Research On Model And Algorithm For Storage Assignment Optimization In Warehouse

With the rapid development of logistics industry, the traditional storages can’t satisfy the needs of material production and circulation, so storage and retrieval systems are widely used in logistics industry. The efficiency of storage and retrieval system mainly depends on the storage assignment policies. Excellent allocation policies can improve the efficiency of operation and reduce the cost of warehousing systems. So the research on model and algorithm for storage assignment optimization constantly goes deeper.This paper first stated the situation and significance of research on slotting optimization, and introduced the characteristics of the ordinary warehouses, the application of ordinary warehouses, storage strategies and allocation principles. In this paper, we convert the efficiency and cost of warehouse into the distance of handling equipments, and established slotting optimization models which aims to reduce total distance of handling equipments during input and output operation. To establish these models, we concerned constraints in ordinary storage and retrieval system, such as storage strategies, volume of storages,loading capacity of handling equipments, requirement of commodities and so on.Secondly,we designed genetic algorithm, tabu search algorithm and genetic-tabu search algorithm to slove the models. The design content of genetic algorithm included designing different chromosomes which encoded in different methods for slotting optimization models and their respective operational procedures to realize crossover,mutation and repair chromosomes. We respectively used the truncation selection and the tournament selection to realize evolution,and improved algorithms of selection and updating the population to overcome the shortages during operations.The design content of tabu research algorithm included the express forms of solutions for different models, the heuristic method to generate initial solution. According to the characteristics of the problem, we designed the researching method to acquire neighborhood solutions, selection strategy, tabu table and amnesty criterion. In order to take full advantage of the global researching ability of genetic algorithm and the local ability of tabu search algorithm, we designed the genetic-tabu search algorithm. According to the different mixed strategies, we used the tabu research to improve population and designed the TSMO. Finally,we applied these three different algorithms to solve a given case. In experiments, we analysed the influence by changing parameters in three algorithms, stated the improvement of genetic algorithm through introducing tabu search, contrasted the optimizing results which got from three different algorithms and analysed the result of slotting optimization. Simulation results validated the feasibility and effectiveness of models and algorithms.