Intelligent control mechanism of part picking operations of automated warehouse

This paper study the part picking operations of automated warehouse. It assumed the demand of picking orders of automated warehouse are dynamic generated. Once the picking orders of certain period of time are known, it is necessary to decide an efficient order picking sequence and routing to minimize the total travel distance to complete those orders. Assumed there are n_k items to be picked in order k. Each item in the picking order is located in different locations in the warehouse. Since it is possible the same items appear in the different picking orders, it will reduce the picking distance if these orders can be batched and picked in one path. However, there are several constraints for the order batching and order picking operations. These constraint are (1) the crane of the automated warehouse has the carrying capacity of C, and (2) for the management convenience, it is assumed that one picking order must be complete in one path. Because of the complexity of problem, it is inefficient to solve the problem by analytical approach. Although the heuristic method can significantly reduce of the computation time, the quality of the solution is always unacceptable. It is the intention of this paper to integrate the advantages of neural network and simulated annealing technique to develop an intelligent control mechanism for the planning of order picking operations of automated warehouse