Pallet Management System: A Study of the Implementation of UID/RFID Technology for Tracking Shipping Materials within the Department of Defense Distribution Network

Sponsored Report (for Acquisition Research Program)The purpose of this MBA project is to identify the typical pallet utilization for the Defense Distribution Depot San Joaquin (DDJC) shipments to the Defense Distribution Depot San Diego (DDDC). That information will be used as the basis for suggesting a standardized reutilization management system for wood and non-wood pallets. This project will analyze the inclusion of Radio Frequency Identification and Unique Item Identification in conjunction with bar code technology for the improvement of asset visibility within the Department of Defense''s supply network.Naval Postgraduate School Acquisition Research ProgramApproved for public release; distribution is unlimited

Stochastic premarshalling of block stacking warehouses

Warehouse premarshalling (also pre-marshalling or remarshalling) is the activity of reordering items in a storage location so that subsequent retrieval orders can be serviced with little or no need for further relocations. It has deep impact on warehouse efficiency. We are interested in a stochastic case, where pickup orders become known only at the moment when they are to be retrieved. The problem is framed in a business analytics settings, where a forecasting statistical model based on historic data generates the input of a two stage stochastic optimization module. Computational results both on artificial and real-world data confirm the effectiveness of the approach

S-ACO: An Ant-Based Approach to Combinatorial Optimization Under Uncertainty

Abstract. A general-purpose, simulation-based algorithm S-ACO for solving stochastic combinatorial optimization problems by means of the ant colony optimization (ACO) paradigm is investigated. Whereas in a prior publication, theoretical convergence of S-ACO to the globally opti-mal solution has been demonstrated, the present article is concerned with an experimental study of S-ACO on two stochastic problems of fixed-routes type: First, a pre-test is carried out on the probabilistic traveling salesman problem. Then, more comprehensive tests are performed for a traveling salesman problem with time windows (TSPTW) in the case of stochastic service times. As a yardstick, a stochastic simulated annealing (SSA) algorithm has been implemented for comparison. Both approaches are tested at randomly generated problem instances of different size. It turns out that S-ACO outperforms the SSA approach on the considered test instances. Some conclusions for fine-tuning S-ACO are drawn.