ItemBook Chapter

Advanced Machine Learning Model for Optimizing Pricing Strategies for Logistic Firms

Title
Advanced Machine Learning Model for Optimizing Pricing Strategies for Logistic Firms
Creator
Rithul, V.; George, Jossy; Nair, Akhil M.; Alapatt, Bosco Paul; Baby, Riya
Description
Cost optimization in logistics is a very crucial aspect for businesses to remain profitable and competitive by identifying and eliminating unnecessary costs. Most of the researchers concentrated primarily on demand modeling, vehicle routing challenges, and warehouse cost optimization, hence the existing models underperform. This study introduces a novel prediction model that optimizes costs by considering critical factors such as labor charges, material costs, transportation expenses, task types, and branch location. The current model is worked on a primary dataset of 2468 rows and 28 columns which was obtained from an established relocation company in India with all the confidentiality followed. To improve model performance, the required features were adjusted by rigorous feature engineering and data pretreatment techniques such as box-cox scaling, Winsorization, robust scaling, and one-hot encoding. Three ensemble learning techniques were tested: AdaBoost, XGBoost, and gradient boosting. The gradient boosting model correctly captured the complicated nonlinear connections between cost components and income, enabling for cost optimization decisions across a wide range of operational conditions. The proposed model has shown excellent results with the values achieving an MSE of 15% which demonstrates the effectiveness in cost optimization. However, the presence of residuals and potential outliers suggests that more model refinement and process improvements are required. The studys findings offer a data-driven framework for logistics and relocation companies to reduce costs, boost profitability, and gain a competitive advantage in the marketplace. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Learning and Analytics in Intelligent Systems;Volume;43;pp.77-88
Date
01-01-2025
Publisher
Springer Nature
Subject
Cost optimization; Logistics; Machine learning; Operational efficiency; Profitability; Relocation
Coverage
Rithul V., CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India; George J., CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India; Nair A.M., Luxsh Technologies Pvt Ltd., Middlesex, United Kingdom; Alapatt B.P., CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India; Baby R., CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 26623447;
Format
online
Language
English
Type
Book chapter

Citation

Rithul, V.; George, Jossy; Nair, Akhil M.; Alapatt, Bosco Paul; Baby, Riya, “Advanced Machine Learning Model for Optimizing Pricing Strategies for Logistic Firms,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/24171.

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