Journal of Advances in Developmental Research

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Statistical Model Based Truck Scheduling

Author(s) Ashish Patil
Country United States
Abstract This paper, explores the necessity of third-party logistics providers like UPS for companies such as Amazon, particularly focusing on truck scheduling optimization thus leading to overall supply chain efficiency improvement. Truck scheduling is vital for efficient logistics operations, and its optimization can significantly enhance operational efficiency, reduce costs, and improve service quality. This paper presents a methodology to enhance truck scheduling through the application of historical data analysis and statistical confidence intervals. Specifically, we demonstrate using a 95% confidence interval to predict upstream demand, enabling the creation of upper and lower demand limits that guide more accurate scheduling decisions. The practical implementation of this approach is illustrated through a detailed pseudo-code example. Quantitative data and analytical insights validate the efficacy of the proposed methodology, underscoring its potential benefits for logistics and supply chain operations.
Keywords Truck scheduling, Logistics,third-party logistics providers, Amazon, Confidence interval, Supply chain optimization, Historical data analysis, statistical analysis
Field Engineering
Published In Volume 11, Issue 1, January-June 2020
Published On 2020-03-04
Cite This Statistical Model Based Truck Scheduling - Ashish Patil - IJAIDR Volume 11, Issue 1, January-June 2020. DOI 10.71097/IJAIDR.v11.i1.1367
DOI https://doi.org/10.71097/IJAIDR.v11.i1.1367
Short DOI https://doi.org/g9fzxh
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