A mathematical model and solution for cost-driven job-shop scheduling problem

Abstract

This paper presents a cost-driven model of the job-shop scheduling problem and an effective hybrid optimization algorithm to solve it. The cost model is developed in terms of a combination of multi-dimensional costs generated from product transitions, revenue loss, earliness / tardiness penalty, and so on. The new hybrid optimization algorithm combines the strong global search ability of scatter search with the strong local search ability of simulated annealing. In order to illustrate the effectiveness of the hybrid method, several test problems are generated, and the performance of the proposed method is compared with other evolutionary algorithms. The experimental simulation tests show that the hybrid method is quite effective at solving the cost-driven jobshop scheduling problem. © 2011 Springer-Verlag Berlin Heidelberg.