Abstract

In this chapter, an investigation of the influence of Crossover Probability on Genetic Algorithm (GA) performance for the bi-criteria objective function to obtain the best solution in a reasonable time in scheduling of parallel machines is studied. A heuristic model for reducing the workload imbalance on the machines considering work-in-process material is developed. The simulation on a proposed genetic algorithm was carried out with a crossover probability of 0.4 to 0.95 (with a step of 0.05) and 0.97, and it was discovered that the results were converging for the crossover probability of 0.6 with a computing time of 3.41 seconds. The suggested algorithm assists the decision maker in analysing the objective function with the computational time.

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