Multi-product continuous plant scheduling: combination of decomposition, genetic algorithm, and constructive heuristic

Abstract

We propose a polylithic method for medium-term scheduling of a large-scale industrial plant operating in a continuous mode. The method combines a decomposition approach, a genetic algorithm (GA) and a constructive MILP-based heuristic. In the decomposition, decisions are made at two levels, using the rolling horizon approach. At the upper level, a reduced set of products and the time period is chosen to be considered in the lower level. At the lower level, a short-term scheduling MILP-model with event-based representation is used. A heuristic solution to the lower level problem is found using a constructive Moving Window heuristic guided by a genetic algorithm. The GA is applied for finding efficient utilisation of critical units in the lower level problem. For solving the one unit scheduling problem, a parallel dynamic programming algorithm is proposed. Implementation of the dynamic programming algorithm for a graphics processing unit (GPU) is incorporated in the GA for improving its performance. The experimental study of the proposed method on a real case of a large-scale plant shows a significant improvement of the solution quality and the solving time comparing to the pure decomposition algorithm proposed in the earlier study, and confirmed suitability of the proposed approach for the real-life production scheduling. In particular, the reduction of the number of changeovers and their duration in the obtained solution as well as the CPU time of solving the problem was about 60% using the new approach.

Keywords:

• process planning
• mixed integer linear programming
• genetic algorithms
• dynamic programming
• greedy algorithms
• GPU
• polylithic approach

Acknowledgments

The authors would like to thank Dr M. Bussieck (GAMS GmbH) for his help with GAMS and Dr H.-J. Pitz (BASF SE) for the stimulating discussions.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Anton Eremeev http://orcid.org/0000-0001-5289-7874

Additional information

Funding

The research was supported by the Russian Academy of Science (the Program of basic research) project 0314-2019-0019.