Planning on Discrete Event Systems Using Parallelism Maximization

TL;DR

This paper introduces heuristics based on maximizing parallelism within Supervisory Control Theory to improve production planning in Discrete Event Systems, effectively reducing makespan and computation time.

Contribution

It develops novel heuristics leveraging parallelism maximization within Supervisory Control Theory for more efficient production planning in discrete event systems.

Findings

Heuristics achieve optimal solutions in all tested batch sizes.

Significant reduction in makespan compared to baseline methods.

Demonstrated computational efficiency in case study.

Abstract

This work deals with the production planning problem in Discrete Event Systems, using the Supervisory Control Theory to delimit the search universe and developing two heuristics based on the maximization of the parallelism to find sequences that minimize makespan. The role of the Supervisory Control Theory is to provide the set of all safe production sequences, given by the closed loop behavior. Although the use of heuristics does not provide necessarily the absolute optimal solution in general, we present a case study where it happens for all batch sizes. The efficiency in terms of computation time is also illustrated by the case study.