Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Approximation on the Web: A Compendium of NP Optimization Problems
RANDOM '97 Proceedings of the International Workshop on Randomization and Approximation Techniques in Computer Science
Adapting polyhedral properties from facility to hub location problems
Discrete Applied Mathematics - The fourth international colloquium on graphs and optimisation (GO-IV)
Computing minimal doubly resolving sets of graphs
Computers and Operations Research
A fuzzy-driven genetic algorithm for sequence segmentation applied to genomic sequences
Applied Soft Computing
Structure-specified IIR filter and control design using real structured genetic algorithm
Applied Soft Computing
An informed genetic algorithm for the examination timetabling problem
Applied Soft Computing
Computing the metric dimension of graphs by genetic algorithms
Computational Optimization and Applications
Formulating and solving splittable capacitated multiple allocation hub location problems
Computers and Operations Research - Articles presented at the conference on routing and location (CORAL)
An iterated local search heuristic for a capacitated hub location problem
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
Computers and Industrial Engineering
Hi-index | 0.00 |
This paper addresses the capacitated hub location problem (CHLP), which is a variant of the classical capacitated hub problem. What is presented is a modified mixed integer linear programming (MILP) formulation for the CHLP. This modified formulation includes fewer variables and constraints compared to the existing problem formulations in the literature. We propose two evolutionary algorithms (EAs) that use binary encoding and standard genetic operators adapted to the problem. The overall performance of both EA implementations is improved by a caching technique. In order to solve large-scale instances within reasonable time, the second EA also uses a newly designed heuristic to approximate the objective function value. The presented computational study indicates that the first EA reaches optimal solutions for all smaller and medium-size problem instances. The second EA obtains high-quality solutions for larger problem dimensions and provides solutions for large-scale instances that have not been addressed in the literature so far.