Special Issue "Numerical and Evolutionary Optimization 2021"

A special issue of Mathematical and Computational Applications (ISSN 2297-8747). This special issue belongs to the section "Engineering".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Marcela Quiroz
E-Mail Website
Guest Editor
Centro de Investigación en Inteligencia Artificial, University of Veracruz, Xalapa 91000, Mexico
Interests: experimental algorithmics; metaheuristics; genetic algorithms; bin packing; machine learning; causal inference applications
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Dr. Luis Gerardo de la Fraga
E-Mail Website
Guest Editor
Computer Science Department, Cinvestav, Av. IPN 2508, Mexico City 07360, Mexico
Interests: computer vision; optimization; metaheuristics
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Dr. Adriana Lara
E-Mail Website
Guest Editor
Instituto Politécnico Nacional ESFM-IPN, Mexico City 07730, Mexico
Interests: multi-objective optimization; optimization; evolutionary computation; mathematical programming; memetic algorithms
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Dr. Leonardo Trujillo
E-Mail Website
Guest Editor
Departamento de Ingeniería en Electrónica y Eléctrica, Instituto Tecnológico de Tijuana, Calzada Tecnológico SN, Tomas Aquino, Tijuana 22414, Mexico
Interests: evolutionary computation; machine learning; data science; computer vision
Prof. Dr. Oliver Schütze
E-Mail Website
Guest Editor
Depto de Computacion, CINVESTAV-IPN, Mexico City 07360, Mexico
Interests: multi-objective optimization; evolutionary computation (genetic algorithms and evolution strategies); numerical analysis; engineering applications
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues, 

This Special Issue will mainly consist of selected papers presented at the 9th International Workshop on Numerical and Evolutionary Optimization (NEO 2021, see http://neo.cinvestav.mx for detailed information). However, other works that fit within the scope of the NEO are welcome. Papers considered to fit the scope of the journal and to be of sufficient quality after evaluation by the reviewers will be published free of charge. 

The aim of this Special Issue is to collect papers on the intersection of numerical and evolutionary optimization. We strongly encourage the development of fast and reliable hybrid methods that maximize the strengths and minimize the weaknesses of each underlying paradigm while also being applicable to a broader class of problems. Moreover, this Special Issue aims to foster the understanding and adequate treatment of real-world problems, particularly in emerging fields that affect us all, such as healthcare, smart cities, and big data, among many others. 

Topics of interest include (but are not limited to) the following:

A) Search and Optimization:
Single- and multi-objective optimization
Mathematical programming techniques
Evolutionary algorithms
Genetic programming
Hybrid and memetic algorithms
Set-oriented numerics
Stochastic optimization
Robust optimization 

B) Real-World Problems:
Optimization, Machine Learning, and Metaheuristics applied to:
Energy production and consumption
Health monitoring systems
Computer vision and pattern recognition
Energy optimization and prediction
Modeling and control of real-world energy systems
Smart cities

Dr. Marcela Quiroz
Dr. Luis Gerardo de la Fraga
Dr. Adriana Lara
Dr. Leonardo Trujillo
Prof. Dr. Oliver Schütze
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematical and Computational Applications is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Solving a Real-Life Distributor’s Pallet Loading Problem
Math. Comput. Appl. 2021, 26(3), 53; https://doi.org/10.3390/mca26030053 - 19 Jul 2021
Viewed by 215
We consider the distributor’s pallet loading problem where a set of different boxes are packed on the smallest number of pallets by satisfying a given set of constraints. In particular, we refer to a real-life environment where each pallet is loaded with a [...] Read more.
We consider the distributor’s pallet loading problem where a set of different boxes are packed on the smallest number of pallets by satisfying a given set of constraints. In particular, we refer to a real-life environment where each pallet is loaded with a set of layers made of boxes, and both a stability constraint and a compression constraint must be respected. The stability requirement imposes the following: (a) to load at level k+1 a layer with total area (i.e., the sum of the bottom faces’ area of the boxes present in the layer) not exceeding α times the area of the layer of level k (where α1), and (b) to limit with a given threshold the difference between the highest and the lowest box of a layer. The compression constraint defines the maximum weight that each layer k can sustain; hence, the total weight of the layers loaded over k must not exceed that value. Some stability and compression constraints are considered in other works, but to our knowledge, none are defined as faced in a real-life problem. We present a matheuristic approach which works in two phases. In the first, a number of layers are defined using classical 2D bin packing algorithms, applied to a smart selection of boxes. In the second phase, the layers are packed on the minimum number of pallets by means of a specialized MILP model solved with Gurobi. Computational experiments on real-life instances are used to assess the effectiveness of the algorithm. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2021)
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