A reinforcement learning approach to the stochastic cutting stock problem

We propose a formulation of the stochastic cutting stock problem as a discounted infinite-horizon Markov decision process. At each decision epoch, given current inventory of items, an agent chooses in which patterns to cut objects in stock in anticipation of the unknown demand. An optimal solution c...

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Huvudupphovsmän: Anselmo R. Pitombeira-Neto, Arthur H.F. Murta
Materialtyp: Artigo
Språk:Inglês
Publicerad: Elsevier 2022-01-01
Serie:EURO Journal on Computational Optimization
Ämnen:
Cutting stock problem
Reinforcement learning
Approximate dynamic programming
Länkar:http://www.sciencedirect.com/science/article/pii/S219244062200003X
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