International Journal on Science and Technology

E-ISSN: 2229-7677   •   Impact Factor: 9.88

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Volume 16 Issue 2
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AI-Driven Optimization of Truck Scheduling in E-Commerce Logistics

Author(s) Ashish Patil
Country United States
Abstract E-commerce has revolutionized global logistics, with truck scheduling playing a pivotal role in ensuring cost-efficient, timely deliveries and operational excellence. The growing complexity of e-commerce logistics—driven by increasing order volumes, customer expectations for fast deliveries, and operational constraints—has created challenges in optimizing truck scheduling. Traditional methods for truck scheduling are often inefficient and inflexible, which results in higher transportation costs, underutilized fleets, and delayed deliveries. This paper explores how Artificial Intelligence (AI) can address these challenges, providing a deeper understanding of truck scheduling in e-commerce logistics and how AI methodologies, such as machine learning (ML), reinforcement learning (RL), and genetic algorithms (GA), can optimize the truck scheduling process. The paper compares these AI techniques, proposes a hybrid model combining ML and GA for optimal scheduling, and presents a practical example of its implementation with pseudo code. Furthermore, the paper discusses the impact of optimized truck scheduling on cost savings, delivery performance, and sustainability. With AI as a key enabler, e-commerce businesses can unlock efficiencies that are crucial for thriving in an increasingly competitive market.
Field Engineering
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-06-23
DOI https://doi.org/10.71097/IJSAT.v16.i2.6507
Short DOI https://doi.org/g9q4b6
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