scholarly journals Some variants of reverse selective center location problem on trees under the Chebyshev and Hamming norms

2017 ◽  
Vol 27 (3) ◽  
pp. 367-384 ◽  
Author(s):  
Roghayeh Etemad ◽  
Behrooz Alizadeh
Keyword(s):  
Facility Location ◽  
Upper Bound ◽  
Location Problem ◽  
The Other ◽  
Location Problems ◽  
Combinatorial Algorithms ◽  
Optimal Solutions ◽  
Total Cost ◽  
Tree Graphs ◽  
Center Location

This paper is concerned with two variants of the reverse selective center location problems on tree graphs under the Hamming and Chebyshev cost norms in which the customers are existing on a selective subset of the vertices of the underlying tree. The first model aims to modify the edge lengths within a given modification budget until a prespecified facility location becomes as close as possible to the customer points. However, the other model wishes to change the edge lengths at the minimum total cost so that the distances between the prespecified facility and the customers satisfy a given upper bound. We develop novel combinatorial algorithms with polynomial time complexities for deriving the optimal solutions of the problems under investigation.

scholarly journals Nash Welfare in the Facility Location Problem

2021 ◽  
Author(s):  
Alexander Lam
Keyword(s):  
Facility Location ◽  
Location Problem ◽  
Facility Location Problem ◽  
Maximum Cost ◽  
Total Cost ◽  
Strategic Behaviour ◽  
Fairness And Efficiency ◽  
Facility Placement

In most facility location research, either an efficient facility placement which minimizes the total cost or a fairer placement which minimizes the maximum cost are typically proposed. To find a solution that is both fair and efficient, we propose converting the agent costs to utilities and placing the facility/ies such that the product of utilities, also known as the Nash welfare, is maximized. We ask whether the Nash welfare's well-studied balance between fairness and efficiency also applies to the facility location setting, and what agent strategic behaviour may occur under this facility placement.

scholarly journals Center location problems on tree graphs with subtree-shaped customers

2008 ◽  
Vol 156 (15) ◽  
pp. 2890-2910 ◽  
Author(s):  
J. Puerto ◽  
A. Tamir ◽  
J.A. Mesa ◽  
D. Pérez-Brito
Keyword(s):  
Location Problems ◽  
Tree Graphs ◽  
Center Location

scholarly journals Aggregation and non aggregation techniques for large facility location problems: A survey

2015 ◽  
Vol 25 (3) ◽  
pp. 313-341 ◽  
Author(s):  
Chandra Irawan ◽  
Said Salhi
Keyword(s):  
Facility Location ◽  
Location Problem ◽  
Facility Location Problem ◽  
Location Problems ◽  
Objective Functions ◽  
Facility Location Problems ◽  
Aggregation Techniques ◽  
Number Of Customers

A facility location problem is concerned with determining the location of some useful facilities in such a way so to fulfil one or a few objective functions and constraints. We survey those problems where, in the presence of a large number of customers, some form of aggregation may be required. In addition, a review on conditional location problems where some (say q) facilities already exist in the study area is presented.

A polygonal upper bound for the efficient set for single-facility location problems with mixed norms

Top ◽  
1993 ◽  
Vol 1 (1) ◽  
pp. 107-116 ◽  
Author(s):  
Emilio Carrizosa Priego ◽  
Francisco Ramón Fernández García
Keyword(s):  
Facility Location ◽  
Upper Bound ◽  
Efficient Set ◽  
Location Problems ◽  
Mixed Norms ◽  
Facility Location Problems ◽  
Single Facility

scholarly journals Solving k-obnoxious facility location problem on a plane

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Utpal Kumar Bhattacharya
Keyword(s):  
Linear Programming ◽  
Facility Location ◽  
Upper Bound ◽  
Location Problem ◽  
Distance Measure ◽  
Optimal Solution ◽  
Facility Location Problem ◽  
Problem Area ◽  
Obnoxious Facility Location ◽  
Obnoxious Facility

 In this paper k-obnoxious facility location problem has been modeled as a pure planner location problem.  Area restriction concept has been incorporated by inducting a convex polygon in the constraints set. A linear programming iterative algorithm for k- obnoxious facility locations has been developed. An upper bound has been incorporated in the algorithm to get the  optimal solution. Also the concept of upper bound has reduced  the number of linear programming problems to solved in the algorithm. Rectilinear distance norm has been considered as the distance measure as it is more appropriate to the various realistic situations. 

scholarly journals Solving Classic Discrete Facility Location Problems Using Excel Spreadsheets

2021 ◽  
Author(s):  
Michael J. Brusco
Keyword(s):  
Facility Location ◽  
Operations Management ◽  
Location Problem ◽  
Set Covering ◽  
Location Problems ◽  
Discrete Location ◽  
Facility Location Problems ◽  
Location Models ◽  
Optimal Subset ◽  
Discrete Facility Location

There are a variety of discrete facility location models that have practical relevance for operations management and management science courses. Integer linear programming (ILP) is the standard technique for solving such problems. An alternative approach that is often conceptually appealing to students is to pose the problem as one of finding the best possible subset of p facilities out of n possible candidates. I developed an Excel workbook that allows students to interactively evaluate the quality of different subsets, to run a VBA macro that finds the optimal subset, or to solve an ILP formulation that finds the optimal subset. Spreadsheets are available for five classic discrete location models: (1) the location set-covering problem, (2) the maximal covering location problem, (3) the p-median problem, (4) the p-centers problem, and (5) the simple plant location problem. The results from an assignment in a master’s-level business analytics course indicate that the workbook facilitates a better conceptual understanding of the precise nature of the discrete facility location problems by showing that they can be solved via enumeration of all possible combinations of p subsets that can be drawn from n candidate locations. More important, students directly observe the superiority of ILP as a solution approach as n increases and as p approaches n/2.

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