Interactive adaptive particle swarm optimization for optimal global supply chain design

Abstract

This paper integrates all concerned levels of supply chain with their conflicting objectives and identifies the best solution for its design. More precisely two objectives viz. maximisation of overall quality and overall cost have been targeted. Considering both objectives, a multi-objective model has been formulated to integrate both tangible and intangible factors in the resource assignment problem of a product driven supply chain. Quality corresponding to each entity has been determined by applying a fuzzy-analytical hierarchical process approach. Minimisation of cost has been mathematically formulated with due consideration of various cost types. Proposed interactive adaptive multi-objective algorithm incorporates the decision maker's preference model to improve the accuracy of PSO in deciding the weight corresponding to each objective considered. Extensive experiments are performed on the underlying example, and computational results are reported and compared with the traditional particle swarm optimisation (PSO) algorithm and genetic algorithm to support the efficacy of the proposed algorithm.