Optimization of Pick and Place Robot Task Scheduling Operations in a Warehouse using Metaheuristics
Keywords:
Metaheuristics, Tabu search, Genetic algorithm, Hybrid GA- Tabu search , Computation timeAbstract
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.
References
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