Parallel Metaheuristics for Shop Scheduling: enabling Industry 4.0

Abstract

Production scheduling is one of the most critical activities in manufacturing. Under the context of Industry 4.0 paradigm, shop scheduling becomes even more complex. Metaheuristics present the potential to solve these harder problems but demand substantial computational power. The use of high-performance parallel architectures, present in cloud computing and edge computing, may support the develop of better metaheuristics, enabling Industry 4.0 with solution techniques to deal with their scheduling complexity. This study provides an overview of parallel metaheuristics for shop scheduling in recent literature. We reviewed 28 papers and classified them, according to parallel architectures, shop configuration, metaheuristics and optimization criteria. The results support that parallel metaheuristic have potential to tackle Industry 4.0 scheduling problems. However, it is essential to extend the research to the cloud and edge computing, flexible shop configurations, dynamic problems with multi-resource, and multi-objective optimization. Future studies should consider the use of real-world data instances.