Fuzzy MOGA for supply chain models with Pareto decision space at different α-cuts

Fuzzy MOGA for supply chain models with Pareto decision space at different α-cuts Today’s global environment is highly competitive. The competition between two firms means the competition in their supply chain. The prime objective of decision makers are to optimize the supply chain. In every stage of supply chain, the cost and delivery time are involved. These criteria are uncertain in nature. We express these criteria in either the triangular or trapezoidal fuzzy numbers. While optimizing the time and cost simultaneously, the problem becomes bi-objective. Such problems can be solved by using Multiple Objective Genetic Algorithm (MOGA). We get the different Pareto front at different α-cuts which is called as the Pareto decision space. According to the uncertainty in the market, decision maker has a choice to take decision among the set of alternatives. Here, the proposed solution methodology is fusion of fuzzy set theory and MOGA. Fuzzy set theory is used for uncertainty in parameters and MOGA is used for obtaining non-dominated solutions. The numerical example is given to illustrate the proposed methodology and industrial case study is solved by using proposed methodology. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Advanced Manufacturing Technology Springer Journals

Fuzzy MOGA for supply chain models with Pareto decision space at different α-cuts

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Springer London
Copyright © 2017 by Springer-Verlag London
Engineering; Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design
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