IFAC PapersOnLine 50-1 (2017) 7953–7958 ScienceDirect ScienceDirect Available online at www.sciencedirect.com 2405-8963 © 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2017.08.896 © 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Schedule robustness; Production planning and control under uncertainty; Job and activity scheduling 1. INTRODUCTION The specification of efficient production schedules is important for job shop manufacturing systems execution. Each order has to be assigned to an available machine so that specific performance measurements are improved. However, the increasing customization of products requires that production handles higher numbers of product variants along with decreasing lot sizes. Furthermore, to ensure process quality and speed, several specialized machines are employed. Hence, job shop manufacturing systems become more complex and the efficient management of these systems requires reliable and robust scheduling methods. In general, the computation of schedules employs optimization techniques, such as mixed-integer programming (Mula et al., 2010) but do not consider schedule stability or robustness. Despite the increasing power of modern solvers, most real manufacturing systems are still too complex to be properly represented, modeled and solved without ample simplifications (they belong to the class of NP-hard optimization problems). As a consequence, the optimal solution often cannot be computed or requires long computation times. Therefore, frequently heuristic methods are applied. These methods cannot guarantee optimality but are often able to generate near-optimal solutions in relatively short computation times (Papadimitriou, 2003). One of the simplest heuristic scheduling approaches is the use of dispatching rules, assigning a specific priority value to each job (or order) in the queue of a machine according to some predefined criteria, such as the remaining time until its due date (Pickardt and Branke, 2012; Rajendran and Holthaus, 1999). Whenever a suitable machine is available, the job with the highest priority is chosen for the next operation. Due to the low implementation effort, dispatching rules are often used. A more sophisticated approach, which offers more flexibility, is the use of meta-heuristics, such as genetic algorithms. Genetic algorithms are able to compute solutions also for larger instances of combinatorial problems than exact optimization methods. However, they also feature limitations, such as the dependence on the choice of several parameters and the possibility to converge towards local extrema (Jungwattanakit et al., 2008). Moreover, both, exact optimization methods as well as meta-heuristics are not able to deal with two particular characteristics of production systems: dynamics and randomness (stochasticity), which can greatly compromise the initial production schedule. During production, it is well known that random events occur affecting the adherence to a production schedule. Indeed, production systems can be regarded as dynamic systems that are subject to internal as well as external dynamic and unexpected factors, such as: (i) changes and delays in raw/component materials arrival from suppliers; (ii) unacceptable quality for raw/component materials from suppliers or during production, leading to reprocessing, reworks and reordering, and consequently to delays; (iii) unexpected production resources (machines, tools, castings etc.) failures, which require stops for maintenance and repair; (iv) workers absenteeism; (v) need for extra cleaning of production resources and other unexpected setup activities (Vieira et al., 2003; Vieira et al., 2009a; Vieira et al., 2009b; Scholz-Reiter et al., 2002). *Industrial and Systems Engineering Department, Federal University of Santa Catarina, Florianopolis, Brazil **BIBA - Bremer Institut für Produktion und Logistik GmbH at the University of Bremen, Bremen, Germany ***Department of Production Engineering, University of Bremen, Bremen, Germany (e-mail: 1. g.vieira@ufsc.br, 2. r . kue@biba.uni-bremen.de, 3. enzo.frazzon@ufsc.br, 4. fre@biba.uni-bremen.de) Abstract: 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. Guilherme Ernani Vieira* ,1 , Mirko Kück** ,2 , Enzo Frazzon* ,3 , Michael Freitag** , ***, 4 Evaluating the Robustness of Production Schedules using Discrete-Event Simulation