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Solving the picker routing problem in multi-block high-level storage systems using metaheuristics

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Date
2023
Author
Cano J.A
Cortés P
Muñuzuri J
Correa-Espinal A.

Citación

       
TY - GEN T1 - Solving the picker routing problem in multi-block high-level storage systems using metaheuristics Y1 - 2023 UR - http://hdl.handle.net/11407/8557 PB - Springer AB - This study aims to minimize the travel time in multi-block high-level storage systems considering height level constraints for picking devices to leave aisles. Considering these operating environments, the formulation of minimum travel times between each pair of storage positions is proposed and the picker routing problem (PRP) is solved by means of Genetic Algorithms (GA) and Ant Colony Optimization (ACO). A parameter tuning is performed for both metaheuristics, and the performance of the GA and ACO is compared with the optimal solution for small-sized problems demonstrating the reliability of the algorithms solving the PRP. Then, the performance of the GA and ACO is tested under several warehouse configurations and pick-list sizes obtaining that both metaheuristics provide high-quality solutions within short computing times. It is concluded that the GA outperforms the ACO in both efficiency and computing time, so it is recommended to implement the GA to solve the PRP in joint order picking problems. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. ER - @misc{11407_8557, author = {}, title = {Solving the picker routing problem in multi-block high-level storage systems using metaheuristics}, year = {2023}, abstract = {This study aims to minimize the travel time in multi-block high-level storage systems considering height level constraints for picking devices to leave aisles. Considering these operating environments, the formulation of minimum travel times between each pair of storage positions is proposed and the picker routing problem (PRP) is solved by means of Genetic Algorithms (GA) and Ant Colony Optimization (ACO). A parameter tuning is performed for both metaheuristics, and the performance of the GA and ACO is compared with the optimal solution for small-sized problems demonstrating the reliability of the algorithms solving the PRP. Then, the performance of the GA and ACO is tested under several warehouse configurations and pick-list sizes obtaining that both metaheuristics provide high-quality solutions within short computing times. It is concluded that the GA outperforms the ACO in both efficiency and computing time, so it is recommended to implement the GA to solve the PRP in joint order picking problems. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.}, url = {http://hdl.handle.net/11407/8557} }RT Generic T1 Solving the picker routing problem in multi-block high-level storage systems using metaheuristics YR 2023 LK http://hdl.handle.net/11407/8557 PB Springer AB This study aims to minimize the travel time in multi-block high-level storage systems considering height level constraints for picking devices to leave aisles. Considering these operating environments, the formulation of minimum travel times between each pair of storage positions is proposed and the picker routing problem (PRP) is solved by means of Genetic Algorithms (GA) and Ant Colony Optimization (ACO). A parameter tuning is performed for both metaheuristics, and the performance of the GA and ACO is compared with the optimal solution for small-sized problems demonstrating the reliability of the algorithms solving the PRP. Then, the performance of the GA and ACO is tested under several warehouse configurations and pick-list sizes obtaining that both metaheuristics provide high-quality solutions within short computing times. It is concluded that the GA outperforms the ACO in both efficiency and computing time, so it is recommended to implement the GA to solve the PRP in joint order picking problems. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. OL Spanish (121)
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Abstract
This study aims to minimize the travel time in multi-block high-level storage systems considering height level constraints for picking devices to leave aisles. Considering these operating environments, the formulation of minimum travel times between each pair of storage positions is proposed and the picker routing problem (PRP) is solved by means of Genetic Algorithms (GA) and Ant Colony Optimization (ACO). A parameter tuning is performed for both metaheuristics, and the performance of the GA and ACO is compared with the optimal solution for small-sized problems demonstrating the reliability of the algorithms solving the PRP. Then, the performance of the GA and ACO is tested under several warehouse configurations and pick-list sizes obtaining that both metaheuristics provide high-quality solutions within short computing times. It is concluded that the GA outperforms the ACO in both efficiency and computing time, so it is recommended to implement the GA to solve the PRP in joint order picking problems. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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http://hdl.handle.net/11407/8557
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  • Indexados Scopus [2005]

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