Multi-Agent System Model for Dynamic Scheduling in Flexibile Job Shops

Ebufegha, Akposeiyifa; Li, Simon

doi:10.1109/wsc52266.2021.9715441

Cited by 5 publications

(1 citation statement)

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“…One assumption in their study is that there is no disruption to the availability of current jobs. Ebufegha and Li (2021) developed a simulation model of an intelligent manufacturing system (flexible job shop) where machines and parts are explicitly modeled as autonomous agents to negotiate and make real-time adjustments to the planned operations to maximize machine utilization and reduce part flow.…”

Section: Related Workmentioning

confidence: 99%

Effect of Real-Time Truck Arrival Information on the Resilience of Slot Management Systems

Vanga

Maknoon

Tavasszy

et al. 2022

2022 Winter Simulation Conference (WSC)

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Traffic congestion is uncertain and undesirable in logistics and leads to arrival uncertainty at downstream locations engendering disruptions. This paper considers a loading facility that uses Truck Appointment System (TAS) for slot management and faces incoming truck arrival uncertainty due to traffic congestion. Due to the recent advancements in cyber-physical systems, we propose an adaptive system that uses the real-time truck Estimated Time of Arrival (ETA) data to make informed decisions. We develop an integer mathematical model to represent the adaptive behavior that determines the optimal reschedules by minimizing the average truck waiting time. We developed a simulation model of the adaptive system and reported the estimated benefits from our initial experiments.

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Section: Related Workmentioning

confidence: 99%