Formulating and solving integrated order batching and routing in multi-depot AGV-assisted mixed-shelves warehouses

TL;DR

This paper introduces a novel integrated order batching and routing model for multi-depot AGV-assisted mixed-shelves warehouses, combining storage and transportation solutions to optimize order picking efficiency.

Contribution

It is the first to combine mixed-shelves storage with AGV transportation, modeling the problem as an extended multi-depot vehicle routing problem and developing a new variable neighborhood search algorithm.

Findings

Mixed-shelves policy reduces AGV driving distances by up to 62%.

The proposed algorithm finds optimal solutions efficiently.

Integrated approach improves order picking performance.

Abstract

Different retail and e-commerce companies are facing the challenge of assembling large numbers of time-critical picking orders that include both small-line and multi-line orders. To reduce unproductive picker working time as in traditional picker-to-parts warehousing systems, different solutions are proposed in the literature and in practice. For example, in a mixed-shelves storage policy, items of the same stock keeping unit are spread over several shelves in a warehouse; or automated guided vehicles (AGVs) are used to transport the picked items from the storage area to packing stations instead of human pickers. This is the first paper to combine both solutions, creating what we call AGV-assisted mixed-shelves picking systems. We model the new integrated order batching and routing problem in such systems as an extended multi-depot vehicle routing problem with both three-index and two-commodity network flow formulations. Due to the complexity of the integrated problem, we develop a novel variable neighborhood search algorithm to solve the integrated problem more efficiently. We test our methods with different sizes of instances, and conclude that the mixed-shelves storage policy is more suitable than the usual storage policy in AGV-assisted mixed-shelves systems for orders with different sizes of order lines (saving up to 62% on driving distances for AGVs). Our variable neighborhood search algorithm provides optimal solutions within an acceptable computational time.