Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/286736 
Year of Publication: 
2021
Citation: 
[Journal:] OR Spectrum [ISSN:] 1436-6304 [Volume:] 44 [Issue:] 1 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2021 [Pages:] 29-56
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
In this study, we propose a reinforcement learning (RL) approach for minimizing the number of work overload situations in the mixed model sequencing (MMS) problem with stochastic processing times. The learning environment simulates stochastic processing times and penalizes work overloads with negative rewards. To account for the stochastic component of the problem, we implement a state representation that specifies whether work overloads will occur if the processing times are equal to their respective 25%, 50%, and 75% probability quantiles. Thereby, the RL agent is guided toward minimizing the number of overload situations while being provided with statistical information about how fluctuations in processing times affect the solution quality. To the best of our knowledge, this study is the first to consider the stochastic problem variation with a minimization of overload situations.
Subjects: 
Scheduling
Mixed model sequencing
Reinforcement learning
Metaheuristics
Combinatorial optimization
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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