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a b s t r a c tCompanies often require additional warehouse space due to increased demand and often the option of building a warehouse is not feasible due to a high investment cost. As a result excess inventory that cannot be stored in owned warehouses is transferred to third-party warehouses for which the company pays rent, as well as incurs labor and transportation costs for storing items and moving items back to the production site. In this situation, the company ends up with two warehouses -owned and rented. This research introduces the material location selection problem that allocates materials to these two warehouses while minimizing the total storage and transportation costs. Four material location models/ policies, each with different decision restrictions, are proposed to solve the problem from different material handling perspectives. The four models are analyzed and contrasted with each other based on data derived from a real industrial scenario. The case results show a cost savings opportunity between 20-40% due to reassigning materials between the rented and owned warehouses.

The cost of warehouse operations contributes approximately 20% of the total cost in a supply-chain system, and the cost of the picking process can be as high as 65% of the warehousing cost. Therefore, there is an incentive to improve the picking process in order to reduce the overall cost of the supply-chain system. The item assignment policy which allocates items to storage locations is one of the main decision processes that is aimed at lowering the travelling cost, which is normally considered as waste. Existing methods like the Cube-per-Order-Index (COI) and the volume-based strategy focus on the picking of items individually, the fact that multiple items can be picked in one single route is not considered. In this research, the concept of linear placement that has been used in computer science is adopted to create the Minimum Delay Algorithm (MDA). The method includes the relationship among items in order to generate the item storage layout that minimizes the total walking distance of order pickers. MDA was tested against other methods like CLP, COI, and OOS with 30 study cases. The results show that MDA can provide up to a 40% travel distance saving. Finally, MDA is combined with CLP, which is an iterative improvement method, in order to further improve the solution

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