Machine learning and optimization for production rescheduling in Industry 4.0

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.

Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study

“…However, most metaheuristic algorithms are non-deterministic and require long solution times for large problem sizes (Maoudj et al, 2019). Recent studies explore the use of ML to address these limitations and to support more efficient use of metaheuristics, for example, by using ML for the reduction of the solution space for metaheuristics (Bouzary et al, 2021) or for identifying when it is beneficial to rerun the metaheuristic (Li et al, 2020). Bouzary et al (2021) propose a combination of support vector machines and genetic algorithm for addressing the service composition problem in a cloud manufacturing context, where they use ML for identifying the solution space for the metaheuristic.…”

“…Bouzary et al (2021) propose a combination of support vector machines and genetic algorithm for addressing the service composition problem in a cloud manufacturing context, where they use ML for identifying the solution space for the metaheuristic. Li et al (2020) use tabu search and genetic algorithm for schedule optimization, and a random forest classifier for identifying instances when production should be rescheduled based on whether the metaheuristic is likely to yield a more efficient schedule than the one available.…”

A Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling Flexible Job-Shop Systems in Industry 4.0 Context

“…Recently, Li et al [16] developed an optimization algorithm to solve the FJSP in the context of a smart production plant. Ghaleb et al [17] proposed a real-time scheduling model for the FJSP.…”

Reformist Framework for Improving Human Security for Mobile Robots in Industry 4.0

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