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Symmetry
Special Issues
Variable Neighborhood Search for Symmetrical and Asymmetrical Optimization Problems
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Variable Neighborhood Search for Symmetrical and Asymmetrical Optimization Problems

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 2542

Special Issue Editors

Prof. Dr. Angelo Sifaleras

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Guest Editor
Department of Applied Informatics, University of Macedonia, 54636 Thessaloniki, Greece
Interests: operations research; optimization; metaheuristics
Dr. Andrei Sleptchenko

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Guest Editor
Department of Industrial and Systems Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
Interests: optimization modeling; stochastic processes; inventory; service parts supply
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Adriana Felicia Gabor

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Guest Editor
Department of Mathematics, Khalifa University, Abu Dhabi, United Arab Emirates
Interests: discrete optimization problems; stochastic processes and their applications in operations research; applications of machine learning in optimization; operations research and management; combinatorial optimization; inventory; storage; reservoirs; transport

Special Issue Information

Dear Colleagues,

The 8th International Conference on Variable Neighborhood Search (ICVNS 2021) will be held in Abu Dhabi, U.A.E., on 21–25 March 2021 (http://www.icvns2020.info), and plans to continue the success of the previous ICVNS conferences. It will provide a stimulating environment in which researchers from various scientific fields can share and discuss their knowledge, expertise, and ideas related to the VNS metaheuristic and its applications. A key goal of the ICVNS conferences is the interdisciplinary synergy between scientists in the areas of operations research, computer science, and artificial intelligence, in order to mutually develop new methods for various optimization problems and machine learning tasks. This call for papers is dedicated to contributions on theoretical, methodological, or applied aspects of the VNS metaheuristic. This Special Issue is intended to cover both symmetrical and asymmetrical optimization problems occurring in real-life problems. Although we strongly encourage submissions from authors who attended ICVNS 2021, this call for papers is also open and welcomes submissions from the entire community of academics and practitioners. Please note that all submitted papers must be within the general scope of the Symmetry journal.

Prof. Dr. Angelo Sifaleras
Prof. Dr. Andrei Sleptchenko
Prof. Dr. Adriana Felicia Gabor
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 submissions that pass pre-check are 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. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). 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.

Keywords

  • Metaheuristics
  • variable neighborhood search
  • combinatorial optimization
  • global optimization
  • machine learning
  • interdisciplinary optimization approaches
  • multidisciplinary optimization
  • symmetrical/asymmetrical optimization problems

Published Papers (1 paper)

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Research

13 pages, 300 KiB  
Article
Solving the Max-Diversity Orthogonal Regrouping Problem by an Integer Linear Programming Model and a GRASP/VND with Path-Relinking Approach
by Eduardo Canale, Franco Robledo, Pablo Sartor and Luis Stábile
Symmetry 2022, 14(1), 18; https://doi.org/10.3390/sym14010018 - 23 Dec 2021
Viewed by 1616
Abstract
Students from Master of Business Administration (MBA) programs are usually split into teams. In light of the generalistic nature of MBA programs, diversity within every team is desirable in terms of gender, major, age and other criteria. Many schools rotate the teams at [...] Read more.
Students from Master of Business Administration (MBA) programs are usually split into teams. In light of the generalistic nature of MBA programs, diversity within every team is desirable in terms of gender, major, age and other criteria. Many schools rotate the teams at the beginning of every term so that each student works with a different set of peers during every term, thus training his or her adaptation skills and expanding the peer network. Achieving diverse teams while avoiding–or minimizing—the repetition of student pairs is a complex and time-consuming task for MBA Directors. We introduce the Max-Diversity Orthogonal Regrouping (MDOR) problem to manage the challenge of splitting a group of people into teams several times, pursuing the goals of high diversity and few repetitions. We propose a hybrid Greedy Randomized Adaptive Search Procedure/Variable Neighborhood Descent (GRASP/VND) heuristic combined with tabu search and path relinking for its resolution, as well as an Integer Linear Programming (ILP) formulation. We compare both approaches through a set of real MBA cohorts, and the results show that, in all cases, the heuristic approach significantly outperforms the ILP and manually formed teams in terms of both diversity and repetition levels. Full article
(This article belongs to the Special Issue Variable Neighborhood Search for Symmetrical and Asymmetrical Optimization Problems)
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