Optimization Algorithm Simulation for Dual-Resource Constrained Job-Shop Scheduling

Abstract

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 fanshaped 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.

Production, maintenance and resource scheduling: A review

“…Compared to other algorithms from literature, results show that the proposed method is more effective. Similarly, Zhong & Yang (2018) propose a BPGA to solve the DRC Job Shop Problem (DR-CJSP), based on a compressed time window scheduling strategy, under the objective of minimizing makespan and total costs. Compared to an NSGA algorithm of the same authors, BPGA provides better results, lowering both objectives.…”

Manufacturing Scheduling Performance – a Case Study

“…TOC's key processes are focused on removing barriers that prevent each part from working together as an integrated whole. TOC becomes an important problem structuring and solving methodology, which changes the way of thinking of managers [16], [17], [18].…”

Optimization of welding job-shop scheduling problem under variable workstation constraint: an industrial application with Arena simulation based genetic algorithm

“…Zhang et al [14] used binary PSO to optimize the multi-technique, multiresponse FJSSP. Zhong et al [15] optimized dual resource constraint JSSP to optimize the makespan and total processing cost. They proposed branch population GA for optimization.…”