Storage Scheduling Optimization Research Of AS/RS Based On Power Drive-through Rack

With the clustering of motor industry, the large-scaling of logistics enterprises and the intensifying of land utilization, requiring logistics equipments to meet the demand of high density and agile distribution. The research group propose and design a high density agile delivery system based on power drive-through rack. In this paper, storage scheduling optimization problem of the power drive-through rack system were studied, which has important theoretical significance and use value.Analyzed the structure and function the high density agile distribution system of power drive-through rack. From the weight of the goods, quantity and warehousing scheduling frequency, deep analyzed influencing factors of warehousing scheduling problem and studied the principles of the shelf optimization. Introduced constraints to meet the range of shelf, with the assumption that first put in storage goods which stability is the strongest, quantity is the largest and warehousing operation frequency is highest, we proposed multiple criteria slotting optimization model of power drive-through rack system.Adopted multi-objective optimization method transform the problem of judgment matrix, the assignment of weights into single-objective optimization problem. Based on genetic algorithms, searching for the optimal solution. Using the case of the shelf optimization of a logistics distribution center, we introduced cargo weight, quantity, and warehousing operation frequency, Comprehensive the above two algorithms for optimization calculation, to obtain the pallet layout in the power drive-through rack.Proposed the storage path scheduling optimization model with the assumption of set size range, the maximum load and maximum cargo capacity, the shortest total path of gantry shuttle car as objective function. Based on ant colony algorithm for the traveling salesman problem, introduced the specifications of the goods, packaging size, stacking and storage of information, use the ant colony algorithm to construct the solution space, update the pheromone instance to optimize operations, the number of iterations reaches113times then obtain optimum path, the total run time of196.14seconds, and the total moving distance of157.82meters, indicating that the powerful capabilities of the ant colony algorithm.