Evaluating the Robustness of Production Schedules using Discrete-Event Simulation

Complex stochastic job-shop scheduling problems can be handled by simulation-based optimization (SBO), combining the optimization capabilities of meta-heuristics with the system representativeness of simulation models. In order to explore the potential of coupling optimization and simulation techniques in different job shop scheduling scenarios, this paper presents some of the ideas on an ongoing research project developing an SBO strategy coupling genetic algorithm and discrete-event simulation. Furthermore, this paper describes an approach to aid in the analysis of computed schedule feasibility subject to stochastic behavior, which is the case for most of the real world industries. One of the research goals is to provide an efficient and effective way to evaluate schedule robustness and to find robust schedules. The research may significantly contribute to businesses where scheduling changes are expensive, like in airline and train companies and automakers industries and suppliers. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.