Publication in the Journal "OR Spectrum"

31 August 2021

Our study "Stochastic mixed model sequencing with multiple stations using reinforcement learning and probability quantiles" has been accepted for publication at OR Spectrum. The study presents a reinforcement learning approach for the mixed model sequencing problem with stochastic processing times. The state representation provides the agent with distributional knowledge according to several probability quantiles. Thereby, the agent is able to learn how different values from the distribution affect the solution quality.

Authors: Janis Brammer, Bernhard Lutz, Dirk Neumann

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 species 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 towards 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.