This paper studies the part picking operations of a ut omated warehouse. It assumed the demand of picking orders of automated warehouse are dynamic generated. Once the picking orders of certain period of time are kn o...This paper studies the part picking operations of a ut omated warehouse. It assumed the demand of picking orders of automated warehouse are dynamic generated. Once the picking orders of certain period of time are kn own, it is necessary to decide an efficient order picking sequence and routing t o minimize the total travel distance to complete those orders. Assumed there are n i items to be picked in order O i. Each item in the picking ord er 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 di stance 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 capacit y of C, and (2) for the management convenience, it is assumed that one picki ng order must be completed 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 the control mechanism for the planning of order picking operations of automated warehouse. A systematic computational simulation is conducted to evaluate the proposed method. The results show the pr oposed method can generate superior solution in most cased.展开更多
文摘This paper studies the part picking operations of a ut omated warehouse. It assumed the demand of picking orders of automated warehouse are dynamic generated. Once the picking orders of certain period of time are kn own, it is necessary to decide an efficient order picking sequence and routing t o minimize the total travel distance to complete those orders. Assumed there are n i items to be picked in order O i. Each item in the picking ord er 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 di stance 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 capacit y of C, and (2) for the management convenience, it is assumed that one picki ng order must be completed 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 the control mechanism for the planning of order picking operations of automated warehouse. A systematic computational simulation is conducted to evaluate the proposed method. The results show the pr oposed method can generate superior solution in most cased.
