Optimizing picking operations in a distribution
center of the large-scale retail trade

  • Eleonora Bottani 
  • Beatrice Franchi  
  • a,b Department of Engineering and Architecture, University of Parma, Viale delle Scienze 181/A, 43124 Parma, Italy
Cite as
Bottani E., Franchi B. (2021). Optimizing picking operations in a distribution center of the large-scale retail trade. Proceedings of the 23rd International Conference on Harbor, Maritime and Multimodal Logistic Modeling & Simulation(HMS 2021), pp. 60-69.
DOI: https://doi.org/10.46354/i3m.2021.hms.008


This paper proposes different scenarios for optimizing picking operations in the distribution center of a large retail chain business case, operating mainly in the fruit and vegetable sector. The chosen approach consists in modifying and combining the routing of pickers and the allocation rules of items, with the aim of decreasing the average distance traveled by the operator during the picking tasks. Four allocation policies have been implemented to this end, namely: 1) random, which is the one currently used by the Company (and therefore represents the benchmark scenario); 2) dedicated, based on the withdrawal frequency; 3) dedicated, based on the quantity of product; 4) a categorization of products into classes based on their demand. As regards the pickers’ routing, two heuristic algorithms currently used by the Company and three meta-heuristics are compared: Ant colony optimization (ACO), Min-max ant system (MMAS) and Backtrack. The study reveals that the best choice is to use the Backtrack algorithm on orders up to 15 lines, because in these cases the algorithm is very fast and always finds the best possible path. Instead, MMAS is to be preferred in case of larger orders: although finding the optimal path is no longer guaranteed, MMAS has a computational time much shorter (approximately 15 times) compared to the remaining algorithms.


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