Real-Time Selection System of Dispatching Rules for the Job Shop Scheduling Problem

Abstract

Personalized market demands make the job shop scheduling problem (JSSP) increasingly complex, and the need for scheduling methods that can solve scheduling strategies quickly and easily has become very urgent. In this study, we utilized the variety and simplicity of dispatching rules (DRs) and constructed a DR real-time selection system with self-feedback characteristics by combining simulation techniques with decision tree algorithms using makespan and machine utilization as scheduling objectives, which are well adapted to the JSSP of different scales. The DR real-time selection system includes a simulation module, a learning module, and an application module. The function of the simulation module is to collect scheduling data in which is embedded a novel mathematical model describing the JSSP; the function of the learning module is to construct a DR assignment model to assign DR combinations to the job shop system, and the function of the application module is to apply the assigned DR combinations. Finally, a series of job shop systems are simulated to compare the DR assignment model with the NSGA-II and PSO algorithms. The aim is to verify the superiority of the DR assignment model and the rationality of the DR real-time selection system.