A novel fuzzy mathematical model for an integrated supply chain planning using multi-objective evolutionary algorithm

Abstract

Due to the high competition in today’s global market, design of an efficient supply chain is necessary to achieve competitive advantages. Having concerned this issue, scheduling is one of critical concepts in supply chain management that leads to increase efficiency and customer satisfaction as well as decrease costs. Recently, build-to-order supply chain (BTO-SC) system has attracted considerable attention because of its successful implementation in high-tech companies such as Dell, BMW, Compaq, and Gateway. Despite several studies in the context of BTO-SC, there has been revealed several gaps that this study attempts to eliminate them. In this paper, a fuzzy mixed-integer linear programming optimization model is proposed for an integrated supply, production, and distribution supply chain of multi-product, multi-level, multi-plant. Because of uncertainty, imprecision and variability associated with real data, fuzzy approach is utilized for processing such information in modeling the problem. Usually, supply chain problems have conflicting objectives that should be optimized simultaneously. Thus, the proposed model aims to optimize customer satisfaction as well as chain supply costs, simultaneously. Due to high complexity and lack of proper benchmark for this problem, the model is solved by some popular multi-objective meta-heuristic algorithms. Also, Taguchi method is deployed to calibrate as well as control the parameters in four mentioned algorithms. Finally, a new framework is proposed for ranking of these algorithms use of fuzzy VIKOR method. The presented framework is an excellent comparison dashboard, and it is applicable not only for comparing multi-objective genetic algorithms but also for comparing all of multi-objective meta-heuristic algorithms.