An adaptive real-time scheduling method for flexible job shop scheduling problem with combined processing constraint

Abstract

Flexible job shop scheduling problem with combined processing constraint is a common scheduling problem in assembly manufacturing industry. However, traditional methods for classic flexible job shop scheduling problem (FJSP) cannot be directly applied. To address this problem, the concepts of 'combined processing constraint' and 'virtual operation' are studied and introduced to simplify and transform FJSP with combined processing constraint into FJSP. A Multi-agent system (MAS) for FJSP is used for fitting the requirement of building complex, flexible, robust and dynamic manufacturing scheduling. On this basis, a novel adaptive real-time scheduling method for MAS is further proposed for better adaptability and performance. This method solves the previously converted problem and conquers the shortcoming of poor performance of traditional single dispatching rule method in MAS. In this approach, the scheduling process is modeled as contextual bandit, so that each job agent can select the most suitable dispatching rules according to the environment state after learning to achieve scheduling optimization. The proposed method is compared with some common dispatching rules that have been widely used in MAS. Results illustrate the high performance of the proposed method in a simulated environment.