Smart Short Term Capacity Planning: A Reinforcement Learning Approach - Artificial Intelligence for Sustainable and Resilient Production Systems PART IV, IFIP WG 5.7 International Conference, APMS 2021 Access content directly
Conference Papers Year : 2021

Smart Short Term Capacity Planning: A Reinforcement Learning Approach

Abstract

Capacity planning is an important production control function that significantly influences firm performance. Especially, in the short term, we face a dynamically changing system which calls for an adaptive capacity planning system that reacts based on the current state of the shop floor. Thus, this paper analyzes the performance of a reinforcement learning (RL) algorithm for overtime planning for a make-to-order job shop. We compare the performance of the RL algorithm to mechanisms that set overtime-hours statically or randomly over time. Performance is measured in total costs which consist of overtime, holding and backorder costs. The results show that our tested benchmarks can be outperformed by the RL algorithm, where the major savings were achieved due to less needed overtime.
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hal-04030409 , version 1 (16-03-2023)

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Manuel Schneckenreither, Sebastian Windmueller, Stefan Haeussler. Smart Short Term Capacity Planning: A Reinforcement Learning Approach. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2021, Nantes, France. pp.258-266, ⟨10.1007/978-3-030-85874-2_27⟩. ⟨hal-04030409⟩
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