Authors : Bharani Kumar Deepuru; Praveen Burra; Bharath S.; Manish Raj; Mohd. Amer Hussain; Poojitha S.; Sowmiya Radhakrishnan; Rohit Srivastava

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/2vkzt89h

DOI : https://doi.org/10.38124/ijisrt/25may335

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Optimizing logistics cutting expenses and guaranteeing seamless supply chain operations all depend on effective warehouse inventory and pallet management this study addresses issues related to ineffective pallet stacking wrong weight distribution and inadequate space usage by introducing a machine-learning-driven method for warehouse weight and space optimization, although they have been investigated traditional techniques like as rule-based algorithms 3d scanners and industrial weighing scales are frequently expensive and difficult to incorporate into dynamic warehouse settings This work uses state-of-the-art machine learning approaches to analyze real-time data from integrated weight sensors and 3D imaging systems in order to optimize pallet arrangement. The most efficient approach for pallet placement and load balancing was determined by testing a range of optimization algorithms, such as Reinforcement Learning (RL), Linear Programming (LP), and Genetic Algorithms (GA). By utilizing historical data and real-time inputs, machine learning models can dynamically adjust to shifting warehouse conditions, including changing box dimensions and fluctuating inventory levels. According to the findings, the suggested AI-driven optimization method improved stacking techniques and decreased pallet space waste, resulting in a 15% increase in warehouse productivity. According to the study, intelligent warehouse optimization can greatly increase throughput and operational efficiency by lowering the risk of overloading, eliminating needless pallet transfers, and optimizing weight distribution. Additionally, there was a 10% decrease in pallet waste, which reduced expenses. By showing how machine learning improves inventory accuracy, optimizes supply chain workflows, and increases overall warehouse productivity, the research findings highlight the importance of data-driven decision-making in warehouse logistics. As industries continue to embrace Artificial Intelligence (AI), Predictive Analytics, and IoT-driven automation, the suggested approach lays the groundwork for future innovations like demand-based storage allocation, automated load balancing, and real-time pallet tracking. This study demonstrates how scalable and flexible warehouse optimization solutions can be produced by combining intelligent algorithms with real-time data analytics.

Keywords : Pallet Optimization, Warehouse Management, Machine Learning, Logistics Efficiency, Linear Programming, Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization.

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