Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/316982 
Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Operations Research Forum [ISSN:] 2662-2556 [Volume:] 5 [Issue:] 1 [Article No.:] 20 [Publisher:] Springer International Publishing [Place:] Cham [Year:] 2024
Verlag: 
Springer International Publishing, Cham
Zusammenfassung: 
Abstract Reinforcement learning (RL) algorithms have proven to be useful tools for combinatorial optimisation. However, they are still underutilised in facility layout problems (FLPs). At the same time, RL research relies on standardised benchmarks such as the Arcade Learning Environment. To address these issues, we present an open-source Python package (gym-flp) that utilises the OpenAI Gym toolkit, specifically designed for developing and comparing RL algorithms. The package offers one discrete and three continuous problem representation environments with customisable state and action spaces. In addition, the package provides 138 discrete and 61 continuous problems commonly used in FLP literature and supports submitting custom problem sets. The user can choose between numerical and visual output of observations, depending on the RL approach being used. The package aims to facilitate experimentation with different algorithms in a reproducible manner and advance RL use in factory planning.
Schlagwörter: 
Combinatorial optimisation
Artificial intelligence
OpenAI
Production management
Factory planning
Simulation
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