A multi-agent system simulation based approach for collision avoidance in integrated Job-Shop Scheduling Problem with transportation tasks

Sanogo, Kader; Benhafssa, Abdelkader Mekhalef; Sahnoun, M’hammed; Bettayeb, Belgacem; Abderrahim, Moussa; Bekrar, Abdelghani

doi:10.1016/j.jmsy.2023.03.011

Cited by 10 publications

(1 citation statement)

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“…The transportation times of products between machines introduce delays that directly affect the launch dates of subsequent tasks and the overall makespan [7]. Internal transport tasks in today's automated factories are performed by robotic vehicles, which involves challenges and interesting issues for their optimization [8]. Moreover, our model considers operator flexibility, enabling them to switch between machines, which may introduce additional time delays and affect their fatigue levels [9].…”

Section: Introductionmentioning

confidence: 99%

NSGA-II for Solving a Multi-objective, Sustainable and Flexible Job Shop Scheduling Problem

Destouet,

Tlahig,

Bettayeb

et al. 2023

IFIP Advances in Information and Communication Technology

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Recently, sustainable scheduling has emerged to make the trade-off between economic, environmental and social factors. While classical scheduling research focuses on economic and environmental indicators such as makespan and energy consumption, modern human-centred manufacturing systems consider additional important factors such as workers' well-being and ergonomic risks. This study proposes a multiobjective mathematical model that jointly optimizes the makespan, the total energy consumption, and the OCRA (Occupational Repetitive Actions) index (a measure of ergonomic risk) in the case of a flexible job shop. The model has the originality of also taking into account the travel times of operators and products between machines. We use the NSGA-II method to solve it. The first results allow us to analyze the mutual influences of the criteria and demonstrate the advantage of such a method to obtain a good compromise between the three objectives in a reasonable resolution time.

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Section: Introductionmentioning

confidence: 99%