Name
Title | ||
|---|---|---|
Solving the mixed model sequencing problem with reinforcement learning and metaheuristics |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.cie.2021.107704 | COMPUTERS & INDUSTRIAL ENGINEERING |
Keywords | DocType | Volume |
Scheduling, Mixed model sequencing, Reinforcement learning, Metaheuristics, Mixed-integer linear programming | Journal | 162 |
ISSN | Citations | PageRank |
0360-8352 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Janis Brammer | 1 | 0 | 0.68 |
| Bernhard Lutz | 2 | 1 | 2.03 |
| Dirk Neumann | 3 | 0 | 0.34 |