A significant issue for the production sector was the complicated scheduling requirement due to shorter product life cycles and unexpected fluctuations. Scheduling has a significant effect on the ability of a manufacturing system to meet the deadlines and the schedule should be reactive to resolve disturbances during operation. Yet, job shop scheduling issues are nondeterministic polynomial time - hard (NP-hard). This research will address some aspects of combining simulation and optimization-based algorithms for job-shop scheduling and rescheduling of flexible production systems. The predictive part determines the feasible schedule to be used for a flow shop which is generated using a combination of rule-based simulation and optimization: first, using the optimization algorithm to compute a rough plan, followed by using a rule based simulation system to locally fine tune the plan to obtain the final schedule. The schedule obtained will be implemented to the real-world system which is adapted by the reactive part of the system. The results had proved that the predictive-reactive scheduling can effectively increase the effectiveness of flexible production system. It would be a promising approach to combine the advantages of simulation with optimization algorithm.
|Journal of Advanced Manufacturing Technology
|Early online date
|30 Dec 2020
|Early online date - 30 Dec 2020
Bibliographical noteFunding Information:
Authors are grateful to Universiti Teknikal Malaysia Melaka for the financial support through FRGS/1/2017/TK03/FKP-SMC/F00342.
© 2020. All Rights Reserved.
Copyright 2021 Elsevier B.V., All rights reserved.
- Genetic Algorithm
- Job-Shop Scheduling
ASJC Scopus subject areas
- Automotive Engineering
- Hardware and Architecture
- Computer Networks and Communications
- Control and Optimization
- Industrial and Manufacturing Engineering
- Management of Technology and Innovation