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...cost service perspective is the design of the order picking system. Frazelle (2002) reports that order picking typically comprises about 50% of the total operating costs of a distribution center; and, to some extent, order picking determines customer service because it is one of several time consuming tasks that occur after a customer places an order. It also affects service quality because certain designs are more susceptible to mispicks and clerical errors than others.
Designers of order picking systems face a complex set of decisions, which interact mainly to satisfy three design goals:
* Throughput: how many picks per hour will be required?
* Space utilization: how many items must be stored in how much space? and
* Cost: what will be the fixed and variable costs?
For a greenfield design, the objective is usually to minimize cost, subject to constraints on throughput and space utilization. However, this may change over the lifetime of the distribution center: as the business grows, throughput requirements often increase, and managers attempt to maximize throughput; or storage space could become a problem, and managers would focus on space utilization. Regardless of the objective, these design goals involve tradeoffs: a higher throughput system typically requires more workers or automation and therefore has a higher cost. Higher space utilization may require one-way aisles, leading to worker congestion and lower throughput, or to automation and its associated increased costs.
To achieve these goals, designers must specify two primary characteristics of the system: (i) the physical configuration (racks, aisles, picking vehicles, etc.); and (ii) the order picking policy; that is, how workers are organized to pick and consolidate items for orders. The physical configuration determines space utilization by specifying the type of storage racks (single-deep, double-deep, flow-rack, etc.) and the width of aisles. Narrow aisles result in a higher space utilization, but can lead to increased travel and congestion associated with one-way travel.
The order picking policy specifies how workers are organized to retrieve orders. Tompkins et al. (2003) identify three major order picking policies: (i) discrete order picking, in which a worker picks all the items for a single order on each tour; (ii) batch picking, in which a worker collects items for several orders on each tour; and (iii) zone picking, in which each worker is assigned to a specific area of the warehouse and portions of orders are assigned to appropriate zones. (Here, we consider discrete order picking to be a form of batch picking, and therefore use the term batch picking to refer to either method.) Naturally, each method has advantages and disadvantages: discrete order picking is simple to implement and not prone to mispicks, but is labor intensive; batch picking has a higher productivity due to less worker travel per item picked, but can result in congestion and mispicks; zone picking has a high productivity (if there is sufficient picking activity to keep workers busy), but requires downstream sortation, which can be costly. In an attempt to garner the advantages from multiple methods, some warehouses use hybrid policies. We also note that multiple workers are allowed in a zone in some zone picking systems, but this is rare in our experience.
Notice that the physical configuration and the order picking policy might interact. If, in an attempt to increase space utilization, the designer specifies aisles too narrow to allow passing (which is not uncommon in practice), then having multiple workers in the same aisle (as in batch picking) could lead to congestion. This is the situation we consider in our work. Specifically, we are interested in narrow-aisle picking systems (hereafter we mean this to imply no passing within the aisles) when batch picking is the order picking policy; that is, when there can be congestion among workers. We have visited narrow-aisle systems with batch picking that were designed this way and one, which we describe below, that was forced into batch picking by a need for more throughput.
We believe, however, that our work is relevant for another reason. Our experience suggests that batch picking is not very common in narrow-aisle systems because managers believe that if required to produce high throughput, congestion would be a major problem. Therefore, managers often choose zone picking when narrow aisles prevent workers from passing, without (in our opinion) carefully weighing the disadvantages of zone picking, such as the cost of downstream sortation, the difficulty of balancing zones, which leads to the possibility of idle workers due to zone imbalances, and so on.
The models we present suggest that the effect of congestion in narrow-aisle systems, particularly busy ones, is misunderstood. The idea for our work came after discussions with a Distribution Center (DC) manager who told us he used zone picking rather than batch picking in a narrow-aisle picking area because the pick density (the probability that a worker will pick from any given location during his tour) was so low (corresponding to a low throughput). Several months later, when we visited again, he had converted the area to batch picking, citing a sharp increase in requests from this area and a need to increase the throughput; that is, pick density was high. This was not in keeping with our observation of industry practice, which seems to use zone picking to avoid congestion in busy picking areas with narrow aisles.
His statement led us to pose the following questions:
* Does a lower pick density lead to more congestion in narrow-aisle systems? If so then why? What is the relationship between pick density and congestion, and how should this influence the design of an order picking system?
* Could we develop insights that would help designers know if congestion would be a problem in...
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