Hybrid Algorithm Based on Genetic Simulated Annealing Algorithm for Complex Multiproduct Scheduling Problem with Zero-Wait Constraint

Aiming at the complex multiproduct scheduling problem with 0-wait constraint, a hybrid algorithm based on genetic algorithm (GA) and simulated annealing (SA) algorithm was studied. Based on the results of pruning and grading to the operation tree of complex multiproduct, the design structure matrix (DSM) with precedence constraints was established. Then, an initial population coding method based on DSM was proposed and three strategies to optimize the initial population were proposed to improve the quality of the initial population for the situation of multiple operations in the same grade which need to be processed on the same machine. The specific process flow and the setting method of related parameters for the hybrid algorithm were given out. For the infeasible solution produced in the crossover operation, the repair method was proposed. In the decoding process with makespan as the optimization objective, the chromosome genes were classified and the decoding for complex multiproduct scheduling problem with 0-wait constraint was realized through the analysis of its characteristics. The effectiveness of the proposed algorithm for complex multiproduct scheduling problem with 0-wait constraint is verified by the test of related examples in the existing literature.

Automated summary

A hybrid algorithm combining genetic algorithm and simulated annealing was studied for the complex multiproduct scheduling problem with 0-wait constraint. Improvement on initial population quality was achieved through DSM and three optimization strategies were proposed. The decoding process was successfully implemented, and the effectiveness of the algorithm was verified on related examples.