Joint order batching and picker routing

The paper (co-authored with Sajjad Hedayati, Mostafa Setak and Emrah Demir):

A new approach to the joint order batching and picker routing problem with alternative locations

is now accepted and available online at the IMA Journal of Management Mathematics (see here).

This paper introduces a new approach to solving a problem in logistics called the joint order batching and picker routing problem. This problem involves optimizing the process of picking up items from different locations and delivering them to customers. The proposed approach uses mathematical models to find the most efficient way to group orders and route pickers to minimize travel time and distance. The authors suggest that this approach could be applied to real-world logistics scenarios to improve efficiency and reduce costs.

The main insights of this paper are that the proposed approach can effectively optimize the joint order batching and picker routing problem in mixed-shelves settings, where SKUs have alternative locations. This approach can help companies reduce process time and costs while improving the service level of their fulfillment operations. Moreover, this approach can also benefit the pickers, as they can efficiently complete multiple orders while navigating the warehouse aisles. The paper also highlights the importance of considering factors such as the number of clusters or distinct items per order and the density of overlapping in the ordered items within each order to achieve optimal results. Finally, the paper suggests that future research should explore incorporating stochastic settings and developing heuristic solution algorithms to effectively tackle large-scale instances of these problems.

This paper makes several contributions to the logistics and supply chain management literature. Firstly, it proposes a new approach to solving the joint order batching and picker routing problem in mixed-shelves settings, a common challenge e-commerce companies face. Secondly, it introduces two mixed-integer linear programming formulations for the Capacitated General Vehicle Routing Problem (CGVRP), which can be used to optimize the order-picking process. Thirdly, the paper highlights the importance of considering factors such as the number of clusters or distinct items per order and the density of overlapping in the ordered items within each order to achieve optimal results. Finally, the paper suggests future research directions, such as incorporating stochastic settings and developing heuristic solution algorithms that can effectively tackle large-scale instances of these problems.