Selection of Transportation Channels in Closed-Loop Supply Chain Using Meta-Heuristic Algorithm

Selection of Transportation Channels in Closed-Loop Supply Chain Using Meta-Heuristic Algorithm

Sonu Rajak (National Institute of Technology, Tiruchirappalli, India), P. Parthiban (National Institute of Technology, Tiruchirappalli, India) and R. Dhanalakshmi (National Institute of Technology, Nagaland, India)
DOI: 10.4018/978-1-7998-0945-6.ch034

Abstract

This article presents a closed-loop supply chain (CLSC) network design problem consisting of both forward and reverse material flows. Here, a four-echelon single-product system is introduced in which multiple transportation channels are considered between the nodes of each echelon. Each design is analyzed for the optimum cost, time and environmental impact which form objective functions. The problem is modeled as a tri-objective mixed integer linear programming (MILP) model. The cost objective aggregates the opening cost (fixed cost) and the variable costs in both forward and reverses material flow. The time objective considers the longest transportation time from plants to customers and reverse. Factors of environmental impact are categorized and weighed using an analytic network process (ANP) which forms the environmental objective function. A genetic algorithm (GA) has been applied as a solution methodology to solve the MILP model. Ultimately, a case problem is also used to illustrate the model developed and concluding remarks are made regarding the results.
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Literature Review

Supply chain designs have been studied by many authors (Beamon, 1998; Melo et al., 2009; Thomas & Griffin, 1996; Vidal & Goetschalckx, 1997). In the Recent past, we can find that reverse logistics and CLSC are not only a cost minimization approach but also a revenue opportunity for the manufacturers and supply chain partners (Guide & Van Wassenhove, 2009). CLSC design has been the subject of study of many authors (Georgiadi et al., 2006; Atasu et al., 2008; Amin & Zhang, 2013; Chen et al., 2015; Giovanni & Zaccour, 2014; Ramezani et al., 2014; Shi et al., 2016; Tsao et al., 2016; Yang et al., 2009; Zohal & Soleimani., 2016; Chen et al., 2016; O'Reilly & Kumar, 2016).

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