Development and Comparison of Hybrid Genetic Algorithms for Network Design Problem in Closed Loop Supply Chain (original) (raw)
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Mathematics
This paper deals with a complex optimization problem, more specifically the two-stage transportation problem with fixed costs. In our investigated transportation problem, we are modeling a distribution network in a two-stage supply chain. The considered two-stage supply chain includes manufacturers, distribution centers, and customers, and its principal feature is that in addition to the variable transportation costs, we have fixed costs for the opening of the distribution centers, as well as associated with the routes. In this paper, we describe a different approach for solving the problem, which is an effective hybrid genetic algorithm. Our proposed hybrid genetic algorithm is constructed to fit the challenges of the investigated supply chain network design problem, and it is achieved by incorporating a linear programming optimization problem within the framework of a genetic algorithm. Our achieved computational results are compared with the existing solution approaches on a set ...
Sustainable Closed-Loop Supply Chain Design Problem: A Hybrid Genetic Algorithm Approach
Mathematics, 2020
In this paper, we propose a solution to the sustainable closed-loop supply chain (SCLSC) design problem. Three factors (economic, environmental, and social) are considered for the problem and the three following requirements are addressed while satisfying associated constraint conditions: (i) minimizing the total cost; (ii) minimizing the total amount of CO2 emission during production and transportation of products; (iii) maximizing the social influence. Further, to ensure the efficient distribution of products through the SCLSC network, three types of distribution channels (normal delivery, direct delivery, and direct shipment) are considered, enabling a reformulation of the problem as a multi-objective optimization problem that can be solved using Pareto optimal solutions. A mathematical formulation is proposed for the problem, and it is solved using a hybrid genetic algorithm (pro-HGA) approach. The performance of the pro-HGA approach is compared with those of other conventional ...
A steady-state genetic algorithm for multi-product supply chain network design
Computers & Industrial Engineering, 2009
Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management (SCM). The problem is often an important and strategic operations management problem in SCM. The design task involves the choice of facilities (plants and distribution centers (DCs)) to be opened and the distribution network design to satisfy the customer demand with minimum cost. This paper presents a solution procedure based on steady-state genetic algorithms (ssGA) with a new encoding structure for the design of a single-source, multi-product, multi-stage SCN. The effectiveness of the ssGA has been investigated by comparing its results with those obtained by CPLEX, Lagrangean heuristic, hyrid GA and simulated annealing on a set of SCN design problems with different sizes.
Applied Mathematical Modelling, 2015
Today, tracking the growing interest in closed-loop supply chain shown by both practitioners and academia is easily possible. There are many factors, which transform closed-loop supply chain issues into a unique and vital subject in supply chain management, such as environmental legislation, customer awareness, and the economical motivations of the organizations. However, designing and planning a closed-loop supply chain is an NP-hard problem, which makes it difficult to achieve acceptable results in a reasonable time. In this paper, we try to cope with this problem by proposing a new and effective solution methodology. On the other hand, this research considers improving closed-loop supply chain network optimization processes through dealing with mathematical programming tools; developing a deterministic multi-product, multi-echelon, multi-period model; and finally presenting an appropriate methodology to solve various sizes of instances. Both design and planning decision variables (location and allocation) are considered in the proposed network. Besides, in order to have a reliable performance evaluation process, large-scale instances are regarded in computational analysis. Two popular metaheuristic algorithms are considered to develop a new elevated hybrid algorithm: The Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Analyzing the above-mentioned algorithms' strengths and weaknesses leads us to attempt to improve the GA using some aspects of PSO. Therefore, a new hybrid algorithm is proposed and a complete validation process is undertaken using CPLEX and MATLAB software. In small instances, the global optimum points of CPLEX for the proposed hybrid algorithm are compared to genetic algorithm, and particle swarm optimization. Then, in small, mid, and large-size instances, performances of the proposed meta-heuristics are analyzed and evaluated. Finally, a case study involving an Iranian hospital furniture manufacturer is used to evaluate the proposed solution approach. The results reveal the superiority of the proposed hybrid algorithm when compared to the GA and PSO.
