A Cluster Based Genetic Algorithm for Product Allocation Across Multiple Warehouse

Matteo Gabellini (), Alberto Regattieri, Marco Bortolini, Pasquale di Nardo and Riccardo Siena
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Matteo Gabellini: University of Bologna, Deparment of Industrial Engineering
Alberto Regattieri: University of Bologna, Deparment of Industrial Engineering
Marco Bortolini: University of Bologna, Deparment of Industrial Engineering
Pasquale di Nardo: University of Bologna, Deparment of Industrial Engineering
Riccardo Siena: University of Bologna, Deparment of Industrial Engineering

A chapter in Proceedings of the International Conference on Industrial Logistics (ICIL) 2025, 2026, pp 105-112 from Springer

Abstract: Abstract In recent years, the growth of e-commerce has driven a trend toward order fulfillment strategies that draw products from multiple dispersed warehouses. This evolution has heightened the need for optimal product allocation to warehouse locations to minimize inter-warehouse shipment flows and reduce order completion times and costs. Despite the practical significance of this allocation problem, there is a lack of heuristic approaches capable of addressing large-scale, real-world instances. This paper proposes a novel genetic algorithm to solve the multi-warehouse product allocation problem, integrating tailored genetic operators and constraint-handling mechanisms to enhance solution quality. We evaluate the approach on an industrial case study drawn from an e-commerce company, comprising realistic demand and distribution scenarios. Computational experiments demonstrate that our genetic algorithm outperforms baseline methods in reducing total inter-warehouse flow, achieving significant improvements in logistical efficiency. These results clearly confirm the proposed method’s practical applicability and robustness for complex e-commerce fulfillment networks.

Keywords: e-commerce; genetic algorithm; logistics; order splitting; warehouse products distribution (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-14489-8_11

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