An improved adaptive genetic algorithm for job shop scheduling problem

Zhongyuan Liang; Peisi Zhong M.D.; Chao Zhang M.D.; Mei Liu; Jinming Liu

doi:10.1117/12.2625335

12 December 2021 An improved adaptive genetic algorithm for job shop scheduling problem

Zhongyuan Liang, Peisi Zhong M.D., Chao Zhang M.D., Mei Liu, Jinming Liu

Proceedings Volume 12127, International Conference on Intelligent Equipment and Special Robots (ICIESR 2021); 1212722 (2021) https://doi.org/10.1117/12.2625335
Event: International Conference on Intelligent Equipment and Special Robots (ICIESR 2021), 2021, Qingdao, China

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.

Citation Download Citation

Zhongyuan Liang, Peisi Zhong M.D., Chao Zhang M.D., Mei Liu, and Jinming Liu "An improved adaptive genetic algorithm for job shop scheduling problem", Proc. SPIE 12127, International Conference on Intelligent Equipment and Special Robots (ICIESR 2021), 1212722 (12 December 2021); https://doi.org/10.1117/12.2625335

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