Distributed job-shop rescheduling problem considering reconfigurability of machines: a self-adaptive hybrid equilibrium optimiser

Abstract

The recent trend of globalisation of the economy has been accelerated thanks to emerging new communication technologies. This forces some companies to be adapted to rapidly changing market requirements utilising a multi-factory production network. Job scheduling in such a distributed manufacturing system, is significantly complicated especially in the presence of dynamic events. Furthermore, production systems need to be flexible to timely react to the imposed changes. Hence, reconfigurable machine tools (RMTs) can be used as a resource for flexibility in manufacturing systems. This paper deals with a distributed job-shop rescheduling problem, in which the facilities benefit from reconfigurable machines. Firstly, the problem is mathematically formulated to minimise total weighted lateness in a static state. Then, the dynamic version is extent based on a designed conceptual framework of rescheduling module to update the current schedule. Since the problem is NP-hard, a self-adaptive hybrid equilibrium optimiser algorithm is proposed. The experiments show that the proposed EO algorithm is extremely efficient. Finally, a simulation-optimisation model is developed to evaluate the performance of the manufacturing system facing stochastic arriving jobs. The obtained results show that the production system can be very flexible relying on its distributed facilities and reconfigurable machines.

KEYWORDS:

• Distributed manufacturing
• reconfigurable machine tools
• dynamic scheduling
• cyber-physical shop floor
• simulation-optimisation model
• meta-heuristic algorithms

Disclosure statement

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

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Notes on contributors

M. Mahmoodjanloo

Mehdi Mahmoodjanloo is a Ph.D. candidate in Industrial Engineering at College of Engineering, University of Tehran in Iran. He obtained his M.Sc. and B.Sc. degrees in Industrial Engineering from Amirkabir University of Technology (Tehran Polytechnic) and Sharif University of Technology, respectively. He also does his research as a visiting researcher at LIRIS laboratory, University of INSA Lyon in France. His research interests include scheduling in manufacturing systems, applied operations research, logistics optimisation, and machine learning. He has published several papers in reputable journals and international conferences.

R. Tavakkoli-Moghaddam

Reza Tavakkoli-Moghaddam is a Professor of Industrial Engineering at the College of Engineering, University of Tehran, Iran. He obtained his Ph.D., M.Sc. and B.Sc. degrees in Industrial Engineering from the Swinburne University of Technology in Melbourne (1998), the University of Melbourne in Melbourne (1994), and the Iran University of Science and Technology in Tehran (1989), respectively. He serves as the Editor-in-Chief of the Journal of Industrial Engineering published by the University of Tehran and as the Editorial Board member of nine reputable academic journals. He is the recipient of the 2009 and 2011 Distinguished Researcher Awards and the 2010 and 2014 Distinguished Applied Research Awards at the University of Tehran, Iran. He has been selected as the National Iranian Distinguished Researcher in 2008 and 2010 by the MSRT (Ministry of Science, Research, and Technology) in Iran. He has obtained an outstanding rank as the top 1% scientist and researcher in the world elite group since 2014. He also received the Order of Academic Palms Award as a distinguished educator and scholar for the insignia of Chevalier dans l’Ordre des Palmes Academiques by the Ministry of National Education of France in 2019. He has published 5 books, 32 book chapters, and more than 1000 journal and conference papers.

A. Baboli

Armand BABOLI is currently an Associate Professor in the Department of Industrial Engineering of INSA Lyon-France (National Institute of Applied Sciences) and he carries out his research activities in LIRIS laboratory (Laboratoire d'InfoRmatique en Image et Systèmes d'information). His recent research focuses on intelligence, robustness, flexibility, and agility in the design, configuration, and optimisation of production systems and the supply chain, as well as the evolution of traditional production systems into intelligent production systems (the factory of the future & industry 4.0). He is currently working on the decision support system & methods, called ‘dynamic predictive decision making’. He develops the methods and tools by combining the concepts that come from data science, operation research, and simulation. He is the creator and principal investigator of several industrial collaborations. Actually, he is Chair of three long projects with Dassault FalconJet-USA (2015-2021) concerning ‘smart supply chain’, Fiat Powertrain Technologies (2015-2022) concerning ‘Transformation of the production system towards industry 4.0’, and Volvo Group (2018-2024) concerning ‘Dynamic predictive production planning sequencing and line balancing’.

A. Bozorgi-Amiri

Ali Bozorgi-Amiri is an Associate Professor in the School of Industrial Engineering, University of Tehran, Iran. His research interests include: Sustainable and resilient supply chain network design, Humanitarian and disaster relief assistance, Business process redesign, Multi-criteria decision-making techniques, and Stochastic programming. He has published several papers in the related areas in refereed journals and conferences.