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.

Machine Learning and Optimization for Production Rescheduling in Industry 4.0 / Li, Yuanyuan; Carabelli, Stefano; Fadda, Edoardo; Manerba, Daniele; Tadei, Roberto; Terzo, Olivier. - In: THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY. - ISSN 1433-3015. - 110:(2020), pp. 2445-2463. [10.1007/s00170-020-05850-5]

Machine Learning and Optimization for Production Rescheduling in Industry 4.0

Stefano Carabelli;Edoardo Fadda;Roberto Tadei;
2020

Abstract

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.
File in questo prodotto:
File Dimensione Formato  
I40for_Advanced_Manufacturing_Technology.pdf

accesso aperto

Tipologia: 1. Preprint / submitted version [pre- review]
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 879.3 kB
Formato Adobe PDF
879.3 kB Adobe PDF Visualizza/Apri
Fadda-Machine.pdf

accesso aperto

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Creative commons
Dimensione 1.72 MB
Formato Adobe PDF
1.72 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2842141