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Data-Driven Order Consolidation with Vehicle Routing Optimization

Author

Listed:
  • 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)

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.

Suggested Citation

  • Changhee Yang & Yongjin Lee & Chulung Lee, 2025. "Data-Driven Order Consolidation with Vehicle Routing Optimization," Sustainability, MDPI, vol. 17(3), pages 1-29, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:848-:d:1572947
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