Extracting New Dispatching Rules for Multi-objective Dynamic Flexible Job Shop Scheduling with Limited Buffer Spaces

Abstract

Dispatching rules are among the most widely applied and practical methods for solving dynamic flexible job shop scheduling problems in manufacturing systems. Hence, the design of applicable and effective rules is always an important subject in the scheduling literature. The aim of this study is to propose a practical approach for extracting efficient rules for a more general type of dynamic job shop scheduling problem in which jobs arrive at the shop at different times and machine breakdowns occur stochastically. Limited-buffer conditions are also considered, increasing the problem complexity. Benchmarks are selected from the literature, with some modifications. Gene expression programming combined with a simulation model is used for the design of scheduling policies. The extracted rules are compared with several classic dispatching rules from the literature based on a multi-objective function. The new rules are found to be superior to the classic ones. They are robust and can be used for similar complex scheduling problems. The results prove the efficiency of gene expression programming as a nature-inspired method for dispatching rule extraction.