Reactive scheduling approach for solving a realistic flexible job shop scheduling problem

ABSTRACT

Reactive Scheduling (RS) and the realistic Flexible Job Shop Scheduling Problem (FJSSP) are of major importance for the implementation of real-world manufacturing systems. The present study proposes a scheduling rules-based surrogate assisted simulation-optimisation approach for solving a combinatorial optimisation problem related to a realistic FJSSP. The proposed approach aims to capture the dynamic nature of the FJSSP and to balance both short-term reactivity facing repetitive perturbations and the overall performance of manufacturing systems. Besides and to enhance the optimisation process, a GA-based computational procedure allows managing the use of a hybrid neuronal surrogate and DES model for the accurate and fast calculation of the fitness function, considering the Makespan minimisation criterion and dealing with rush orders. The approach is applied to a highly automated Flexible robotised Manufacturing System (FMS) integrating different realistic and representative constraints to the classical FJSSP. Computational simulations and comparisons demonstrate that the proposed approach shows competitive performances compared to other resolution models, considering obtained solutions quality and short-term reactivity. The proposed resolution model provides technical tools for future control systems and allows for the practical implementation of customised assembly systems in Industry 4.0, relying on innovative emerging technologies.

KEYWORDS:

• Simulation optimisation
• scheduling and dispatching rules
• genetic algorithm
• surrogate model
• artificial neural network
• metaheuristics

Disclosure statement

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

Additional information

Notes on contributors

B. Mihoubi

Bachir Mihoubi received a master's degree in Telecommunication from Amar Thelidji University, Laghouat, Algeria in 2012. He is currently a PhD student in University of Annaba, Algeria and a Research Associate at the Intelligent Manufacturing and Robotic division, Advanced Technologies Development Center, Algeria. Her research interests include Cyber-Physical Manufacturing Systems design and control, Artificial intelligence, Scheduling and control in industry 4.0 and evolutionary computation and optimization.

B. Bouzouia

Dr Brahim Bouzouia is, at present, a Research Director and lead of industry 4.0 research team, at the “Centre de Développement des Technologies avancées (CDTA)” of Algiers, since 1989. He was also the CEO the “CDTA” from 2010 to 2014. He obtained his PhD on Robotics and Automation, in 1989, from Centre National de Recherche Scientifique (CNRS) and Paul Sabatier university, France. His main domains of interest are related to Robotics, Industry 4.0, Intelligent manufacturing System, Advanced Automation, etc.

M. Gaham

Mehdi Gaham received the Doctorate in Sciences degree from USTHB University, Algeria in 2019. He is currently a Research Associate at the Intelligent Manufacturing and Robotic division, Advanced Technologies Development Center, Algeria. Her research interests include Cyber-Physical Collaborative Robotic and Manufacturing Systems design and control, Human-centric CPPS, Scheduling and control in industry 4.0 and evolutionary computation and optimization.