Optimisation of multi-stage supply chain systems by integrated simulation-variable neighbourhood search algorithm

Abstract

In this paper, multi–stage supply chain systems (SCSs) controlled by kanban system are appraised a new simulation metaheuristics approach. In the kanban system, decision making is based on determination of batch size for each kanban. This paper simulates supply chain system regarding the costs under just–in–time (JIT) production philosophy. Since the adopted model is of backward type, the desired output is given in order to find the parameters and/or the structure of the model producing the output. This backward problem is non–analytic and often seems to be even more complex than the forward one. This paper applies genetic algorithm (GA) and variable neighbourhood search (VNS) to optimise the simulation model. A simple real–coded GA and VNS is presented and used to change the simulation model parameters. With each new set of parameters, a simulation run is performed. From the statistics gathered by running the simulation, a goal function is constructed to measure the quality of these parameters. GA and VNS and GA–VNS successfully provide a parameter set to demonstrate its capability to solve such difficult backward problems even in the area of complex simulation model optimisation specially when there is no prior knowledge of simulation model behaviour.