A New Look at Spatially Competitive Facility Location Models

1985 ◽  
pp. 1-19 ◽  
Author(s):  
Roger L. Tobin ◽  
Terry L. Friesz
Keyword(s):  
Facility Location ◽  
Competitive Facility Location ◽  
Location Models ◽  
Facility Location Models ◽  
New Look

Competitive facility location models

2009 ◽  
Vol 49 (6) ◽  
pp. 994-1009 ◽  
Author(s):  
A. V. Kononov ◽  
Yu. A. Kochetov ◽  
A. V. Plyasunov
Keyword(s):  
Facility Location ◽  
Competitive Facility Location ◽  
Location Models ◽  
Facility Location Models

Facility location models for immobile servers with stochastic demand

2003 ◽  
Vol 51 (1) ◽  
pp. 137-152 ◽  
Author(s):  
Qian Wang ◽  
Rajan Batta ◽  
Christopher M. Rump
Keyword(s):  
Facility Location ◽  
Stochastic Demand ◽  
Location Models ◽  
Facility Location Models

Network facility-location models in Stackelberg-Nash-Cournot spatial competition

1992 ◽  
Vol 71 (3) ◽  
pp. 277-291 ◽  
Author(s):  
Tan Miller ◽  
Roger L. Tobin ◽  
Terry L. Friesz
Keyword(s):  
Facility Location ◽  
Spatial Competition ◽  
Location Models ◽  
Facility Location Models

Solving Facility Location Problems with a Tol for Rapid Development of Multi-Objective Evolutionary Algorithms (MOEAs)

2007 ◽  
pp. 642-660
Author(s):  
A. L. Medaglia
Keyword(s):  
Genetic Algorithm ◽  
Facility Location ◽  
Rapid Development ◽  
Service Level ◽  
International Markets ◽  
Nsga Ii ◽  
Multi Objective ◽  
Location Models ◽  
Cost Cutting ◽  
Facility Location Models

The low price of coffee in the international markets has forced the Federación Nacional de Cafeteros de Colombia (FNCC) to look for cost-cutting opportunities. An alternative that has been considered is the reduction of the operating infrastructure by closing some of the FNCC-owned depots. This new proposal of the coffee supplier network is supported by (uncapacitated and capacitated) facility location models that minimize operating costs while maximizing service level (coverage). These bi-objective optimization models are solved by means of NSGA II, a multi-objective evolutionary algorithm (MOEA). From a computational perspective, this chapter presents the multi-objective Java Genetic Algorithm (MO-JGA) framework, a new tool for the rapid development of MOEAs built on top of the Java Genetic Algorithm (JGA). We illustrate MO-JGA by implementing NSGA II-based solutions for the bi-objective location models.

On the Implementation of Quantitative Facility Location Models: The Case of a Hospital in a Rural Region

1996 ◽  
Vol 47 (5) ◽  
pp. 612-625 ◽  
Author(s):  
Abraham Mehrez ◽  
Zilla Sinuany-Stern ◽  
Tal Arad-Geva ◽  
Shemuel Binyamin
Keyword(s):  
Facility Location ◽  
Rural Region ◽  
Location Models ◽  
Facility Location Models
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