Fabricatio-Rl: A Reinforcement Learning Simulation Framework For Production Scheduling

Rinciog, Alexandru; Meyer, Anne Marie

doi:10.1109/wsc52266.2021.9715366

Cited by 5 publications

(1 citation statement)

References 20 publications

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“…Applied research often considers an additional dimension in the problem formulation inspired by real-world use-cases, such as stochasticity [13,14], machine flexibility [15][16][17], dynamic job releases [18], machine failures [19] or multi-objective optimization criteria [20,21]. These studies show the general feasibility of DRL to learn, but are typically not very competitive with expert systems.…”

Section: Deep Reinforcement Learning For Job Shop Scheduling Problemsmentioning

confidence: 99%

Dynamic Storage Location Assignment in Warehouses Using Deep Reinforcement Learning

Puiseau

Nanfack²,

Tercan

et al. 2022

Technologies

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The warehousing industry is faced with increasing customer demands and growing global competition. A major factor in the efficient operation of warehouses is the strategic storage location assignment of arriving goods, termed the dynamic storage location assignment problem (DSLAP). This paper presents a real-world use case of the DSLAP, in which deep reinforcement learning (DRL) is used to derive a suitable storage location assignment strategy to decrease transportation costs within the warehouse. The DRL agent is trained on historic data of storage and retrieval operations gathered over one year of operation. The evaluation of the agent on new data of two months shows a 6.3% decrease in incurring costs compared to the currently utilized storage location assignment strategy which is based on manual ABC-classifications. Hence, DRL proves to be a competitive solution alternative for the DSLAP and related problems in the warehousing industry.

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Section: Deep Reinforcement Learning For Job Shop Scheduling Problemsmentioning

confidence: 99%

Dynamic Storage Location Assignment in Warehouses Using Deep Reinforcement Learning

Puiseau

Nanfack²,

Tercan

et al. 2022

Technologies

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show abstract

SLAPStack: A Simulation Framework and a Large-Scale Benchmark Use Case for Autonomous Block Stacking Warehouses

Pfrommer

Rinciog

Zahid

et al. 2022

Lecture Notes in Computer Science

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Towards Standardising Reinforcement Learning Approaches for Production Scheduling Problems

Rinciog

Meyer

2022

Procedia CIRP

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Fabricatio-Rl: A Reinforcement Learning Simulation Framework For Production Scheduling

Cited by 5 publications

References 20 publications

Dynamic Storage Location Assignment in Warehouses Using Deep Reinforcement Learning

Dynamic Storage Location Assignment in Warehouses Using Deep Reinforcement Learning

SLAPStack: A Simulation Framework and a Large-Scale Benchmark Use Case for Autonomous Block Stacking Warehouses

Towards Standardising Reinforcement Learning Approaches for Production Scheduling Problems

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