An efficient procedure for designing single allocation hub and spoke systems
Management Science
HubLocator: an exact solution method for the multiple allocation hub location problem
Computers and Operations Research - Location analysis
Location models for airline hubs behaving as M/D/c queues
Computers and Operations Research
The Latest Arrival Hub Location Problem
Management Science
Designing Distribution Networks: Formulations and Solution Heuristic
Transportation Science
A Note on "The Latest Arrival Hub Location Problem"
Management Science
Solving the uncapacitated hub location problem using genetic algorithms
Computers and Operations Research
Hub-and-spoke network design with congestion
Computers and Operations Research
Hub Arc Location Problems: Part I-Introduction and Results
Management Science
Exact solution of a class of nonlinear knapsack problems
Operations Research Letters
Single allocation hub location problem under congestion: Network owner and user perspectives
Expert Systems with Applications: An International Journal
Twenty-Five Years of Hub Location Research
Transportation Science
Exact Solution of Large-Scale Hub Location Problems with Multiple Capacity Levels
Transportation Science
Hi-index | 0.00 |
Hub-and-spoke networks are widely applied in a variety of industries such as transportation, postal delivery, and telecommunications. Although they are designed to exploit economies of scale, hub-and-spoke networks are known to favour congestion, jeopardizing the performance of the entire system. This paper looks at incorporating congestion and capacity decisions in the design stage of such networks. The problem is formulated as a nonlinear mixed-integer program (NMIP) that explicitly minimizes congestion, capacity acquisition, and transportation costs. Congestion at hubs is modeled as the ratio of total flow to surplus capacity by viewing the hub-and-spoke system as a network of M/M/1 queues. To solve the NMIP, we propose a Lagrangean heuristic where the problem is decomposed into an easy subproblem and a more difficult nonlinear subproblem. The nonlinear subproblem is first linearized using piecewise functions and then solved to optimality using a cutting plane method. The Lagrangean lower bound is found using subgradient optimization. The solution from the subproblems is used to find a heuristic solution. Computational results indicate the efficiency of the methodology in providing a sharp bound and in generating high-quality feasible solutions in most cases.