A fuzzy set covering-clustering algorithm for facility location problem

2011 ◽  
Author(s):  
Rashed Sahraeian ◽  
Mohammad Sadeq Kazemi
Keyword(s):  
Facility Location ◽  
Fuzzy Set ◽  
Clustering Algorithm ◽  
Location Problem ◽  
Facility Location Problem ◽  
Set Covering

scholarly journals Development of set covering model for determining the open /closed facilities location and resizing capacity of fasilities

2020 ◽  
Vol 909 (1) ◽  
pp. 012082 ◽  
Author(s):  
Utaminingsih Linarti ◽  
Cahaya Annisa’ Fatonah ◽  
Annie Purwani
Keyword(s):  
Facility Location ◽  
Location Problem ◽  
Facility Location Problem ◽  
Set Covering ◽  
Second Stage ◽  
Basic Model ◽  
Facilities Location ◽  
Covering Model ◽  
Two Stages ◽  
The Cost

Abstract Set Covering model determines the location of facilities that can be used that minimize the cost of assigning facilities to the point of request with the limitation that each facility was used for a number points of request. If there is an imbalance between the volume of demand points and the capacity of the facilities, it is necessary to open/closed facilities. In fact, facility location problem can extended for the decision not only open/closed but rezising area the existing facilities that can be adjusted considering the avaibility area of facilities location and demand points. This case has not been accommodated on the basic model of set covering. Not all the existing facilities can be resized. Rezising the area of facility location was assumed discrete. The aims of this study was optimized the alghorithm of set covering model. The solution this study was devided into two stages. The first stage was screening facilities that can be resized with survey location. The second stage was set covering model which the facilities will be close/opened and resize the existing facilities.

scholarly journals Facility Recommendation System Using Domination Set Theory in Graph

2019 ◽  
Vol 8 (9) ◽  
pp. 313-317
Keyword(s):  
Facility Location ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
Location Problem ◽  
Transportation Cost ◽  
Facility Location Problem ◽  
Geographical Region ◽  
Infrastructure Development ◽  
Police Station

Facility location problem has gained importance with the increased applications involving infrastructure development which involves placing facilities in right positions. With the help of GPS based services the analysis of locations and traffic which is vital input for the problem of facility location. The problem of facility location recommender is a multi-objective problem of reducing the transportation cost and increasing the coverage in the geographical region. Conditions to place a facility for a better coverage and reduced cost will differ from facility to facility. For the purpose algorithms such as route finder, Fastest clustering algorithm are used to cluster geographical region for improved infrastructure and better Quality of Service. In this paper analyzes facility of locating schools, hospital and police station in a bounded geographical region. The algorithm uses domination set and k-means clustering algorithm to choose the facility and its corresponding cluster in the region. Clustered are validated using index measures including DBI and Dunn Index values. An experimental analysis is conducted for Coimbatore city and results are evaluated against real facilities.

Clustering Approach Using Artificial Bee Colony Algorithm for Healthcare Waste Disposal Facility Location Problem

2019 ◽  
Vol 10 (1) ◽  
pp. 56-75 ◽  
Author(s):  
Zeynep Gergin ◽  
Nükhet Tunçbilek ◽  
Şakir Esnaf
Keyword(s):  
Facility Location ◽  
Waste Disposal ◽  
Real World ◽  
Artificial Bee Colony ◽  
Clustering Algorithm ◽  
Location Problem ◽  
Facility Location Problem ◽  
Healthcare Waste ◽  
Bee Colony ◽  
Disposal Facility

In this study, an Artificial Bee Colony (ABC) based clustering algorithm is proposed for solving continuous multiple facility location problems. Unlike the original version applied to multivariate data clustering, the ABC based clustering here solves the two-dimensional clustering. On the other hand, the multiple facility location problem the proposed clustering algorithm deals with is aimed to find site locations for healthcare wastes. After applying ABC based clustering algorithm on test data, a real-world facility location problem is solved for identifying healthcare waste disposal facility locations for Istanbul Municipality. Geographical coordinates and healthcare waste amounts of Istanbul hospitals are used to decide the locations of sterilization facilities to be established for reducing the medical waste generated. ABC based clustering is performed for different number of clusters predefined by Istanbul Metropolitan Municipality, and the total cost—the amount of healthcare waste produced by a hospital, multiplied by its distance to the sterilization facility—is calculated to decide the number of facilities to be opened. Benchmark results with four algorithms for test data and with two algorithms for real world problem reveal the superior performance of the proposed methodology.

scholarly journals Dispersing Obnoxious Facilities on a Graph

Algorithmica ◽  
2021 ◽  
Author(s):  
Alexander Grigoriev ◽  
Tim A. Hartmann ◽  
Stefan Lendl ◽  
Gerhard J. Woeginger
Keyword(s):  
Facility Location ◽  
Location Problem ◽  
Unit Length ◽  
Facility Location Problem ◽  
Np Hard ◽  
Obnoxious Facilities ◽  
Rational Parameter ◽  
Polynomially Solvable

AbstractWe study a continuous facility location problem on a graph where all edges have unit length and where the facilities may also be positioned in the interior of the edges. The goal is to position as many facilities as possible subject to the condition that any two facilities have at least distance $$\delta$$ δ from each other. We investigate the complexity of this problem in terms of the rational parameter $$\delta$$ δ . The problem is polynomially solvable, if the numerator of $$\delta$$ δ is 1 or 2, while all other cases turn out to be NP-hard.

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