Order picking describes the process of retrieving a set of products from a warehouse in response to newly incoming customer orders. Deterministic order picking models assume all customer orders to be known at the beginning of the planning horizon. As a result, the application of such models in a dynamic environment is limited to a reactive strategy and ad-hoc decision-making each time new information becomes available.
To mitigate demand uncertainty and improve the efficiency of the
picking operations, we explore the idea of anticipatory order picking (AOP). In AOP, expected (but uncertain) customer orders are considered when planning and executing order picking activities. In contrast to the confirmed orders, these expected orders - if picked - are stored in a small buffer zone close to the packing and/or labeling station from which they can be retrieved with very limited travel time.
Extensive simulation experiments revealed the following advantages. First, AOP can provide a better workload balance by relocating peak hour orders during preceding, off-peak time intervals. Second, by picking orders at a time when their marginal travel cost is low, the total travel time and distance are reduced as well as the overall amount of traffic in the warehouse. Third, by better utilizing the pickers’ working time, the picking of all confirmed orders can complete earlier.
Original languageEnglish
Publication statusPublished - 2021
EventEURO European Conference on Operational Research - University of West Attica, Athens, Greece
Duration: 11 Jul 202114 Jul 2021


ConferenceEURO European Conference on Operational Research
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