Joint Optimization of Storage Allocation and Picking Efficiency for Fresh Products Using a Particle Swarm-Guided Hybrid Genetic Algorithm

Yixuan Zhou, Yao Xu, Kewen Xie and Jian Li ()
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Yixuan Zhou: College of Information Management, Nanjing Agricultural University, Nanjing 211800, China
Yao Xu: College of Information Management, Nanjing Agricultural University, Nanjing 211800, China
Kewen Xie: College of Information Management, Nanjing Agricultural University, Nanjing 211800, China
Jian Li: College of Information Management, Nanjing Agricultural University, Nanjing 211800, China

Mathematics, 2025, vol. 13, issue 21, 1-30

Abstract: The joint optimization of storage location assignment and order picking efficiency for fresh products has become a vital challenge in intelligent warehousing because of the perishable nature of goods, strict temperature requirements, and the need to balance cost and efficiency. This study proposes a comprehensive mathematical model that integrates five critical cost components: picking path, storage layout deviation, First-In-First-Out (FIFO) penalty, energy consumption, and picker workload balance. To solve this NP-hard combinatorial optimization problem, we develop a Particle Swarm-guided hybrid Genetic-Simulated Annealing (PS-GSA) algorithm that synergistically combines global exploration by Particle Swarm Optimization (PSO), population evolution of Genetic Algorithm (GA), and the local refinement and probabilistic acceptance of Simulated Annealing (SA) enhanced with Variable Neighborhood Search (VNS). Computational experiments based on real enterprise data demonstrate the superiority of PS-GSA over benchmark algorithms (GA, SA, HPSO, and GSA) in terms of solution quality, convergence behavior, and stability, achieving 4.08–9.43% performance improvements in large-scale instances. The proposed method not only offers a robust theoretical contribution to combinatorial optimization but also provides a practical decision-support tool for fresh e-commerce warehousing, enabling managers to flexibly weigh efficiency, cost, and sustainability under different strategic priorities.

Keywords: fresh products; storage allocation; hybrid metaheuristic algorithm; particle swarm optimization; variable neighborhood search (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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