I
Ivan Contreras
Researcher at Concordia University Wisconsin
Publications - 50
Citations - 2261
Ivan Contreras is an academic researcher from Concordia University Wisconsin. The author has contributed to research in topics: Network planning and design & Integer programming. The author has an hindex of 23, co-authored 46 publications receiving 1779 citations. Previous affiliations of Ivan Contreras include Universidad de las Américas Puebla & Polytechnic University of Catalonia.
Papers
More filters
Journal ArticleDOI
Stochastic Uncapacitated Hub Location
TL;DR: A Monte-Carlo simulation-based algorithm is described that integrates a sample average approximation scheme with a Benders decomposition algorithm to solve problems having stochastic independent transportation costs.
Journal ArticleDOI
Benders Decomposition for Large-Scale Uncapacitated Hub Location
TL;DR: This paper describes an exact algorithm capable of solving large-scale instances of the well-known uncapacitated hub location problem with multiple assignments by applying Benders decomposition to a strong path-based formulation of the problem.
Book ChapterDOI
Hub Location Problems
TL;DR: This chapter overviews the key distinguishing features, assumptions and properties commonly considered in HLPs, and highlights the role location and network design decisions play in the formulation and solution of HLPs.
Journal ArticleDOI
The Tree of Hubs Location Problem
TL;DR: This paper presents the Tree of Hubs Location Problem, a network hub location problem with single assignment where a fixed number of hubs have to be located, with the particularity that it is required that the hubs are connected by means of a tree.
Journal ArticleDOI
General network design: A unified view of combined location and network design problems
Ivan Contreras,Elena Fernández +1 more
TL;DR: A unified framework for the general network design problem which encompasses several classical problems involving combined location and network design decisions, and relevant modeling aspects, alternative formulations and possible algorithmic strategies are presented and analyzed.