Configuration model of partial repairable spares under batch ordering policy based on inventory state

Rational planning of spares configuration project is an effective approach to improve equipment availability as well as reduce life cycle cost （LCC）. With an analysis of various impacts on support system, the spares demand rate forecast model is constructed. According to systemic analysis method, spares support effectiveness evaluation indicators system is built, and then, initial spares configuration and optimization method is researched. To the issue of discarding and con-sumption for incomplete repairable items, its expected backorders function is approximated by Laplace demand distribution. Combining the （s-1, s） and （R, Q） inventory policy, the spares resup-ply model is established under the batch ordering policy based on inventory state, and the optimi-zation analysis flow for spares configuration is proposed. Through application on shipborne equipment spares configuration, the given scenarios are analyzed under two constraint targets：one is the support effectiveness, and the other is the spares cost. Analysis reveals that the result is consistent with practical regulation;therefore, the model＇s correctness, method＇s validity as well as optimization project＇s rationality are proved to a certain extent.

Intelligent Control Mechanism of Part Picking Operations of Automated Warehouse

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.

Tackling the storage problem through genetic algorithms

The capability automated warehouse in of a company to implement an an optimized way might be nowadays a crucial leverage in order to gain competitive advantage to satisfy the demand. The order picking is a warehouse function that needs to deal with the retrieval of articles from their storage locations. Merging several single customer orders into one, a picking order can increase efficiency of warehouse operations. The aim of this paper is to define throughout the use of ad-hoc genetic algorithm （GA） how better a warehouse can be set up. The paper deals with order batching, which has a major effect on efficiency of warehouse operations to avoid wastes of resources in terms of processes and to control possibility of unexpected costs in advance.