A priority-based modified encoding–decoding procedure for the design of a bi-objective SC network using meta-heuristic algorithms. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Pasandideh, Seyed Hamid Reza; Asadi, Kobra
• Is Part Of: International journal of management science and engineering management. Volume 11:Issue 1(2016)
• Page Start: 8
• Page End: 21
• Publication Date: 2016-01-02
• Subjects: meta-heuristic algorithms -- priority-based encoding–decoding -- supply chain network design -- bi-objective optimization; C02 -- C12 -- C25 -- C44 -- C62; Management science -- Periodicals; Engineering -- Management -- Periodicals; Engineering -- Management; Management science; Periodicals; 658.005
• DOI: 10.1080/17509653.2014.959087
• Languages: English
• ISSNs: 1750-9661

Abstract

Abstract : This paper aims at a bi-objective optimization of the multi-periodic multi-product supply chain network design (SCND) problem under uncertainty consisting of manufacturing centers, distribution centers, and customer nodes. The problem is considered as a two-stage transportation model while there are several candidate transportation options for each plant. The supposed uncertainty is related to the services of the warehouses and affects the decision making of the SCND that are critical issues. So, the goal is to make the best decisions to maximize the minimum average percentages of products dispatched for each customer in each period while minimizing the sum of strategic, tactical and operational costs. Since the proposed model is of constrained bi-objective mixed-integer nonlinear programming type, we apply a linear programming metric and goal attainment methods to turn the problem into a single-objective model. Each of two methods is separately embedded in simulated annealing, a genetic algorithm, and an imperialist competition algorithm to solve this bi-objective model by MATLAB™ software. Furthermore, we extend a priority-based encoding–decoding method that leads to feasible solutions. The performance of the proposed algorithms is examined on a set of problem instances. Then, some numerical results are provided to demonstrate the algorithm's effectiveness and efficiency further. For this purpose, we propose Duncan's multi-range test and TOPSIS methods.