3011. A Benders Decomposition approach for the clustered hierarchical hub location problem
Invited abstract in session MB-15: Location problems, stream Combinatorial Optimization.
Monday, 10:30-12:00Room: Esther Simpson 1.08
Authors (first author is the speaker)
| 1. | Yerlan Kuzbakov
|
| ESSEC | |
| 2. | Laurent Alfandari
|
| ESSEC Business School |
Abstract
The Clustered Hierarchical Hub Location Problem (CHHLP) aims to find the locations of two types of hubs in a transportation network: local and global, and directing traffic through these hubs between each pair of customers at minimum cost. A key feature of CHHLP is the clustering of nodes, where inter-cluster connectivity is exclusively provided by global hubs. A practical application of this problem can be observed in transatlantic transportation systems, where continents function as clusters and seaports serve as global hubs. In this study, we propose a mixed-integer linear programming (MILP) formulation to address the CHHLP. The problem becomes computationally challenging for large-scale instances. To tackle this, we employ a Branch-and-Cut method, where cuts are generated using Benders Decomposition to enhance computational efficiency. Additionally, we explore heuristic approaches to provide near-optimal solutions for larger instances, ensuring a balance between solution quality and computational tractability. We also explore the application of Branch-and-Price techniques to solve various instances of the CHHLP. The results aim to demonstrate the effectiveness and scalability of the proposed methods.
Keywords
- Combinatorial Optimization
- Transportation
- Branch and Cut
Status: accepted
Back to the list of papers