Data-Driven Order Consolidation with Vehicle Routing Optimization

Changhee Yang, Yongjin Lee () and Chulung Lee ()
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Changhee Yang: Department of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
Yongjin Lee: Department of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
Chulung Lee: School of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea

Sustainability, 2025, vol. 17, issue 3, 1-29

Abstract: This study compares time-based and quantity-based consolidation strategies within the Vehicle Routing Problem (VRP) framework to optimize supplier profitability and logistical efficiency. The time-based model consolidates deliveries at fixed intervals, offering predictable routes, reduced customer wait times, and cost efficiency in stable markets. Conversely, the quantity-based model dynamically adjusts delivery volumes to meet fluctuating demand, providing flexibility in dynamic environments but potentially increasing long-term costs due to logistical complexity. Using a mixed-integer linear programming (MILP) model, sensitivity analyses, and scenario-based experiments, the study demonstrates that the time-based model excels in stable conditions, while the quantity-based model performs better in highly variable demand scenarios. These findings provide actionable insights for selecting consolidation strategies that optimize delivery operations and enhance supply chain performance based on market dynamics.

Keywords: time-based model; quantity-based model; vehicle routing problem; consolidation strategies; supply chain optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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