Application of particle swarm optimisation with backward calculation to solve a fuzzy multi–objective supply chain master planning model. (2015)

• Record Type: Journal Article
• Authors: Grillo, Hanzel; Peidro, David; Alemany, M.M.E.; Mula, Josefa
• Is Part Of: International journal of bio-inspired computation. Volume 7:Number 3(2015)
• Page Start: 157
• Page End: 169
• Publication Date: 2015
• Subjects: metaheuristics -- particle swarm optimisation -- PSO -- backward calculation -- fuzzy set theory -- master planning -- bioinspired computation -- fuzzy logic -- supply chain planning -- supply chain management -- SCM -- uncertainty modelling; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

Abstract

Traditionally, supply chain planning problems consider variables with uncertainty associated with uncontrolled factors. These factors have been normally modelled by complex methodologies where the seeking solution process often presents high scale of difficulty. This work presents the fuzzy set theory as a tool to model uncertainty in supply chain planning problems and proposes the particle swarm optimisation (PSO) metaheuristics technique combined with a backward calculation as a solution method. The aim of this combination is to present a simple effective method to model uncertainty, while good quality solutions are obtained with metaheuristics due to its capacity to find them with satisfactory computational performance in complex problems, in a relatively short time period.