Developing efficient order picker routing policies in manual picker-to-parts order picking systems

This dissertation develops several efficient order picker routing policies for manual picker-to-parts order picking systems. This work consists of six chapters and is structured as follows. Chapter 1 provides a brief introduction of the dissertation. Chapter 2 then presents the results of a systematic review of research on order picker routing. First, it identifies order picker routing policies in a systematic search of the literature and then develops a conceptual framework for categorizing the various policies. Order picker routing policies identified during the literature search are then descriptively analyzed and discussed in light of the developed framework. Our discussion of the state-of-knowledge of order picker routing shows that there is potential for future research to develop exact algorithms and heuristics for the routing of order pickers, both for order picking in specific scenarios and/or for non-conventional warehouses. One result of the literature review is that prior research on order picker routing always assumed that the picking tour starts and ends at the same location, which is usually the depot. In practice, however, it does not necessarily start and end at the same location, for example in case picking tours are updated in real time while they are being completed. Therefore, Chapter 3 proposes an exact algorithm as well as a routing heuristic for a conventional warehouse with two blocks where the starting and ending points of the picking tour are not fixed to the depot, but where they can be any locations in the warehouse instead. This chapter extends an earlier work of Loffler et al. (2018), who studied the case of a conventional warehouse with a single block, and adapts the solution procedures proposed by Ratliff and Rosenthal (1983) and Roodbergen and de Koster (2001a) that are both based on graph theory and dynamic programming procedure. Chapter 3 also develops a routing heuristic, denoted S*-shape, for solving the order picker routing problem in this scenario. In computational experiments, we compare the performance of the proposed routing heuristic to the exact algorithm. Our results indicate that the exact algorithm obtained tours that were between 6.32% and 35.34% shorter than those generated by the heuristic. One of the observations of Chapter 2 is that the order picker routing problem in non-conventional warehouses has not received much attention yet. Therefore, Chapter 4 studies the problem of routing an order picker in a non-conventional warehouse that has been referred to as the chevron warehouse in the literature. We propose an optimal order picker routing policy based on the solution procedures proposed by Ratliff and Rosenthal (1983) and Roodbergen and de Koster (2001a). Moreover, we modify three simple routing heuristics, namely the chevron midpoint, chevron largest gap, and chevron S-shape heuristics. The average order picking tour lengths resulting from the exact algorithm and the three routing heuristics were compared to evaluate the performance of the routing heuristics under various demand distributions and storage assignment policies used in warehouses. The results indicate that the picking tours resulting from the exact algorithm are 10.29% to 39.08% shorter than the picking tours generated by the routing heuristics. Chapter 5 then proposes an exact order picker routing algorithm for another non-conventional warehouse referred to as the leaf warehouse, and it again uses the concepts of Ratliff and Rosenthal (1983) and Roodbergen and de Koster (2001a). Moreover, it proposes four simple routing heuristics, referred to as the leaf S-shape, leaf return, leaf midpoint, and leaf largest gap heuristics. Similar to Chapter 4, we evaluate the performance of these heuristics compared to the exact algorithm for various demand distributions and storage assignment policies. Our results show that the picking tours resulting from the exact algorithm were, on average, between 3.96% to 43.68% shorter than the picking tours generated by the routing heuristics. Finally, Chapter 6 concludes the dissertation and presents an outlook on future research opportunities.