계약 유형을 고려한 간선수송 내 트럭 할당 계획 최적화 모델
An Optimization Model for Truck Allocation Planning in Line-haul Transportation Considering Contract Types
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The domestic parcel delivery market is expanding as a result of the growing e-commerce sector, which has increased online transactions. Concurrently, domestic freight rates are rising due to labor costs and global oil price increases. In this challenging landscape, domestic logistics firms are striving to enhance efficiency and cut costs in line-haul transportation using a quantitative approach. To tackle this challenge, this study presents an optimization model designed to minimize the total cost of truck allocations within a line-haul transportation network. The model considers practical constraints such as contract types (fixed and temporary) and truck tonnage capacity (5 and 11 tons) assigned to each transportation route. We conducted a case study to validate the model using nationwide transportation data from Company A in 2021. A comparison with the company’s existing practices showed a significant 11% reduction in total costs, achieved by decreasing temporary truck allocations and increasing fixed contract truck allocations. Additionally, proposing a flexible operating policy for fixed contracts yielded an additional 3% cost reduction. This study’s methodology and experimental results offer valuable insights to logistics firms seeking optimal truck allocation plans for their line-haul transportation operations.
Abstract
The domestic parcel delivery market is expanding as a result of the growing e-commerce sector, which has increased online transactions. Concurrently, domestic freight rates are rising due to labor costs and global oil price increases. In this challenging landscape, domestic logistics firms are striving to enhance efficiency and cut costs in line-haul transportation using a quantitative approach. To tackle this challenge, this study presents an optimization model designed to minimize the total cost of truck allocations within a line-haul transportation network. The model considers practical constraints such as contract types (fixed and temporary) and truck tonnage capacity (5 and 11 tons) assigned to each transportation route. We conducted a case study to validate the model using nationwide transportation data from Company A in 2021. A comparison with the company’s existing practices showed a significant 11% reduction in total costs, achieved by decreasing temporary truck allocations and increasing fixed contract truck allocations. Additionally, proposing a flexible operating policy for fixed contracts yielded an additional 3% cost reduction. This study’s methodology and experimental results offer valuable insights to logistics firms seeking optimal truck allocation plans for their line-haul transportation operations.
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