Chapter 4 Public facility location: A multiregional and multi-authority decision context

1987 ◽  
pp. 133-170
Author(s):  
Börje Johansson ◽  
Giorgio Leonardi
Keyword(s):  
Facility Location ◽  
Public Facility ◽  
Decision Context

Public facility location and urban spatial structure

1992 ◽  
Vol 48 (1) ◽  
pp. 83-118 ◽  
Author(s):  
Jacques-François Thisse ◽  
David E. Wildasin
Keyword(s):  
Spatial Structure ◽  
Facility Location ◽  
Public Facility ◽  
Urban Spatial Structure

Generalized coverage models and public facility location

1983 ◽  
Vol 53 (1) ◽  
pp. 117-135 ◽  
Author(s):  
Richard L. Church ◽  
Kenneth L. Roberts
Keyword(s):  
Facility Location ◽  
Public Facility ◽  
Coverage Models

scholarly journals A branch-and-Benders-cut algorithm for a bi-objective stochastic facility location problem

OR Spectrum ◽  
2021 ◽  
Author(s):  
Sophie N. Parragh ◽  
Fabien Tricoire ◽  
Walter J. Gutjahr
Keyword(s):  
Facility Location ◽  
Branch And Bound ◽  
Location Problem ◽  
Optimization Problems ◽  
Facility Location Problem ◽  
Public Facility ◽  
Integer Optimization ◽  
Decomposition Approach ◽  
Deterministic Equivalent ◽  
Cut Generation

AbstractIn many real-world optimization problems, more than one objective plays a role and input parameters are subject to uncertainty. In this paper, motivated by applications in disaster relief and public facility location, we model and solve a bi-objective stochastic facility location problem. The considered objectives are cost and covered demand, where the demand at the different population centers is uncertain but its probability distribution is known. The latter information is used to produce a set of scenarios. In order to solve the underlying optimization problem, we apply a Benders’ type decomposition approach which is known as the L-shaped method for stochastic programming and we embed it into a recently developed branch-and-bound framework for bi-objective integer optimization. We analyze and compare different cut generation schemes and we show how they affect lower bound set computations, so as to identify the best performing approach. Finally, we compare the branch-and-Benders-cut approach to a straight-forward branch-and-bound implementation based on the deterministic equivalent formulation.

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