Service Oriented Considerate Routing: Data, Predictions and Robust Decisions

45 Pages Posted: 15 Apr 2024

See all articles by Yue Zhao

Yue Zhao

University of Toronto - Rotman School of Management

Zhixing Luo

Nanjing University

Stanley Frederick W. T. Lim

Michigan State University – The Eli Broad College of Business

Caihua Chen

affiliation not provided to SSRN

Melvyn Sim

National University of Singapore (NUS) - NUS Business School

Date Written: April 2, 2024

Abstract

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 dataset 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 (SOR) 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.

Keywords: Service Oriented Routing, Courier Assigned Location Mismatch Metric, Courier Behavior, Robust Satisficing, Data-driven Optimization

JEL Classification: C6, D8

Suggested Citation

Zhao, Yue and Luo, Zhixing and Lim, Stanley and Chen, Caihua and Sim, Melvyn, Service Oriented Considerate Routing: Data, Predictions and Robust Decisions (April 2, 2024). Available at SSRN: https://ssrn.com/abstract=4781416 or http://dx.doi.org/10.2139/ssrn.4781416

Yue Zhao

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada

Zhixing Luo

Nanjing University ( email )

Nanjing, Jiangsu 210093
China

Stanley Lim

Michigan State University – The Eli Broad College of Business

Business College Complex 632 Bogue St
East Lansing, MI 48824
United States

Caihua Chen

affiliation not provided to SSRN

Melvyn Sim (Contact Author)

National University of Singapore (NUS) - NUS Business School ( email )

1 Business Link
Singapore, 117592
Singapore

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