A
Abel García-Nájera
Researcher at Universidad Autónoma Metropolitana
Publications - 32
Citations - 445
Abel García-Nájera is an academic researcher from Universidad Autónoma Metropolitana. The author has contributed to research in topics: Vehicle routing problem & Optimization problem. The author has an hindex of 9, co-authored 31 publications receiving 356 citations. Previous affiliations of Abel García-Nájera include University of Birmingham & Ensenada Center for Scientific Research and Higher Education.
Papers
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Journal ArticleDOI
An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows
TL;DR: A novel multi-objective evolutionary algorithm is proposed, which incorporates methods for measuring the similarity of solutions, to solve the multi- objective problem and achieves highly competitive results compared with previously published studies and those from a popular evolutionary multi-Objective optimizer.
Journal ArticleDOI
LIBEA: A Lebesgue Indicator-Based Evolutionary Algorithm for multi-objective optimization
TL;DR: This paper introduces a Lebesgue indicator-based evolutionary algorithm (LIBEA) for continuous box-constrained multi-objective optimization problems and shows that the proposed approach is highly competitive and that LIBEA significantly improved the state-of-the-art EMOAs on the test problems adopted in the authors' comparative study.
Journal ArticleDOI
An evolutionary approach for multi-objective vehicle routing problems with backhauls
TL;DR: This paper studies the VRP with backhauls (VRPB) in which the set of customers is partitioned into two subsets: linehaul customers requiring a quantity of product to be delivered, and backhaul customers with a quantity to be picked up.
Journal ArticleDOI
Multi-objective grey wolf optimizer based on decomposition
TL;DR: A multi-objective grey wolf optimizer based on the decomposition is introduced that approximates Pareto solutions cooperatively by defining a neighborhood relation among the scalarizing subproblems in which the multi- objective problem is decomposed.
Book ChapterDOI
Bi-objective Optimization for the Vehicle Routing Problem with Time Windows: Using Route Similarity to Enhance Performance
TL;DR: A method to measure route similarity and incorporate it into an evolutionary algorithm to solve the bi-objective VRPTW is proposed and applied to a publicly available set of benchmark instances, resulting in solutions that are competitive or better than others previously published.