Abstract
In today’s rapid economic development, whether the factors of production can be reasonably allocated directly affects the utilization rate of resources. By further affecting the economic benefits, the importance of the logistics system in economic development can be clearly seen. However, with the deepening of economic development and the advantages of efficient and fast logistics, the proportion of production costs is increasing. The optimization of logistics scheduling problems is also on the agenda, but traditional scheduling optimization methods are not suitable for large scale complex problems, and interdisciplinary research provides new ideas for solving such problems. And by imitating the biological immune mechanism, the immune optimization algorithm has shown excellent performance in the research and application of each neighborhood. On this basis, this paper proposes an immune genetic algorithm to optimize the logistics scheduling problem, but the basic immune genetic algorithm has the problems of low local search ability and too fast algorithm speed. Based on the idea of clonal selection in biological immune system, a clonal immune genetic algorithm is proposed in this paper. Through simulation experiments on instances of different scales, the improved immune crown algorithm proposed in this paper can obtain the optimal solution of the problem within the same number of iterations, with an average solution probability of 30%. The quality and convergence speed are also significantly improved compared to the first two.
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