Title: Modelling JIT supply chains by simulation and hybrid genetic variable neighbourhood search algorithm
Authors: Ali Azadeh; Mohammad Abdollahi
Addresses: School of Industrial and Systems Engineering and Center of Excellence for Intelligent Based Experimental Mechanic, University College of Engineering, University of Tehran, Tehran 11365, Iran ' Department of Industrial and Systems Engineering, Wayne State University, Detroit, Michigan, USA
Abstract: The objective of this study is investigating multi-stage supply chain systems which is controlled using a kanban system so as to analyse and optimise it. In this paper, a new optimisation approach based on simulation analysis and metaheuristic methods to optimise a multi-echelon supply chain in a JIT production context is proposed. A hybrid of simulation modelling, genetic algorithm (GA), and variable neighbourhood search (VNS) to solve the above mentioned problem was proposed. In this research paper, GA-VNS is used iteratively to optimise the supply chain. Furthermore, the performance of the GA and VNS compared with their GA-VNS counterpart based on their relative error, and it is illustrated that the GA-VNS has ability to solve NP-hard problems in the area of complicated simulation optimisation models, especially where there is no prior knowledge of the behaviour of the system.
Keywords: supply chain management; SCM; simulation; genetic algorithms; optimisation; variable neighbourhood search; VNS; just-in-time; modelling JIT supply chains; kanban; modelling.
International Journal of Logistics Systems and Management, 2015 Vol.22 No.3, pp.296 - 312
Published online: 08 Oct 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article