A multi-algorithm approach for operational human resources workload balancing in a last mile urban delivery system

TL;DR

This paper introduces a multi-algorithm approach combining k-means, evolutionary, and hybrid algorithms to optimize workload balancing among delivery workers in last-mile urban package delivery, improving efficiency and fairness.

Contribution

It presents a novel multi-algorithm methodology for workload balancing in last-mile delivery, integrating distance and workload considerations for better resource allocation.

Findings

Effective workload balancing achieved in real-world urban delivery scenario.

Improved delivery time and workload equity among workers.

Demonstrated advantages of hybrid algorithms over single-method approaches.

Abstract

Efficient workload assignment to the workforce is critical in last-mile package delivery systems. In this context, traditional methods of assigning package deliveries to workers based on geographical proximity can be inefficient and surely guide to an unbalanced workload distribution among delivery workers. In this paper, we look at the problem of operational human resources workload balancing in last-mile urban package delivery systems. The idea is to consider the effort workload to optimize the system, i.e., the optimization process is now focused on improving the delivery time, so that the workload balancing is complete among all the staff. This process should correct significant decompensations in workload among delivery workers in a given zone. Specifically, we propose a multi-algorithm approach to tackle this problem. The proposed approach takes as input a set of delivery points and a defined number of workers, and then assigns packages to workers, in such a way that it ensures that each worker completes a similar amount of work per day. The proposed algorithms use a combination of distance and workload considerations to optimize the allocation of packages to workers. In this sense, the distance between the delivery points and the location of each worker is also taken into account. The proposed multi-algorithm methodology includes different versions of k-means, evolutionary approaches, recursive assignments based on k-means initialization with different problem encodings, and a hybrid evolutionary ensemble algorithm. We have illustrated the performance of the proposed approach in a real-world problem in an urban last-mile package delivery workforce operating at Azuqueca de Henares, Spain.