Mathematical Modeling for Warehouse Logistics: Stock Loading and Order Picking

This dissertation, "Mathematical Modeling for Warehouse Logistics: Stock Loading and Order Picking" by Li, Pan, 潘莉, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Logistics makes extensive use of human and material resources to achieve a target level of customer service at the lowest possible cost. It has been recognized as a major key to success in commerce and industry, and continues to evolve radically and grow in importance in recent years. Warehousing, as one of the most costly elements of logistics, is often the central operation in most logistics networks. Its successful management is critical in terms of both cost and service. In this thesis, two problem areas in warehouse logistics are studied: stock loading and order picking. Stock loading is an essential operation in modern logistics. Improvement on container capacity utilization and loading efficiency significantly reduces costs. For a given set of boxes in different sizes and an unlimited number of identical containers, the basic cargo loading problem is to determine the minimum number of containers required. The problem is proven NP-hard. To tackle this problem, a Tabu search optimization with a tree-based cargo loading algorithm as its inner heuristic is proposed. This approach has flexibility in taking different box conditions into consideration, and can find better solutions on average than other recent meta- or heuristic algorithms. Decreasing order sizes and increasing fuel costs provide a strong incentive for the inner-city truck loading operation to utilize container space more efficiently in transporting goods to multiple clients during one trip. This considers not only traditional loading constraints, but also multi-drop requirements. A wallbuilding heuristics based on a binary tree data structure is proposed to handle these side constraints. A dynamic space decomposition approach, together with a repacking and space amalgamation strategy, permits an efficient and effective loading plan. Order picking, one of the most critical warehousing operations, is the second problem studied in this thesis. An analytical approximation model is proposed based on probability modeling and queueing network theory applied to a synchronized zone picker-to-part order picking system with different routing and ABC-class inventory storage policies. The numerical results are compared and validated via simulation. The resulting model can therefore be usefully applied in the design and selection process of order picking systems. The routing versus storage issues are further investigated with a simulation model. This extends the existing research by evaluating multiple routing and storage policies under varying operating conditions. Results show that the midpoint, return and traversal routing policies generally perform best when paired with perimeter, across-aisle and within-aisle storage strategies, respectively. Yet performance is indeed dependent on demand patterns, zone sizes, batch sizes and order sizes. At first glance, order picking and stock loading operation seem to pursue different objectives. However, they are two related operations conducted sequentially from internal to the outbound side of warehousing. An efficient order picking system is a precondition for an effective loading operation at the sh