Machine learning and optimization for production rescheduling in Industry 4.0
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ORIGINAL ARTICLE
Machine learning and optimization for production rescheduling in Industry 4.0 Yuanyuan Li1 · Stefano Carabelli2 · Edoardo Fadda2,4 · Daniele Manerba3
· Roberto Tadei2 · Olivier Terzo1
Received: 2 February 2020 / Accepted: 30 July 2020 © The Author(s) 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. Keywords Industry 4.0 · Flexible job-shop scheduling · Rescheduling · Machine learning classification · Optimization algorithms · Real-time data analysis
1 Introduction The fourth industrial revolution, or Industry 4.0 (I4.0) for short, allows decision-makers to obtain real-time information from various plant components and machines to communicate with each other. I4.0 can, therefore, be viewed as the application of the Internet of Things (IoT) to industrial production (IIoT). Daniele Manerba
[email protected] 1
LINKS Foundation, via Pier Carlo Boggio 61, 10138, Turin, Italy
2
Department of Control and Computer Engineering, Politecnico di Torino, corso Duca degli Abruzzi 24, 10129, Turin, Italy
3
Department of Information Engineering, Universit`a degli Studi di Brescia, via Branze 38, 25123, Brescia, Italy
4
ICT for City Logistics and Enterprises Lab, Politecnico di Torino, corso Duca degli Abruzzi 24, 10129, Turin, Italy
The exploitation of new data sources to improve system understanding, as well as its management, is a common trend in several fields (e.g., see [1] for an application in a generic industrial project, [2] in waste collection, [3] in fleet management, and [4] in the gig economy). This trend is even more promising if we observe
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