Metaheuristics for Order Batching and Sequencing in Manual Order Picking Systems
Order picking deals with the retrieval of articles from their storage locations in order to satisfy customer requests. A major issue in manual order picking systems concerns of the transformation and consolidation of customer orders into picking orders (order batching). In practice, customer orders have to be completed by certain due dates in order to avoid delay in the shipment to customers or in production. The composition of the picking orders, their processing times and the sequence according to which they are released have a significant impact on whether and to which extent given due dates are violated. This paper presents how metaheuristics can be used in order to minimize the total tardiness for a given set of customer orders. The first heuristic is based on Iterated Local Search, the second one is inspired by the Attribute-Based Hill Climber, a heuristic based on a simple tabu search principle. In a series of extensive numerical experiments, the performance of these metaheuristics is analyzed for different classes of instances. We will show that the proposed methods provide solutions which may allow for operating order picking systems more efficiently. Solutions can be improved by 46% on average, compared to the ones obtained by standard constructive heuristics such as an application of the Earliest Due Date rule.
|Date of creation:||Jun 2011|
|Date of revision:|
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- Yu, Mengfei & de Koster, René B.M., 2009. "The impact of order batching and picking area zoning on order picking system performance," European Journal of Operational Research, Elsevier, vol. 198(2), pages 480-490, October.
- Derigs, U. & Kaiser, R., 2007. "Applying the attribute based hill climber heuristic to the vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 177(2), pages 719-732, March.
- de Koster, Rene & Le-Duc, Tho & Roodbergen, Kees Jan, 2007. "Design and control of warehouse order picking: A literature review," European Journal of Operational Research, Elsevier, vol. 182(2), pages 481-501, October.
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