Research On Cargo Storage Laying Optimization Problem Based On Simulated Annealing Genetic Algorithm

As an important part of modern logistics, the significance of warehousing is self-evident. A reasonable and effective layout scheme for storage space will help enterprises to improve the effective utilization of storage space, reduce inventory management costs,and thus bring considerable benefits. In theory, the layout problem of storage space can be interpreted as a knapsack problem. Because of many conditions of constraint, in reality, the vast majority layout problem of storage space belongs to NP (Non-deterministic Polynomial, NP) complete problem form which we can't obtain an optimal solution in a limited polynomial time. Therefore, it's of great significance to find an effective solution to the layout algorithm of storage space for solving practical problems and NP complete problems.This paper firstly starts form the status of the logistics warehousing part, gives a simple introduction about the layout problems of storage space and the related, and then makes a detailed analysis and comparison about Simulated Annealing Algorithm and Genetic Algorithm which are pointed out the shortcomings and weaknesses, finally proposes a hybrid aglorithm combined with Simulated Annealing Algorithm and Genetic Algorithm after improved and optimized. The hybrid algorithm is based on heuristic which is based on space decomposition method. Through the decomposition of three-dimension space, the hybrid algorithm uses the spatial merge principle to combine the free space with the usable space in order to fully utilize the space, and then adopts different load order by types and quantities of goods.Through the analysis and comparison of examples, we can kown that the algorithm shows good performance in solving the problem of storage space layout. The models show that the result has greatly improved. so the hybrid algorithm for solving the problem of space layout in this paper has great practicality and feasibility in reality.Finally, this paper discusses the shortcomings and weaknesses in this algorithm, summarizes the whole paper, and points out the direction of future research.