Joint optimisation for dynamic flexible job-shop scheduling problem with transportation time and resource constraints

Weibo RenIndustrial and Systems Engineering Laboratory, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, People’s Republic of ChinaView further author information

Yan YanIndustrial and Systems Engineering Laboratory, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, People’s Republic of ChinaView further author information

Yaoguang HuIndustrial and Systems Engineering Laboratory, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, People’s Republic of ChinaCorrespondence[email protected]
View further author information

Yu GuanIndustrial and Systems Engineering Laboratory, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, People’s Republic of ChinaView further author information

Abstract

Dynamic flexible job-shop scheduling is traditionally a challenge in real-world manufacturing systems, especially considering the constraints of transportation resources and transportation time. To address the dynamic optimisation problem in flexible manufacturing systems, this paper proposes a novel proactive-reactive methodology to adapt to the dynamic changes in working environments and addresses the joint scheduling problem for machine tools and transportation resources. The joint optimisation model is first formulated as a mixed-integer programming model considering production efficiency and transportation constraints. The flowchart of the dynamic scheduling system is then designed for dynamic decision-making, and a novel particle swarm optimisation algorithm integrated with genetic operators is developed to respond to dynamic events and generate the reschedule plan in time. Finally, several numerical experiments and case studies in reality are applied to verify the efficiency of the developed methodology. Common dispatching rules and heuristic methods are also applied to test and evaluate the efficiency of the developed algorithm. Computational results demonstrate that the developed methods and decision models are efficient for dynamic job-shop scheduling problems in flexible manufacturing systems, which can acquire rather a good effect in practical production.

KEYWORDS:

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors would like to thank the National Key R&D Program of China [grant number 2019YFB1704001, 2020YFB1709601] and National Natural Science Foundation of China [grant number 51675051].

Notes on contributors

Weibo Ren

Weibo Ren is a DE (Doctor of Engineering) student in School of Mechanical Engineering at Beijing Institute of Technology. His primary research focussed on Operation Research in Industrial Production and Service System.

Yan Yan

Yan Yan received her Ph.D. in Beijing Institute of Technology, and is Professor in the School of Mechanical Engineering at Beijing Institute of Technology. Her primary research focuses on Knowledge Engineering and Operation Research in Manufacturing system.

Yaoguang Hu

Yaoguang Hu received his Ph.D. in Beihang University and is Professor in the School of Mechanical Engineering at Beijing Institute of Technology. His primary research focuses on smart manufacturing system and Industrial Product-Service Systems.

Yu Guan

Yu Guan is a Ph.D. student in School of Mechanical Engineering at Beijing Institute of Technology. His primary research focussed on smart manufacturing system and industrial engineering.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.