A Hybrid Genetic Algorithm for Flexible Job Shop Scheduling Problem

Abstract

Partially flexible job shop scheduling problem (P-FJSP) is a NP Hard problem more complex than fully flexi-ble job shop scheduling problem (T -FJSP). In this paper, the mathematical model of flexible job shop scheduling is established with the goal of minimizing the maximum completion time (makespan). It combines the local search ability of simu-lated annealing algorithm and the global search ability of ge-netic algorithm. In the process of chromosome decoding, greedy decoding method is used to get a better scheduling solution as far as possible. The hybrid scheduling algorithm is implemented based on Visual Studio and C # language. Finally, 8×8 classic scheduling instance are used for simulation scheduling experiments to verify that the hybrid genetic algorithm proposed in this paper is effective in solving large-scale FJSP.