Bi-local search based variable neighborhood search for job-shop scheduling problem with transport constraints
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Bi-local search based variable neighborhood search for job-shop scheduling problem with transport constraints Moussa Abderrahim1 · Abdelghani Bekrar2 · Damien Trentesaux2 · Nassima Aissani3 · Karim Bouamrane1 Received: 12 April 2020 / Accepted: 16 November 2020 © The Author(s) 2020
Abstract In job-shop manufacturing systems, an efficient production schedule acts to reduce unnecessary costs and better manage resources. For the same purposes, modern manufacturing cells, in compliance with industry 4.0 concepts, use material handling systems in order to allow more control on the transport tasks. In this paper, a job-shop scheduling problem in vehicle based manufacturing facility that is mainly related to job assignment to resources is addressed. The considered job-shop production cell has two types of resources: processing resources that accomplish fabrication tasks for specific products, and transporting resources that assure parts’ transport to the processing area. A Variable Neighborhood Search algorithm is used to schedule product manufacturing and handling tasks in the aim to minimize the maximum completion time of a job set and an improved lower bound with new calculation method is presented. Experimental tests are conducted to evaluate the efficiency of the proposed approach. Keywords Job-shop scheduling · Transport constraints · Variable neighborhood search
1 Introduction Modern manufacturing facilities that comply with Industry 4.0 use flexible resources to ensure more control on their production lines. This allows production workshops to respond quickly and with a minimum investment to an unexpected growing of activities or compensating resources failures. In this field, flexibilization of the transport system
The first author of this work has been funded by the Algerian Ministry of Higher Education and Scientific Research through the Exceptional National Program scholarship under n.◦ :97/PNE/enseignant/France/2018–2019. The work described in this paper was conducted within the framework of the joint laboratory “SurferLab” founded by Bombardier, Prosyst and the Université Polytechnique Hauts-de-France. This Joint Laboratory is supported by the CNRS, the European Union (ERDF) and the Hauts-de-France region. Extended author information available on the last page of the article
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inside the manufacturing cell is a key element to design a more adaptive production schedule. Technologies enable plants owners to easily reconfigure their process and set new objectives through flexible Material Handling System (MHS). Automated Guided Vehicles (AGV) are commonly chosen by manufacturers to implement truly flexible MHS [1]. They are used for transport and storage functions and can be managed to deal with other manufacturing task schedules to meet desired production objectives initially outlined. In compliance with this goal, an effective task scheduler needs to reorder the realization of a set of operations while considering allocation constraints to the required resources (transport resour
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