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Genetic Algorithms for the Picker Routing Problem in Multi-block Warehouses
Abstract
This article presents a genetic algorithm (GA) to solve the picker routing problem in multiple-block warehouses in order to minimize the traveled distance. The GA uses survival, crossover, immigration, and mutation operators, and is complemented by a local search heuristic. The genetic algorithm provides average distance savings of 13.9% when compared with s-shape strategy, and distance savings of 23.3% when compared with the GA with the aisle-by-aisle policy. We concluded that the GA performs better as the number of blocks increases, and as the percentage of picking locations to visit decreases. © 2019, Springer Nature Switzerland AG- Conference Paper
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- Artificial intelligence
- Genetic algorithm
- Multi-block warehouse
- Order picking
- Picker routing
- Warehouse management
- Artificial intelligence
- Heuristic algorithms
- Information systems
- Information use
- Routing algorithms
- Warehouses
- Average Distance
- Local search heuristics
- Multi blocks
- Mutation operators
- Number of blocks
- Order picking
- Picker routing
- Warehouse management
- Genetic algorithms
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Last time updated on 19/03/2021
This paper was published in Repositorio Institucional Universidad de Medellín.
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