Unbalanced supply chain design using the analytic network process and a hybrid heuristic-based algorithm with balance modulating mechanism. (1st January 2011)

• Record Type: Journal Article
• Authors: Che, Z.H.; Cui, Zhihua
• Is Part Of: International journal of bio-inspired computation. Volume 3:Number 1(2011)
• Page Start: 56
• Page End: 66
• Publication Date: 2011-01-01
• Subjects: supply chain design -- partner relationship management -- PRM -- Taguchi method -- analytic network process -- ANP -- particle swarm algorithm -- genetic algorithm -- GA; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

Abstract

In this study, an optimisation mathematical model is developed for presenting the supply chain design problem which is based on a single-product and multi-echelon unbalanced system, and considering four criteria, including cost, quality, delivery time and partner relationship management (PRM), as well as decision factors such as quantity discount and capacity limits. To extract critical factors of PRM evaluation and estimate relative weight among the criteria, the analytic network process (ANP) is utilised. In addition, we propose a heuristic-based approach, called GP-TBM, based on a hybrid of the genetic algorithm (GA) and particle swarm optimisation (PSO) algorithm by introducing the balance modulating (BM) mechanism to solving the mathematical model to find the optimal supply chain network pattern. In the GP-TBM, the parameters are designed by the Taguchi method. Finally, a case of a {4-3-3-3} supply chain network structure is used to demonstrate the effectiveness of the proposed approach, and GP-TBM compared with standard PSO and GA. The empirical analysis results demonstrate GP-TBM is superior to standard PSO and GA in the proposed supply chain planning problems.