OPTIMIZATION ALGORITHM SIMULATION FOR DUAL-RESOURCE CONSTRAINED JOB-SHOP SCHEDULING

This research aims to optimize the job-shop scheduling constrained by manpower and machine under complex manufacturing conditions. To this end, a branch population genetic algorithm was presented based on compressed time-window scheduling strategy, and optimized with elite evolution and fan-shaped roulette operator. Specifically, the compressed time-window scheduling strategy was proposed to meet the two optimization targets: the maximum makespan and the total processing cost. Then, the elite evolution and fan-shaped roulette operator were introduced to simplify the global and local search, enhance the capacity of branch population genetic algorithm, and suppress the early elimination of inferior solutions, thus preventing the algorithm from falling into the local optimal solution. Finally, the rationality and feasibility of the proposed algorithm were verified through a simulation test. The simulation results show that the proposed algorithm lowered the maximum makespan and total processing cost by 7.4 % and 4.7 %, respectively, from the level of the original branch population genetic algorithm. This means the compressed time-window scheduling strategy can significantly optimize the makespan and the cost, as well as the robustness and global search ability.