2024 : 4 : 29
Alireza Eydi

Alireza Eydi

Academic rank: Associate Professor
ORCID:
Education: PhD.
ScopusId: 54974093700
Faculty: Faculty of Engineering
Address:
Phone: 08733664600-داخلی4347

Research

Title
Ring Hierarchical Hub Network Design Problem: Exact and Heuristic Solution Methods
Type
JournalPaper
Keywords
Transportation, ring hierarchical hub network, ring network, Benders decomposition, genetic algorithm, variable neighborhood search algorithm, relax-and-fix algorithm
Year
2022
Journal EURO Journal on Transportation and Logistics
DOI
Researchers Alireza Eydi ، Amir Khaleghi ، Khaled Barzegar

Abstract

In this paper, we study the hierarchical hub network design problem with a ring-star-star structure. In this problem, the hubs are located in two layers. In the first layer, central hubs (main hubs) are located in a ring structure, and in the second layer, secondary hubs are located. Each secondary hub is allocated to a central hub. Other demand points can also be allocated to each of these hubs (central hubs or secondary hubs). The objective is to minimize transportation costs on the network. This problem applies to communication networks when establishing direct links between demand nodes is not cost-effective, and there are two levels of service for customers. Also, the ring structure is used to reduce costs associated with full communication between central hubs. We present a mixed-integer programming model for the problem and report the results of the problem solving for a numerical example on the CAB dataset. Also, we present three solution methods for the problem. First, by exploiting the decomposable structure of the proposed model, we introduce an accelerated Benders decomposition algorithm. Then, we present a hybrid genetic algorithm that uses the Dijkstra algorithm to evaluate solutions. Next, we present a hybrid variable neighborhood search algorithm that uses the Dijkstra algorithm to calculate the cost of each solution. Also, a relax-and-fix algorithm is proposed to find near-optimal feasible solutions for the problem. Computational results are presented on the USA423 dataset. The results show that the proposed relax-and-fix algorithm can solve instances with up to 100 nodes. Also, the proposed hybrid algorithms can solve instances with up to 423 nodes. The performance of the proposed hybrid variable neighborhood search is better than the proposed hybrid genetic algorithm in solving large-sized instances.