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Optimal locations and flow allocations for aggregation hubs in supply chain networks of perishable products

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  • Fahmy, Sherif A.
  • Zaki, Areej M.
  • Gaber, Yomna H.

Abstract

Establishment of aggregation hubs in a supply chain network (SCN) is typically a facility location-allocation (FLA) decision, which is known to be a NP-hard optimization problem. Considering the flow of heterogeneous perishable products, like fresh produce, with different spoilage rates, further increases the complexity of such a problem. This is due to the effect of transportation time and conditions, services provided in the hub, and hub proximity to supply sources, on the quality and quantity of products eventually reaching the demand destinations, and hence on the location-allocation decision. In this paper, this problem is formulated as a mixed integer linear programming (MILP) model that considers a number of problem characteristics simultaneously for the first time, to minimize the transportation, spoilage, processing, and capacity-based hub establishment costs. Due to its complexity, two hybrid algorithms that combine a meta-heuristic with a perishability-modified transportation algorithm, are proposed to solve the problem. The algorithms are based on binary particle swarm optimization (BPSO) and simulated annealing (SA). Taguchi analysis is used to tune the significant parameters of both algorithms considering different problem sizes. Computational analysis is further conducted to evaluate and compare the performances of the algorithms using randomly generated test instances and exact solutions obtained using CPLEX. Results show that while both algorithms are capable of obtaining optimum solutions for most instances, the hybrid BPSO slightly outperforms the hybrid SA in terms of consistency and solution time.

Suggested Citation

  • Fahmy, Sherif A. & Zaki, Areej M. & Gaber, Yomna H., 2023. "Optimal locations and flow allocations for aggregation hubs in supply chain networks of perishable products," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    • Handle: RePEc:eee:soceps:v:86:y:2023:i:c:s003801212200307x
    DOI: 10.1016/j.seps.2022.101500
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