Simulation-based scheduling of a large-scale industrial formulation plant using a heuristics-assisted genetic algorithm

Abstract

Research has brought forth several promising approaches, e.g. Mixed-integer linear programming [8] or Constraint Programming [13] to represent industrial batch production plants and to optimize their production schedules. But still, after decades of research in the field, scheduling is done (semi)-manually in industry in almost all cases. Main reasons for this besides the intrinsic combinatorial complexity are that model development and maintenance require expert knowledge. This work tackles these challenges by using a simulation-based optimization approach in which a Genetic Algorithm provides high-level encodings of schedules and an industrial-strength discrete-event simulator that provides a detailed model of the plant and is used as a fitness evaluator. To enable this approach to solve problems of industrial complexity, the scheduling heuristics are used to reduce the search space to a reasonable size that still contains the important degrees of freedom so that still optimal or at least high-quality solution are obtained. The case study that is considered here is an industrial two-stage formulation plant which is modeled in detail, down to the level of shiftmodels of the operators and mass-balances from source to sink, thus ensuring that the computed schedules are directly applicable at the real-world plant.