Utilizing individual picker skills to improve order batching in a warehouse

Abstract

Batching orders and routing order pickers is a commonly studied problem in many picker-to-parts warehouses. The impact of individual differences in picking skills on performance has received little attention. In this paper, we show that we are able to improve state-of-the-art batching and routing methods by almost 10% taking skill differences among pickers into account in minimizing the sum of total order processing time. Compared to assigning order batches to pickers only based on individual picker productivity, savings of 6% in total time are achieved. The increase in picker productivity depends on the picker category, but values of over 16% are observed for some categories. We demonstrate this for the case of a Finnish retailer. First, using time-stamped picking data, multilevel modeling is used to forecast batch execution times for individual pickers by modeling individual skills of pickers. Next, these forecasts are used to minimize total batch execution time, by assigning the right picker to the right order batch. We formulate the problem as a joint order batching and generalized assignment model, and solve it with an Adaptive Large Neighborhood Search algorithm.