Machine learning and optimization for production rescheduling in Industry 4.0

Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2020-10, Vol.110 (9-10), p.2445-2463
Main Authors: Li, Yuanyuan, Carabelli, Stefano, Fadda, Edoardo, Manerba, Daniele, Tadei, Roberto, Terzo, Olivier
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
CAE) and Design
Computer-Aided Engineering (CAD
Decisions
Engineering
Heuristic methods
Industrial and Production Engineering
Industrial applications
Internet of Things
Job shop scheduling
Job shops
Machine learning
Mechanical Engineering
Media Management
Optimization
Original Article
Production management
Rescheduling
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Summary:Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made under uncertainty, it is still a complicated task to decide whether a rescheduling is worthwhile, which is often addressed in practice on a greedy basis. To find a tradeoff between rescheduling frequency and the growing accumulation of delays, we propose a rescheduling framework, which integrates machine learning (ML) techniques and optimization algorithms. To prove the effectiveness, we first model a flexible job-shop scheduling problem with sequence-dependent setup and limited dual resources (FJSP) inspired by an industrial application. Then, we solve the scheduling problem through a hybrid metaheuristic approach. We train the ML classification model for identifying rescheduling patterns. Finally, we compare its rescheduling performance with periodical rescheduling approaches. Through observing the simulation results, we find the integration of these techniques can provide a good compromise between rescheduling frequency and scheduling delays. The main contributions of the work are the formalization of the FJSP problem, the development of ad hoc solution methods, and the proposal/validation of an innovative ML and optimization-based framework for supporting rescheduling decisions.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-020-05850-5