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Research Paper | Information Technology | United States of America | Volume 9 Issue 3, March 2020 | Popularity: 5.3 / 10
Data - Driven Warehouse Automation and Route Optimization in Cold Storage Logistics
Shafeeq Ur Rahaman
Abstract: This study investigates data-driven approaches for optimizing transportation cost estimation and route planning within agricultural cold storage logistics. By analyzing historical refrigerated truck rate data, three cost estimation models?linear, polynomial, and exponential were evaluated to identify the most accurate approach for calculating transportation expenses across varying distances. Results indicate that the polynomial model provides superior accuracy, effectively capturing the non-linear cost relationships that characterize cold chain logistics. Seasonal analysis revealed that transportation rates peak in the second and third quarters, corresponding with increased agricultural activity. In addition, regional analysis highlights that high-cost routes are concentrated along the East Coast and major urban centers, while regions such as the Midwest offer more cost-effective options. The scatter plot analysis of rate per mile against rate per truckload demonstrates a strong correlation for long-haul routes with high costs, underscoring the importance of route and cost structure selection in logistics planning. These insights offer agricultural logistics managers actionable strategies for reducing expenses, including leveraging seasonal trends and selecting cost-efficient routes. Future research may focus on integrating real-time data for further refinement of cost models to enhance decision-making flexibility in agricultural cold storage logistics.
Keywords: cold storage logistics, agricultural logistics, transportation costs, cost estimation models, polynomial model, seasonal analysis, regional cost variation, rate per mile, rate per truckload, data-driven logistics
Edition: Volume 9 Issue 3, March 2020
Pages: 1718 - 1725
DOI: https://www.doi.org/10.21275/SR200310112008
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