An improved adaptive genetic algorithm for job shop scheduling problem

Abstract

Job shop scheduling problem is to find the optimal jobs sequence, which minimize the expected makespan. In solving NP-H problems such as job shop scheduling problems by genetic algorithm, trapping in local extremum, low search efficiency and instability are often encountered. In order to restrain this condition, an improved adaptive genetic algorithm based on sigmoid function was put forward. The crossover probability and mutation probability can be adjusted in nonlinear and adaptive based on the dispersion of the fitness of population in the evolution, which is better able to generate new individuals to get rid of the local extremum search to the global optimal solution. To research the performance of the improved adaptive genetic algorithm in solving job shop scheduling problems, a detailed application scheme was given out for the process of it. In the solving scheme, the chromosome decoding algorithm with the objective function of makespan was proposed. Ten JSP benchmark instances were solved with the evaluation index of solution accuracy, convergence efficiency and solution time by the simulation of MATLAB. Through the experimental results of comparing with the other three adaptive methods, the improved adaptive genetic algorithm has been significant improvement in solution accuracy and convergence efficiency.