A Genetic Algorithm for a Sensor Device Location Problem

2014 ◽  
pp. 49-57 ◽  
Author(s):  
Egidio D’Amato ◽  
Elia Daniele ◽  
Lina Mallozzi
Keyword(s):  
Genetic Algorithm ◽  
Location Problem ◽  
Sensor Device ◽  
Device Location

A simple and effective genetic algorithm for the two-stage capacitated facility location problem

2014 ◽  
Vol 75 ◽  
pp. 200-208 ◽  
Author(s):  
Diogo R.M. Fernandes ◽  
Caroline Rocha ◽  
Daniel Aloise ◽  
Glaydston M. Ribeiro ◽  
Enilson M. Santos ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Facility Location ◽  
Location Problem ◽  
Facility Location Problem ◽  
Two Stage ◽  
Capacitated Facility Location Problem ◽  
Capacitated Facility Location

scholarly journals Mathematical modeling for a p-mobile hub location problem in a dynamic environment by a genetic algorithm

2018 ◽  
Vol 54 ◽  
pp. 151-169 ◽  
Author(s):  
Mahdi Bashiri ◽  
Mohammad Rezanezhad ◽  
Reza Tavakkoli-Moghaddam ◽  
Hamid Hasanzadeh
Keyword(s):  
Mathematical Modeling ◽  
Genetic Algorithm ◽  
Location Problem ◽  
Dynamic Environment ◽  
Hub Location ◽  
Hub Location Problem

scholarly journals THE APPLICATION OF THE GENETIC ALGORITHM TO MULTI-CRITERIA WAREHOUSES LOCATION PROBLEMS ON THE LOGISTICS NETWORK

Transport ◽  
2018 ◽  
Vol 33 (3) ◽  
pp. 741-750 ◽  
Author(s):  
Mariusz Izdebski ◽  
Ilona Jacyna-Gołda ◽  
Mariusz Wasiak ◽  
Roland Jachimowski ◽  
Michał Kłodawski ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Location Problem ◽  
Labour Force ◽  
Raw Material ◽  
Initial Population ◽  
Optimization Criteria ◽  
Logistics Network ◽  
Local Taxes ◽  
Transition Costs ◽  
Crossover And Mutation

This paper presents multi-criteria warehouses location problem in the logistics network. In order to solve this problem the location model was developed. The limitations and optimization criteria of the model were determined. Optimization criteria refer to transportation costs, costs associated with warehouses, e.g.: local taxes, expenditure on starting the warehouse, the constant costs, the labour force costs, the purchase costs of the additional land for the expansion, the transition costs of the raw material via the warehouses. The final location of warehouse facilities was obtained using a genetic algorithm. The genetic algorithm was developed in order to solve the multi-criteria warehouses location problem. This paper describes the stages of the genetic algorithm i.e. the stage of designating the initial population, the crossover and mutation process, the adaptation function. In this paper, the process of calibration of this algorithm was presented. The results of the genetic algorithm were compared with the random results.

A Biased Random-Key Genetic Algorithm for the Traffic Counting Location Problem

2019 ◽  
Author(s):  
Glaubos Climaco ◽  
Pedro Henrique Gonzalez ◽  
Glaydston Mattos Ribeiro ◽  
Geraldo Regis Mauri ◽  
Luidi Simonetti
Keyword(s):  
Genetic Algorithm ◽  
Location Problem
Sign in / Sign up

Export Citation Format

Share Document