The e-commerce revolution has brought about the need for more efficient order fulfillment. Guidelines are needed to determine the order fulfillment system design, and whether a manual or automated system should be implemented. A spreadsheet-based descriptive model has been developed based on demand levels, labor rates, and order sizes. We propose to combine the descriptive model with simulation output to demonstrate reactions of the system to fluctuations in demand, labor rates, and order sizes. The ultimate research goal is to provide a design aid to companies engaged in order fulfillment systems, emphasizing the decision of whether or not to automate the sorting process. / Master of Science

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<p>In today’s competitive global economy, the focus is on faster delivery of orders at lower total costs. In this paper we are interested in several aspects of order picking systems. We examine the influence of station layout, storage policy, picking policy and sorting solution on order picking system performance. On each of the analysis we consider a few solutions. We determine influence of different number of station in raw on picking system performance. We design the replenishment system supported by kanban philosophy with implemented economical order quantity (EOQ) and reorder point (ROP). The picking system is designed for assumed product’s demand values. The assessment of each of the solution is done through Arena simulation model. The results show that properly designed station with reasonable storage policy and implemented batching policy brings significant raise in order picking system productivity. In addition, we found that proper sortation system logic allows for more equal workload and reduction of maximum queue lengths. The results offer solutions to managers looking to implement improvements in order picking systems.</p>

In today’s competitive global economy, the focus is on faster delivery of orders at lower total costs. In this paper we are interested in several aspects of order picking systems. We examine the influence of station layout, storage policy, picking policy and sorting solution on order picking system performance. On each of the analysis we consider a few solutions. We determine influence of different number of station in raw on picking system performance. We design the replenishment system supported by kanban philosophy with implemented economical order quantity (EOQ) and reorder point (ROP). The picking system is designed for assumed product’s demand values. The assessment of each of the solution is done through Arena simulation model. The results show that properly designed station with reasonable storage policy and implemented batching policy brings significant raise in order picking system productivity. In addition, we found that proper sortation system logic allows for more equal workload and reduction of maximum queue lengths. The results offer solutions to managers looking to implement improvements in order picking systems.

No description available.

The design and operation of a high volume conveyor sortation system are important due to its high cost, large footprint and critical role in the system. In this thesis, we study the characteristics of the conveyor sortation system from performance evaluation and design perspectives employing continuous modeling approaches. We present two continuous conveyor models (Delay and Stock Model and Batch on Conveyor Model) with different representation accuracy in a unified mathematical framework. Based on the Batch on Conveyor Model, we develop a fast fluid simulation methodology. We address the feasibility of implementing fluid simulation from modeling capabilities, algorithm design and simulation performance in terms of accuracy and simulation time. From a design perspective, we focus on rates determination and accumulation design in the accumulation and merge subsystem. The optimization problem is to find a minimum cost design that satisfies some predefined performance requirements under stochastic conditions. We first transform this stochastic programming problem into a deterministic nonlinear programming problem through sample path based optimization method. A gradient based method is adopted to solve the deterministic problem. Since there is no closed form for performance metric even for a deterministic input stream, we adopt continuous modeling to develop deterministic performance evaluation models and conduct sensitivity analysis on these models. We explore the prospects of using the two continuous conveyor models we presented.