Çankaya University Journal of Humanities and Social Sciences, 2013
Designing an optimal supply chain network (SCN) is an NP-hard and highly nonlinear problem; therefore, this problem may not be solved efficiently using conventional optimization methods. In this article, we propose a genetic algorithm (GA) approach with segment-based operators combined with a local search technique (SHGA) to solve the multistage-based SCN design problems. To evaluate the performance of the proposed algorithm, we applied SHGA and other competing algorithms to SCNs with different features and different parameters. The results obtained show that the proposed algorithm outperforms the other competing algorithms.
A genetic algorithm approach for multi-objective optimization of supply chain networks
Computers & Industrial Engineering, 2006
Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management. It is an important and strategic operations management problem in supply chain management, and usually involves multiple and conflicting objectives such as cost, service level, resource utilization, etc. This paper proposes a new solution procedure based on genetic algorithms to find the set of Pareto-optimal solutions for multi-objective SCN design problem. To deal with multi-objective and enable the decision maker for evaluating a greater number of alternative solutions, two different weight approaches are implemented in the proposed solution procedure. An experimental study using actual data from a company, which is a producer of plastic products in Turkey, is carried out into two stages. While the effects of weight approaches on the performance of proposed solution procedure are investigated in the first stage, the proposed solution procedure and simulated annealing are compared according to quality of Pareto-optimal solutions in the second stage.
Fuel and Energy Abstracts, 2010
Supply chain network (SCN) design is a strategic issue which aims at selecting the best combination of a set of facilities to achieve an efficient and effective management of the supply chain. This paper presents an innovative encoding-decoding procedure embedded within a genetic algorithm (GA) to minimize the total logistic cost resulting from the transportation of goods and the location and opening of the facilities in a single product three-stage supply chain network. The new procedure allows a proper demand allocation procedure to be run which avoids the decoding of unfeasible distribution flows at the stage of the supply chain transporting products from plants to distribution centers. A numerical study on a benchmark of problems demonstrates the statistical outperformance of the proposed approach vs. others currently available in literature in terms of total supply chain logistic cost saving and reduction of the required computation burden to achieve an optimal design. . This approach usually involves making tradeoffs among the cost components of the system that include: (i) the costs of opening and operating the facilities and (ii) the inbound and outbound transportation costs.
A Multi-Stage Supply Chain Network Optimization Using Genetic Algorithms
In today's global business market place, individual firms no longer compete as independent entities with unique brand names but as integral part of supply chain links. Key to success of any business is satisfying customer's demands on time which may result in cost reductions and increase in service level. In supply chain networks decisions are made with uncertainty about product's demands, costs, prices, lead times, quality in a competitive and collaborative environment. If poor decisions are made, they may lead to excess inventories that are costly or to insufficient inventory that cannot meet customer's demands.
2019
In the current world, the debate on the reinstatement and reuse of consumer products has become particularly important. Since the supply chain of the closed loop is not only a forward flow but also a reverse one; therefore, companies creating integrity between direct and reverse supply chain are successful. The purpose of this study is to develop a new mathematical model for closed loop supply chain network. In the real world the demand and the maximum capacity offered by the supplier are uncertain which in this model; the fuzzy theory discussion was used to cover the uncertainty of the mentioned variables. The objective functions of the model include minimizing costs, increasing revenues of recycling products, increasing cost saving from recycling and environmental impacts. According to the NP-hard, an efficient algorithm was suggested based on the genetic Meta heuristic algorithm to solve it. Twelve numerical problems were defined and solved using the NSGA-II algorithm to validate...
Solving a multi-stage multi-product solid supply chain network design problem by meta-heuristics
Scientia Iranica
This paper presents an effective optimization method based on meta-heuristics algorithms for the design of a multi-stage, multi-product solid supply chain network design problem. First, a mixed integer linear programming model is proposed. Second, because the problem is a NP-hard, three meta-heuristics algorithms, namely Differential evolution (DE), Particle swarm optimization (PSO) and Gravitational search algorithm (GSA) are developed for the first time of this kind of problem. To the best of our knowledge, neither DE and PSO nor GSA has been considered for the multi-stage solid supply chain network design problems. Furthermore, the Taguchi experimental design method is used to adjust the parameters and operators of the proposed algorithms. Finally, to evaluate the impact of increasing the problem size on the performance of our proposed algorithms, different problem sizes are applied and the associated results are compared with each other. (Masoud Sanei).