Discrete facility location in machine learning

2021 ◽  
Vol 28 (4) ◽  
pp. 5-60
Author(s):  
I. L. Vasilyev ◽  
A. V. Ushakov
Keyword(s):  
Machine Learning ◽  
Facility Location ◽  
Discrete Facility Location

scholarly journals On multi-criteria chance-constrained capacitated single-source discrete facility location problems

Omega ◽  
2019 ◽  
Vol 83 ◽  
pp. 107-122 ◽  
Author(s):  
Ömer Burak Kınay ◽  
Francisco Saldanha-da-Gama ◽  
Bahar Y. Kara
Keyword(s):  
Facility Location ◽  
Location Problems ◽  
Single Source ◽  
Facility Location Problems ◽  
Discrete Facility Location

scholarly journals Discrete facility location and routing of obnoxious activities

2003 ◽  
Vol 133 (1-3) ◽  
pp. 3-28 ◽  
Author(s):  
P. Cappanera ◽  
G. Gallo ◽  
F. Maffioli
Keyword(s):  
Facility Location ◽  
Discrete Facility Location

Discrete facility location with nonlinear diseconomies in fixed costs

1989 ◽  
Vol 18 (1) ◽  
pp. 213-224 ◽  
Author(s):  
Pitu Mirchandani ◽  
Ravindran Jagannathan
Keyword(s):  
Facility Location ◽  
Fixed Costs ◽  
Discrete Facility Location

scholarly journals Combined Simulated Annealing Algorithm for the Discrete Facility Location Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Jin Qin ◽  
Ling-lin Ni ◽  
Feng Shi
Keyword(s):  
Simulated Annealing ◽  
Facility Location ◽  
Location Problem ◽  
Simulated Annealing Algorithm ◽  
Facility Location Problem ◽  
Location Decision ◽  
Previous Algorithm ◽  
Annealing Algorithm ◽  
Discrete Facility Location ◽  
Better Than

The combined simulated annealing (CSA) algorithm was developed for the discrete facility location problem (DFLP) in the paper. The method is a two-layer algorithm, in which the external subalgorithm optimizes the decision of the facility location decision while the internal subalgorithm optimizes the decision of the allocation of customer's demand under the determined location decision. The performance of the CSA is tested by 30 instances with different sizes. The computational results show that CSA works much better than the previous algorithm on DFLP and offers a new reasonable alternative solution method to it.

scholarly journals An Approximation Algorithm for the Facility Location Problem with Lexicographic Minimax Objective

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ľuboš Buzna ◽  
Michal Koháni ◽  
Jaroslav Janáček
Keyword(s):  
Approximation Algorithm ◽  
Facility Location ◽  
Location Problem ◽  
Optimal Solution ◽  
Facility Location Problem ◽  
Exact Algorithm ◽  
General Purpose ◽  
Median Problem ◽  
Discrete Facility Location ◽  
Number Of Customers

We present a new approximation algorithm to the discrete facility location problem providing solutions that are close to the lexicographic minimax optimum. The lexicographic minimax optimum is a concept that allows to find equitable location of facilities serving a large number of customers. The algorithm is independent of general purpose solvers and instead uses algorithms originally designed to solve thep-median problem. By numerical experiments, we demonstrate that our algorithm allows increasing the size of solvable problems and provides high-quality solutions. The algorithm found an optimal solution for all tested instances where we could compare the results with the exact algorithm.

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