Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2007. / Paul M. Griffin, Committee Member ; Gunter P. Sharp, Committee Member ; Hayriye Ayhan, Committee Member ; Leon F. McGinnis, Committee Chair ; Soumen Ghosh, Committee Member.

No description available.

No description available.

Thesis (M.S.)--Ohio University, 2002. / Title from PDF t.p.

Thesis (M.S.)--Ohio University, August, 2001. / Title from PDF t.p.

Thesis (M.S.)--Ohio University, August, 2004. / Includes bibliographical references (p. 120-125).

Thesis (M.S.)--Ohio University, June, 2003. / Title from PDF t.p. Includes bibliographical references (leaves 63-64)

Thesis (M.S.)--Ohio University, August, 2004. / Title from PDF t.p. Includes bibliographical references (p. 120-125)

This research investigates analytical models that might serve to support decisions in the early stages of designing high volume small parts order picking systems. Because the development of analytical closed-forms is challenging, a common approach is to use simulation models for detailed design performance assessment. However, simulation is not suitable for early stage design purposes; because simulation models are time-consuming (thus expensive) to construct and execute, especially when the number of alternatives to evaluate is large. If available, analytical models are computationally cheaper. They provide faster and more flexible solutions and though usually less detailed, may be adequate to support early stages of design. The challenge is to develop generic analytic models providing useful results for a class of problems. This research focuses on a class of problems in high volume small parts order picking systems with pick-to-buffer technology. This is a new technology, and not yet in widespread use. The novelty in the modeling approach is the distinct separation of item-picking and order assembly operations which permits the development of performance models for both throughput and service level. Essentially the system is modeled as a tandem queue, and the two detailed models for the picking and assembly subsystems are developed based on detailed description of the operations. Solving the model provides estimates for performance measures, such as order cycle time and system throughput, which are essential in design. The approximation method requires estimating the squared coefficient of interdeparture times from the classical GX/G/1 queuing model, and a suitable approximation is derived in this thesis. Computational tests show the model to provide reasonably accurate estimates of system performance, with minimal computational overhead. To support the proposed queuing model, new models are developed for estimating mean and squared coefficient of variation for pick and assembly operation times. These models include the variability of order contents and the picking process, along with the physical layout. Results of the estimation compare very well with that of simulation.

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 shipping dock, especially when multiple orders need to be selected for consolidation in shipment. The proposed loading algorithms and the order picking system performance evaluation models can be used to further study the effective integration of these two functions. / published_or_final_version / Mathematics / Doctoral / Doctor of Philosophy