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Hub-and-spoke network design under congestion: A learning based metaheuristic

Maryam Karimi-Mamaghan, Mehrdad Mohammadi, Amir Pirayesh, Amir Mohammad Karimi-Mamaghan and Hassan Irani

Transportation Research Part E: Logistics and Transportation Review, 2020, vol. 142, issue C

Abstract: This paper models a single allocation multi-commodity hub-and-spoke network problem through a bi-objective mathematical model, considering the congestion in both hubs and connection links. A novel aggregation model is developed based on a general GI/G/c queuing system to evaluate the congestion of the flow of the multiple products in the hubs. The proposed model is then solved using a novel learning-based metaheuristic based on NSGA-II, k-Means clustering method, and an Iterated Local Search (ILS) algorithm. The proposed model and solution algorithm are validated through a set of experiments against optimal solutions, and benchmarked against four existing well-known algorithms.

Keywords: Hub-and-spoke network design; Bi-objective optimization; Congestion; Queuing network; Machine learning; K-Means clustering method; Learning based Metaheuristics (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (9)

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DOI: 10.1016/j.tre.2020.102069

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