Improved hybrid immune clonal selection genetic algorithm and its application in hybrid shop scheduling

This paper is based on the multi-objective optimization problem of mixed shop scheduling problem, the strong coupling of the maximum flow and the minimum time, and the deficiencies of the immune genetic algorithm including high computational complexity and high spatial dimension. This paper establishes a mixed shop scheduling mathematical model with the minimization of the maximum total completion time as the target, and puts forward to use the immune clonal selection algorithm to solve the problem. In the algorithm population construction, it uses the grouping strategy, introduces the cross and delete operator, retains the excellent individuals through memory space, deletes the relatively bad individual, and improves the algorithm’s global optimization ability. In order to verify the effectiveness of the proposed algorithm, under the two experimental environments of workpiece machining and automobile shock absorber processing workshop scheduling, simulation experiments are conducted. The experimental results show that the proposed algorithm has better performance, and can achieve smaller maximum total completion time with less iteration. The algorithm can find the global optimal solution of the multi-objective problem, which has a strong practical significance.