts are sent from disassembly centers to processing centers according to their types for inspecting, cleaning and preparing These parts become new products by combined with another parts of processed or new in manufacturing centers In the recycling process, after dismantling products to parts, parts which are not reusable but are recyclable are sent directly from disassembly centers to recycling centers according to their types Some products that do not need to disassemble send directly from returning centers to the processing centers, according to the product type (see Fig ) Model assumptions () The demand of manufacturing centers and recycling centers are regarded as random variables () The number of returning centers and manufacturing centers and recycling centers are known and constant () The number of potential processing centers and disassembly centers and their maximum capacities are known () Some products that do not need to disassemble should send from returning centers to processing centers directly, not through disassembly centers () Some parts should send form disassembly centers to recycling centers directly, not through processing centers The notations used for the considered problem are listed below: Indices i is an index for returning center ( i =,,,, I) j is an index for disassembly center (j =,,,, ) k is an index for processing center (k =,,,, ) f is an index for manufacturing center (f =,,,, F) r is an index for recycling center (r =,,,, R) p is an index for product (p =,,,, P) m is an index for part (m =,,,, M) International ournal of usiness and Social Science Vol No [Special Issue une 0] Model variables x ijp is amount shipped from returning center i to disassembly center j for product p x ikp is amount shipped directly from returning center i to processing center k for product p x jkm is amount shipped from disassembly center j to processing center k for part m x jrm is amount shipped directly from disassembly center j to recycling center r for part m x kfm is amount shipped from processing center k to manufacturing center f for part m x krm is amount shipped from processing center k to recycling center r for part m, if disassembly centerjis open, j, m Y jm = 0, otherwise, if processing center k is open, k, m Q km = 0 otherwise Model parameters I is the number of returning centers is the number of disassembly centers is the number of processing centers F is the number of manufacturing centers R is the number of recycling centers P is the number of products M is the number of parts a ip is the capacity of returning center i for product p b jm is the capacity of disassembly center j for part m u km is the capacity of processing center k for part m d fm is the demand of part m in manufacturing center f (random variable) d rp is the demand of product p in recycling center r (random variable) d rm is the demand of part m in recycling center r (random variable) n mp is the number of part for the part type m from disassembling one unit of product p c ijp is the unit cost of shipping from returning center i to disassembly center j for product p c ikp is the unit cost of shipping from returning center i to processing center k for product p c jkm is the unit cost of shipping from disassembly center j to processing center k for part m c jrm is the unit cost of shipping from disassembly center j to recycling center r for part m c kfm is the unit cost of shipping from processing center k to manufacturing center f for part m c krm is the unit cost of shipping from processing center k to recycling center r for part m c jm oc is the fixed opening cost for disassembly center j for part m oc c km is the fixed opening cost for processing center k for part m α is the confidence level The problem can be formulated as follow: Min Z = P oc c jp Y jm M oc + c km Q km I P + c ijp x ijp I P + c ikp x ikp j = p= k= m = M i= j = p= R M i= k= p= F M + c jkm x jkm + c jrm x jrm + c kfm x kfm j = k= m = R M j = r= m = k= f= m = s t + c krm k = r= m= x krm The Special Issue on Contemporary Research in rts and Social Science j = x ijp a ip, i, p Centre for Promoting Ideas, US () k= x ikp a ip, i, p () k= x jkm b jm Y jm, j, p, m () R x jrm r= b jm Y jm, j, p, m ) F f= x kfm u km Q km, k, m () R r= x krm u km Q km, k, m () x jkm j = k = I n mp R x ijp i= j = m, p () x jrm j = r= I n mp x ijp i= j = m, p () P ( j = k= x jkm F f= d fm ) α fm, f, m (0) I i= k= R d rp P( x ikp P r= ) α rp, r, p () k= x kfm d fm α fm, f, m () P x krp k= d rp α rp, r, p () Int
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