Service-Oriented Considerate Routing: Data, Predictions, and Robust Decisions

In this research, we focus on improving service-oriented routing by addressing the nuanced challenge of punctuality through the consideration of couriers’ ability to ensure on-time deliveries. We utilize a comprehensive real-world data set from a cold chain logistics firm for analysis. Our empirical investigation indicates that relying solely on travel distance is inadequate for accurate delivery time prediction. We highlight critical elements, including couriers’ fixed effects and workload, as key covariates to improve prediction performance. Distinguishing our work from existing literature, we integrate couriers’ workload and location familiarity into our service-oriented routing model to enhance predictions of delivery times. We introduce the courier-assigned location mismatch (CALM) metric as a less intrusive approach to incorporating couriers’ location familiarity into their delivery efficiency. We propose the novel service-oriented considerate routing (SOCR) model; by minimizing the CALM metric, couriers are assigned routes within familiar territories to the extent possible within the total routing distance constraint. The considerate routing strategies could potentially reduce the stress couriers face when delivering in unfamiliar areas. Additionally, we develop the connection of the SOCR model with a robust satisficing approach. This strategy guarantees timely deliveries by effectively mitigating the effects of predictive inaccuracies and potential model misspecifications. To solve the SOCR model, we apply Benders decomposition for an exact solution and tabu search for a heuristic approach, demonstrating their effectiveness and superior out-of-sample performance. Notably, our heuristic solutions significantly outperform exact solutions of classical vehicle routing problems with deadlines, resulting in substantial improvements in timely delivery performance.

This paper was accepted by Wolfram Wiesemann, data science.

Funding: Y. Zhao and M. Sim were partially supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 [Grant MOE-2019-T3-1-010]. Z. Luo was supported by the National Natural Science Foundation of China [Grants 72222011, 72171112, and 72442006]. C. Chen was supported in part by the National Natural Science Foundation of China [Grants 72394363, 12431011, and 72394364].

Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05530.