Abstract
AbstractIn manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations efficiently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem: the first one is based on Iterated Local Search; the second on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods but provide solutions which may allow distribution warehouses to be operated significantly more efficiently.
Highlights
Order picking is a warehouse function dealing with the retrieval of articles from their storage location in order to satisfy a given demand specified by customer orders (Petersen and Schmenner 1999: 481)
In this article we considered the Order Batching Problem, a problem which is pivotal for the efficient management and control of manual pickerto-parts order picking systems in distribution warehouses
By means of extensive numerical experiments it could be demonstrated that -- in terms of solution quality -- both approaches are superior to existing methods
Summary
Order picking is a warehouse function dealing with the retrieval of articles (items) from their storage location in order to satisfy a given demand specified by (internal or external) customer orders (Petersen and Schmenner 1999: 481). Order picking is critical to each supply chain, since underperformance results in an unsatisfactory customer service (long processing and delivery times, incorrect shipments) and high costs (labor cost, cost of additional and/or emergency shipments). Order picking is considered to include the most cost-intensive ones. According to Frazelle (2002) up to 50% of the total warehousing operating costs can be attributed to order picking. Even though there have been different attempts to automate the picking process, manual order picking systems are still prevalent in practice. Such manual order picking systems can be differentiated into two categories: Picker-to-parts systems, in which
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