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.

Made available in DSpace on 2014-06-12T17:40:12Z (GMT). No. of bitstreams: 2 arquivo609_1.pdf: 2974950 bytes, checksum: 06bddbdb21dd83dae335a127ac61aa03 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2010 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / A armazenagem inclui todas as atividades de um ponto destinado à guarda temporária e à distribuição de materiais. Um ponto relevante na administração da armazenagem é estabelecer os parâmetros para a perfeita identificação e facilidade de localização dos itens estocados. Na literatura é apresentado o índice COI como principal índice para determinação de possíveis agregações em classes e a ordenação dos produtos armazenados. O COI é a razão entre o espaço requerido pelo produto por sua demanda. Além disso, há outros trabalhos que utilizam os custos com o uso do espaço no armazém e distância percorrida para picking os produtos como determinantes da melhor política de armazenagem dada pelo COI, em order-picking única. Então, pensando em picking em lotes, onde os lotes são compostos pelas ordens de cada cliente, e sabendo que o COI não menciona o número de clientes, foi proposto neste trabalho a utilização no número de clientes para a determinação da melhor política de armazenagem. Dois novos índices foram propostos: o CIC (razão entre o espaço requerido pelo número de clientes) e o COIC (razão do espaço requerido pela demanda vezes o número de clientes). Quatro cenários foram simulados. Em cada um destes, dois tipos de layouts (5 e 10 colunas), três níveis de diferentes produtos armazenados (10, 20 e 25) e três diferentes capacidades de picking. Concluiu-se que com a crescente preocupação do mercado em manter e conquistar novos clientes, apesar do COI ser um bom índice de atribuição de locais dos produtos em armazém, o seu uso generalizado pode gerar resultados menos satisfatórios do que a adoção de um índice que considere o número de clientes

Order picking operations play a critical role in the order fulfillment process of distribution centers (DCs). Picking a batch of orders is often favored when customers’ demands create a large number of small orders, since the traditional single-order picking process results in low utilization of order pickers and significant operational costs. Specifically, batch picking improves order picking performance by consolidating multiple orders in a "batch" to reduce the number of trips and total travel distance required to retrieve the items. As more pickers are added to meet increased demand, order picking performance is likely to decline due to significant picker blocking. However, in batch picking, the process of assigning orders to particular batches allows additional flexibility to reduce picker blocking. This dissertation aims to identify, analyze, and control, or mitigate, picker blocking while batch picking in picker-to-part systems. We first develop a large-scale proximity-batching procedure that can enhance the solution quality of traditional batching models to near-optimality as measured by travel distance. Through simulation studies, picker blocking is quantified. The results illustrate: a) a complex relationship between picker blocking and batch formation; and b) a significant productivity loss due to picker blocking. Based on our analysis, we develop additional analytical and simulation models to investigate the effects of picker blocking in batch picking and to identify the picking, batching, and sorting strategies that reduce congestion. A new batching model (called Indexed order Batching Model (IBM)) is proposed to consider both order proximity and picker blocking to optimize the total order picking time. We also apply the proposed approach to bucket brigade picking systems where hand-off delay as well as picker blocking must be considered. The research offers new insights about picker blocking in batch picking operations, develops batch picking models, and provides complete control procedures for large-scale, dynamic batch picking situations. The twin goals of added flexibility and reduced costs are highlighted throughout the analysis.

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No description available.

Order picking is considered one of the most time-consuming operations in picker-to-parts warehouses. Accordingly, more emphasis has been given to the task of improving the efficiency of order picking systems in general, and the required traveled distance during the order picking operation, specifically. In this thesis, we focus on two main factors that significantly affect the efficiency of order picking systems: the assignment storage policies, including the full-turnover, nearest-location and random storage policies; and the warehouse layout structure, in terms of the depth and the number of storage aisles. We investigate the combined effects of these two factors on the order picking travel distance. While previous research compares the full-turnover to the random storage policy, we compare the performance of the full-turnover policy to the nearest-location and random storage policies over various warehouse layout alternatives. For this purpose, we present a methodology for estimating order picking travel distance in a single-block, open-ended warehouse, under the assumptions of S-shape routing and discrete order policies.

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)