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

Abstract

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.

Research on the Design of Online Teaching Platform of College Dance Course based on IGA Algorithm

“…The genetic algorithm is a computational model that finds the optimal solution or approximate solution by simulating the evolution process. It has the advantages of being able to solve complex, multi-constrained problems and seek global parallel solutions [4]. Genetic algorithm has attracted the focus of many scholars because of its ability to cope with the questions in the platform, initialize the population, and perform genetic evolution to accurately group the questions [5].…”