TY - JOUR
T1 - Solving the picker routing problem in multi-block high-level storage systems using metaheuristics
AU - Cano, Jose Alejandro
AU - Cortés, Pablo
AU - Muñuzuri, Jesús
AU - Correa-Espinal, Alexander
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61873151, in part by the Shandong Provincial Natural Science Foundation of China under Grant ZR2019MF009.
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2022
Y1 - 2022
N2 - 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.
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.
KW - Ant colony optimization
KW - Genetic algorithms
KW - High-level warehouses
KW - Order picking
KW - Picker routing problem
KW - Warehouse management
UR - http://www.scopus.com/inward/record.url?scp=85124552023&partnerID=8YFLogxK
U2 - 10.1007/s10696-022-09445-y
DO - 10.1007/s10696-022-09445-y
M3 - Artículo
AN - SCOPUS:85124552023
SN - 1936-6582
VL - 35
SP - 376
EP - 415
JO - Flexible Services and Manufacturing Journal
JF - Flexible Services and Manufacturing Journal
IS - 2
ER -