Warehouse manual picking path optimization is a critical issue in modern logistics. In traditional warehouses, workers often follow inefficient routes, leading to increased picking time and reduced productivity. Proper path planning can significantly improve work efficiency and reduce labor costs.
Traditional picking methods often lack proper planning. Workers follow order sequences randomly, creating inefficient zigzag patterns with repeated paths. This approach significantly increases picking time and reduces overall productivity. The chaotic movement wastes energy and creates bottlenecks in warehouse operations.
Optimal path planning uses scientific algorithms like shortest path algorithms and genetic algorithms. By calculating distances between all picking points, these algorithms find the combination with minimum total distance. The Traveling Salesman Problem algorithm is commonly used to determine the most efficient route sequence.
Comparing traditional methods with optimized paths clearly shows efficiency improvements. The optimized route reduces walking distance by 30 to 50 percent, significantly improving picking efficiency and reducing worker fatigue. This comparison demonstrates the substantial benefits of implementing scientific path planning in warehouse operations.