Analysis of facility location model using Bayesian Networks

2012 ◽  
Vol 39 (1) ◽  
pp. 1092-1104 ◽  
Author(s):  
Ibrahim Dogan
Keyword(s):  
Bayesian Networks ◽  
Facility Location ◽  
Location Model

scholarly journals A Facility Location Model for Air Pollution Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
G. Lancia ◽  
F. Rinaldi ◽  
P. Serafini
Keyword(s):  
Genetic Algorithm ◽  
Air Pollution ◽  
Air Quality ◽  
Mathematical Models ◽  
Facility Location ◽  
Large City ◽  
Location Model ◽  
Practical Algorithms ◽  
Pollution Detection ◽  
The City

We describe mathematical models and practical algorithms for a problem concerned with monitoring the air pollution in a large city. We have worked on this problem within a project for assessing the air quality in the city of Rome by placing a certain number of sensors on some of the city buses. We cast the problem as a facility location model. By reducing the large number of data variables and constraints, we were able to solve to optimality the resulting MILP model within minutes. Furthermore, we designed a genetic algorithm whose solutions were on average very close to the optimal ones. In our computational experiments we studied the placement of sensors on 187 candidate bus routes. We considered the coverage provided by 10 up to 60 sensors.

Foulke Consumer Products, Inc.: The Southeast Region

2017 ◽  
pp. 1-8
Author(s):  
Robert L. Carraway ◽  
William Hosler
Keyword(s):  
Linear Programming ◽  
Facility Location ◽  
Consumer Products ◽  
Location Model ◽  
Distribution Problem ◽  
Southeast Region ◽  
The Core ◽  
Lp Model

At the core of this case is a distribution, or sourcing, problem that can be modeled and solved using linear programming (LP). There are also issues of whether to build (a) new plant(s)––and if so, what the capacity should be––and whether to expand or close one or more of the existing plants. These latter issues can be analyzed using a 0/1 LP facility-location model. Alternatively, because the number of options is limited, they can be analyzed using the straight LP model of the distribution problem as a tool to facilitate analysis.

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