Optimization of Pick and Place Robot Task Scheduling Operations in a Warehouse using Metaheuristics

P. Sivasankaran — Mon, 27 Apr 2026 00:00:00 +0000

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.

Journal of Advanced Research in Intelligence Systems and Robotics

Optimization of Pick and Place Robot Task Scheduling Operations in a Warehouse using Metaheuristics