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Optimizing Hub-And-Spoke Networks Under Demand Uncertainty: A Stochastic Capacitated Single Allocation P-Hub Covering Model with Lagrangian Relaxation
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Yaser Khosravian *   , Ali Shahandeh Nookabadi , Ghasem Moslehi |
| , y.khosravian@in.iut.ac.ir |
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Abstract: (316 Views) |
Traditional maximal p-hub covering problems focus on scenarios where network flow is constrained by resource limitations. However, many existing models rely on static parameters, overlooking the inherent randomness present in real-world logistics. This oversight can result in suboptimal network designs that are vulnerable to congestion and rising costs as demand varies. To address this issue, we propose a novel mathematical model for the capacitated single allocation maximal p-hub covering problem that takes into account stochastic variations in origin-destination flows. Although solving this model poses computational challenges, we utilize a Lagrangian relaxation algorithm to enhance efficiency. Computational experiments using the CAB dataset highlight the effectiveness of our approach in achieving optimal solutions while reducing computation time. This framework offers valuable insights for designing robust hub-and-spoke networks in the face of demand uncertainty.
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| Keywords: Maximal hub covering, Demand uncertainty, Lagrangian Relaxation Method, Capacitated hubs |
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Full-Text [PDF 310 kb]
(364 Downloads)
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Type of Study: Original |
Subject:
Mathematical Modeling and Applications of OR
Received: 2024/06/9 | Accepted: 2024/08/21 | Published: 2024/09/2
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