Research Of Job Shop Scheduling Problem Based On Improved Coyote Optimization Algorithm

With the transformation of manufacturing mode from mass production to small batch,multi-variety and fast update,reasonable and efficient production scheduling is of great importance to enterprises.As a key activity of linking planning and production,shop floor scheduling,especially Job Shop Scheduling Problem(JSP),is especially critical for enterprises' production and manufacturing.Reasonable scheduling can improve the production efficiency and the effective utilization of equipment and other resources of enterprises,reduce the manufacturing cycle of products,reduce production costs,and improve their market competitiveness.Scheduling optimization is a kind of combinatorial optimization problem which is difficult to solve.JSP has been proved to be np-hard and difficult to solve.With the expansion of the problem scale and the development of research,intelligent algorithm has gradually become an important means to solve JSP.Coyote Optimization Algorithm(COA)is a new intelligent algorithm,which is characterized by easy implementation,fast calculation,small amount of computation,strong robustness,and superior self-learning ability.and can solve the JSP well.The main research contents of this paper are summarized as follows:(1)Coyote Optimization Algorithm with dimension by dimension improvement(DDICOA)was proposed,which solves 23 function optimization problems and 1 engineering optimization problem,which extended its engineering application field.The dimension by dimension update evaluation strategy can avoid the interference between dimensions,only accept the dimensional information that can improve the solution quality,and improve the algorithm's solution accuracy,convergence speed and robustness.(2)An improved coyote optimization algorithm(ICOA)is proposed to solve the Job Shop Scheduling Problem.Minimizing the maximum completion time as the scheduling goal,the three Job Shop Scheduling Problems MT06,MT10 and MT20 are solved,and a good scheduling scheme is obtained.ICOA redefined the cultural tendency of a pack,proposed the concept of coyote foraging scope to determine the crossover variation spacing,and updated the coyote individual superior to the cultural trend within the pack with the growth mode of dimensional crossover variation.(3)The Flexible Job Shop Scheduling Problem was solved by ICOA.The Flexible Job Shop Scheduling Problem is closer to the actual production mode of the enterprise and more difficult to solve.According to the characteristics of the problem,the coyote foraging base is reset,and the two-layer integer coding method is adopted to encode the two sub-problems of Operations Sequencing(OS)and Machine Selection(MS),and the MSOS genes are generated according to the OS processing sequence according to the ECO-MSOS rule.Minimizing the maximum completion time as the scheduling goal,10 Flexible Job Shop Scheduling Problems of BRdata data set were solved,and a good scheduling scheme was obtained.The research results in this paper have solved the Job Shop Scheduling and Flexible Job Shop Scheduling problems well,providing a new idea for the solution of the problem.Meanwhile,the algorithm is analyzed theoretically,which enriches its improvement research and expands its application field.