Improving the Gains Through Consolidation of Orders from Multiple Shippers at the Cross-Dock Facility in the Philippines
Author(s)
Manansala, Rovie
Sunio, Varsolo
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Abstract
This study investigates how shipment consolidation can enhance truck utilization in a cross-docking facility operated by a logistics service provider (LSP) in the Philippines. Using actual dispatch data from December 2024 to February 2025, the research identifies inefficiencies, including frequent underutilized truck dispatches and limited multi-order consolidation. Three optimization models were built in Python to simulate and improve load planning: 1) the Load Plan Model (LPM), which consolidates orders and requested delivery dates using greedy heuristics and linear optimization; 2) the Dynamic Lower Bound Load Plan Model (DLB-LPM), which introduces a dynamic lower bound to improve truck assignment efficiency; and 3) the DLB+Temporal Relaxation Model (DLB+TR), which incorporates soft constraints for temporal flexibility. These models treat truck loading as a multi-knapsack optimization problem, accounting for real-world operational constraints like weight, volume and delivery scheduling. Compared to actual dispatch performance, the models significantly reduced truck underutilization from 30-40% range to as low as 14.6% and improved consolidation frequency. All three models executed in under 7 minutes, demonstrating scalability and computational feasibility. These findings show that tailored optimization strategies can enhance day-to-day logistics operations and support a shift toward Physical Internet principles for more sustainable and efficient transport systems.
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Date
2025-06
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Text
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Proceedings
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