Assembly Job Shop Scheduling Algorithm Based on Discrete Variable Neighborhood Mayfly Optimization
Due to the impact of the epidemic,it is more urgent for enterprises to reduce costs and increase efficiency by upgrading automated flexible production lines.In this context,the assembly job shop scheduling problem(AJSSP)has once again become a research hotspot in academia and business circles.AJSSP has one more assembly stage than ordinary job-shop scheduling pro-blems,so it has the phenomenon of mutual restriction and multi-machine parallel,and the problem solving is also more complica-ted.To solve this problem,a scheduling method based on a discrete variable neighborhood mayfly algorithm(D-VNMA)is pro-posed.The main work is as follows:1)Adopt the encoding and decoding mechanism conforming to Lamarkian characteristics to realize the iterative inheritance of individual effective information.2)Circle mapping and common heuristic algorithm are used to initialize the ephemera population to ensure the diversity of the population.3)A novel strategy for exploring neighborhoods,incor-porating a variety of distinct neighborhood structures and search strategies,is employed to enhance the diversity of search schemes and optimize the efficiency of finding local optimal solutions.4)An improved mating strategy of male and female mayflies is proposed to accelerate the global exploration ability of the algorithm and improve the overall convergence speed of the algo-rithm.During the experiment,the optimal parameter setting of D-VNMA is obtained by the design of experiment(DOE)method,and D-VNMA is compared with other algorithms in AJSSP example data of different specifications.Experimental results show that the probability of obtaining the optimal solution of D-VNMA is increased by 30％,and the convergence efficiency is increased by 62.15％.
万方数据
维普
