Publikation im Journal "Computers & Industrial Engineering"

23. September 2021

Unsere Studie "Solving the mixed model sequencing problem with reinforcement learning and metaheuristics" wurde bei der Fachzeitschrift Computers & Industrial Engineering zur Publikation angenommen. Die Studie stellt einen Reinforcement Learning Ansatz für ein kombinatorisches Optimierungsproblem vor, wo die durch Reinforcement Learning erstellte Lösung als Initialisierung für verschiedene Metaheuristiken dient.

Autoren: Janis Brammer, Bernhard Lutz, Dirk Neumann

Abstract:

This study presents a reinforcement learning (RL) approach for the mixed model sequencing (MMS) problem with a minimization of work overload situations. The proposed approach generates the sequence in a constructive way, so that an action denotes the model to be sequenced next. The trained policy quickly creates an initial sequence, which allows us to use the cutoff time to further improve the solution quality with a metaheuristic. Our numerical evaluation based on an existing benchmark dataset shows that our approach is superior to established methods if the demand plan follows its expected distribution from the learning process.