Multiobjective Optimization for a Multienterprise Supply Chain Network

Chen, Cheng-Liang; Wang, Bin-Wei; Lee, Wen-Cheng

doi:10.1021/ie0206148

Cited by 94 publications

(58 citation statements)

References 17 publications

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“…The weighted the ε-constraint, the goal programming (GP), the Pareto optimal, and the LP-norm method are the most popular methods in the literature for treating multi-objective optimization problems [63][64][65][66][67] . In multi-objective problems, since the objective functions have several conflicts with each other, obtaining an optimal solution, which can optimize all of the objective functions altogether, is very difficult (or sometimes impossible) in most of the times.…”

Section: Fuzzy Goal Programmingmentioning

confidence: 99%

A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem

2017

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Abstract:This study considers a multi-objective combined budget constrained facility location/network design problem (FL/NDP) in which the system uncertainty is considered. The most obvious practical examples of the problem are territorial designing and locating of academies, airline networks, and medical service centers. In order to assure the network reliability versus uncertainty, an efficient robust optimization approach is applied to model the proposed problem. The formulation is minimizing the total expected costs, including, transshipment costs, facility location (FL) costs, fixed cost of road/link utilization as well as minimizing the total penalties of uncovered demand nodes. Then, in order to consider of several system uncertainty, the proposed model is changed to a fuzzy robust model by suitable approaches. An efficient Sub-gradient based Lagrangian relaxation algorithm is applied. In addition, a practical example is studied. At the following, a series of experiments, including several test problems, is designed and solved to evaluate of the performance of the algorithm. The obtained results emphasize that considering of practical factors (e.g., several uncertainties, system disruptions, and customer satisfaction) in modelling of the problem can lead to significant improvement of the system yield and subsequently more efficient utilization of the established network.

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Section: Fuzzy Goal Programmingmentioning

confidence: 99%

A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem

2017

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“…(Chen et al 2003;Chen and Lee 2004;Guilléna et al 2004;Hung 2011). In all these mentioned methods, multiple objectives are combined and form a single objective problem and then the optimal solution is obtained.…”

Section: Multi-objective Optimization Problem In Scmentioning

confidence: 99%

A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development

et al. 2017

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There are many reasons for the growing interest in developing new product projects for any firm. The most embossed reason is surviving in a highly competitive industry which the customer tastes are changing rapidly. A well-managed supply chain network can provide the most profit for firms due to considering new product development. Along with profit, customer satisfaction and production of new products are goals which lead to a more efficient supply chain. As new products appear in the market, the old products could become obsolete, and then phased out. The most important parameter in a supply chain which considers new and developed products is the time that developed and new products are introduced and old products are phased out. With consideration of the factors noted above, this study proposes to design a tri-objective multi-echelon multiproduct multi-period supply chain model, which incorporates product development and new product production and their effects on supply chain configuration. The supply chain under consideration is assumed to consist of suppliers, manufacturers, distributors and customer groups. In terms of overcoming NP-hardness of the proposed model and in order to solve the complicated problem, a non-dominated sorting genetic algorithm is employed. As there is no benchmark available in the literature, the non-dominated ranking genetic algorithm is developed to validate the results obtained and some test problems are provided to show the applicability of the proposed methodology and evaluate the performance of the algorithms.

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“…One of the traditional objective functions is maximizing the profit of the chain or minimizing the total cost (Tsiakis et al, 2001;Elhedhli and Gzara, 2008;Altiparmak et al, 2009). Another prevalent objective function in the researches is transporting time which aim to select the facilities in a supply chain so that the transportation time be minimized (Sabri and Beamon, 2000;Guillén et al, 2005;Chen et al, 2008). In the recent years some researches consider total cost and transportation time simultaneously (Cardona-Valdés et al, 2011;MoncayoMartı´nez and Zhang, 2011).…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Bi-Objective Multi-Echelon Multi-Product Supply Chain Network Design Using New Pareto-Based Approaches

Jafari¹,

Seifbarghy²

2016

Industrial Engineering and Management Systems

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The efficiency of a supply chain can be extremely affected by its design which includes determining the flow pattern of material from suppliers to costumers, selecting the suppliers, and defining the opened facilities in network. In this paper, a multi-objective multi-echelon multi-product supply chain design model is proposed in which several suppliers, several manufacturers, several distribution centers as different stages of supply chain cooperate with each other to satisfy various costumers' demands. The multi-objectives of this model which considered simultaneously are 1-minimize the total cost of supply chain including production cost, transportation cost, shortage cost, and costs of opening a facility, 2-minimize the transportation time from suppliers to costumers, and 3-maximize the service level of the system by minimizing the maximum level of shortages. To configure this model a graph theoretic approach is used by considering channels among each two facilities as links and each facility as the nodes in this configuration. Based on complexity of the proposed model a multi-objective Pareto-based vibration damping optimization (VDO) algorithm is applied to solve the model and finally non-dominated sorting genetic algorithm (NSGA-II) is also applied to evaluate the performance of MOVDO. The results indicated the effectiveness of the proposed MOVDO to solve the model.

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Multiobjective Optimization for a Multienterprise Supply Chain Network

Cited by 94 publications

References 17 publications

A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem

A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem

A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development

Optimizing Bi-Objective Multi-Echelon Multi-Product Supply Chain Network Design Using New Pareto-Based Approaches

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