Event-Driven Production Rescheduling in Job Shop Environments

Pfitzer, Florian; Provost, Julien; Mieth, Carina; Liertz, Wolfgang

doi:10.1109/coase.2018.8560523

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…This new vision is also in contrast with the old MES-based central control architecture. Proposals of event-driven rescheduling have been performed, for the use cases of metal sheet production lines [49]. • Lastly, a further scenario regarding disassembly can be brought forward.…”

Section: Background Informationmentioning

confidence: 99%

MES-integrated digital twin frameworks

Negri

Berardi

Fumagalli

et al. 2020

Journal of Manufacturing Systems

125

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Section: Background Informationmentioning

confidence: 99%

MES-integrated digital twin frameworks

Negri

Berardi

Fumagalli

et al. 2020

Journal of Manufacturing Systems

125

View full text Add to dashboard Cite

Framework for the usage of data from real-time indoor localization systems to derive inputs for manufacturing simulation

2019

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Advancing Sustainable Manufacturing: Reinforcement Learning with Adaptive Reward Machine Using an Ontology-Based Approach

Golpayegani,

Ghanadbashi,

Zarchini

2024

Sustainability

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Sustainable manufacturing practices are crucial in job shop scheduling (JSS) to enhance the resilience of production systems against resource shortages and regulatory changes, contributing to long-term operational stability and environmental care. JSS involves rapidly changing conditions and unforeseen disruptions that can lead to inefficient resource use and increased waste. However, by addressing these uncertainties, we can promote more sustainable operations. Reinforcement learning-based job shop scheduler agents learn through trial and error by receiving scheduling decisions feedback in the form of a reward function (e.g., maximizing machines working time) from the environment, with their primary challenge being the handling of dynamic reward functions and navigating uncertain environments. Recently, Reward Machines (RMs) have been introduced to specify and expose reward function structures through a finite-state machine. With RMs, it is possible to define multiple reward functions for different states and switch between them dynamically. RMs can be extended to incorporate domain-specific prior knowledge, such as task-specific objectives. However, designing RMs becomes cumbersome as task complexity increases and agents must react to unforeseen events in dynamic and partially observable environments. Our proposed Ontology-based Adaptive Reward Machine (ONTOADAPT-REWARD) model addresses these challenges by dynamically creating and modifying RMs based on domain ontologies. This adaptability allows the model to outperform a state-of-the-art baseline algorithm in resource utilization, processed orders, average waiting time, and failed orders, highlighting its potential for sustainable manufacturing by optimizing resource usage and reducing idle times.

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Event-Driven Production Rescheduling in Job Shop Environments

Cited by 3 publications

References 10 publications

MES-integrated digital twin frameworks

MES-integrated digital twin frameworks

Framework for the usage of data from real-time indoor localization systems to derive inputs for manufacturing simulation

Advancing Sustainable Manufacturing: Reinforcement Learning with Adaptive Reward Machine Using an Ontology-Based Approach

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