A Grasshopper Optimization Algorithm for the Flexible Job Shop Scheduling Problem

Abstract

Job shop scheduling problem (JSP) is one of the most difficult scheduling problems, and flexible job shop scheduling problem (FJSP) is an extension of the classical JSP. In FJSP, a machine for each process should be selected from a given set, which introduces another decision element in the job path that makes FJSP more difficult than traditional JSP. In this paper, a recent proposed intelligence algorithm named grasshopper optimization algorithm (GOA) is used to solve FJSP. Numerical results with comparisons of other classic algorithm counterparts show that GOA has stronger global searching ability and performs better when solving FJSP.