https://www.adrjournalshouse.com/index.php/Intelligence-Robotics-Sysytem Journal of Advanced Research in Intelligence Systems and Robotics Advanced Research Publications en-US Journal of Advanced Research in Intelligence Systems and Robotics https://www.adrjournalshouse.com/index.php/Intelligence-Robotics-Sysytem/article/view/2606 <p>Warehouse automation plays a critical role in modern logistics and supply chain management. Autonomous pick-and-place robots improve operational efficiency by automating storage and retrieval operations. However, inefficient task scheduling may lead to increased travel distance, robot congestion, and reduced system throughput. This research proposes a metaheuristic-based optimisation framework for scheduling warehouse pick-and-place robot tasks. The proposed approach utilises Genetic Algorithm (GA) and Tabu Search (TS) to minimise robot travel distance and task completion time. A warehouse simulation model is developed to evaluate algorithm performance. Experimental results demonstrate that the hybrid GA–Tabu method significantly improves scheduling efficiency compared with conventional approaches.</p> P. Sivasankaran Copyright (c) 2026 Journal of Advanced Research in Intelligence Systems and Robotics 2026-04-27 2026-04-27 8 1 1 11