J
Juan A. Díaz
Researcher at Universidad de las Américas Puebla
Publications - 25
Citations - 1053
Juan A. Díaz is an academic researcher from Universidad de las Américas Puebla. The author has contributed to research in topics: Integer programming & Tabu search. The author has an hindex of 12, co-authored 25 publications receiving 960 citations. Previous affiliations of Juan A. Díaz include University of Iowa & Polytechnic University of Catalonia.
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A compact model and tight bounds for a combined location-routing problem
TL;DR: An auxiliary network is defined and the LP solution to the considered model provides an initial lower bound and is also used in a rounding procedure that provides the initial solution for a Tabu search heuristic.
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A Tabu search heuristic for the generalized assignment problem
Juan A. Díaz,Elena Fernández +1 more
TL;DR: The most distinctive features of the proposed Tabu search heuristic for the GAP are its simplicity and its flexibility, which result in an algorithm that provides good quality solutions in competitive computational times.
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Reactive grasp and tabu search based heuristics for the single source capacitated plant location problem
TL;DR: This paper considers the Single Source Capacitated Plant Location Problem (SSCPLP) and proposes three different hybrid approaches that combine elements of the GRASP and the Tabu Search methodologies.
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Lagrangean relaxation for the capacitated hub location problem with single assignment
TL;DR: A Lagrangean relaxation is proposed to obtain tight upper and lower bounds for the capacitated hub location problem with single assignment and some simple reduction tests are presented that allows us to reduce considerably the size of the formulation and thus, to reduce the computational effort.
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Branch and Price for Large-Scale Capacitated Hub Location Problems with Single Assignment
TL;DR: A branch-and-price algorithm for the capacitated hub location problem with single assignment, in which Lagrangean relaxation is used to obtain tight lower bounds of the restricted master problem, which results in a considerable improvement on the overall efficiency of the solution algorithm.