Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Lower bounds for the hub location problem
Management Science
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
New simple and efficient heuristics for the uncapacitated single allocation hub location problem
Computers and Operations Research
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A Lagrangean Heuristic for Hub-and-Spoke System Design with Capacity Selection and Congestion
INFORMS Journal on Computing
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Two artificial intelligence heuristics in solving multiple allocation hub maximal covering problem
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Uncapacitated single allocation p-hub maximal covering problem
Computers and Industrial Engineering
Choosing point-to-point versus hub-and-spoke flights: a genetic algorithmic approach
Proceedings of the 2012 Symposium on Emerging Applications of M&S in Industry and Academia Symposium
International Journal of Metaheuristics
Release Time Scheduling and Hub Location for Next-Day Delivery
Operations Research
An improved hybrid particle swarm optimization algorithm for fuzzy p-hub center problem
Computers and Industrial Engineering
Computers and Industrial Engineering
Journal of Intelligent Manufacturing
Solving fuzzy p-hub center problem by genetic algorithm incorporating local search
Applied Soft Computing
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Hub location problems are widely studied in the area of location theory, where they involve locating the hub facilities and designing the hub networks. In this paper, we present a new and robust solution based on a genetic search framework for the uncapacitated single allocation hub location problem (USAHLP). To present its effectiveness, we compare the solutions of our GA-based method with the best solutions presented in the literature by considering various problem sizes of the CAB data set and the AP data set. The experimental work demonstrates that even for larger problems the results of our method significantly surpass those of the related work with respect to both solution quality and the CPU time to obtain a solution. Specifically, the results from our method match the optimal solutions found in the literature for all test cases generated from the CAB data set with significantly less running time than the related work. For the AP data set, our solutions match the best solutions of the reference study with an average of 8 times less running time than the reference study. Its performance, robustness and substantially low computational effort justify the potential of our method for solving larger problem sizes.