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Mathematics
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Multi-Objective Optimization and Decision Support Systems
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Multi-Objective Optimization and Decision Support Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 4508

Special Issue Editor

Prof. Dr. Eduard Babkin

E-Mail Website
Guest Editor
Faculty of Informatics, Mathematics, and Computer Science, National Research University Higher School of Economics, Nizhny Novgorod 603093, Russia
Interests: artificial intelligence; coordinated control of multi-agent systems; linear and nonlinear system control; sub-symbolic computations

Special Issue Information

Dear Colleagues,

Multi-Objective Optimization and Decision Support Systems represent a broad and actively developing domain of scientific inquiry and practical applications. Among distinguishing features of research in that domain we may discern a rather large set of highly dispersed models and approaches available for optimization and decision support, variations of symbolic and sub-symbolic fuzzy methods to deal with uncertainty and changes, strong attention to subjective evaluation of criteria values in complex situations, using approaches and tools of linguistics and semiotics. In such circumstances developing of unified mathematical foundations becomes very critical and relevant task. Progress in formalization and unification of sub-symbolic and symbolic multi-criteria approaches to optimization and decision support, seeking for objective comparison of different decision models open perspectives for engaging new frontiers in intellectual information systems, policy-making, and other important application areas.

This Special Issue provides an effective communication media for researchers from academia and industry to present their novel and unpublished cross-disciplinary works in Multi-Objective Optimization and Decision Support Systems. Dissemination and professional discussion of new scenarios, models, algorithms will foster future advances in theory and practice of Multi-Objective Optimization and Decision Support Systems.

Prof. Dr. Eduard Babkin
Guest Editor

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • multi-objective optimization
  • decision support
  • symbolic and sub-symbolic models
  • fuzzy logics
  • linguistic models
  • simulation for decision support

Published Papers (2 papers)

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Research

31 pages, 8781 KiB  
Article
Comprehensive Analysis of Multi-Objective Optimization Algorithms for Sustainable Hybrid Electric Vehicle Charging Systems
by Nahar F. Alshammari, Mohamed Mahmoud Samy and Shimaa Barakat
Mathematics 2023, 11(7), 1741; https://doi.org/10.3390/math11071741 - 05 Apr 2023
Cited by 10 | Viewed by 2124
Abstract
This study presents a multi-objective optimization approach for designing hybrid renewable energy systems for electric vehicle (EV) charging stations that considers both economic and reliability factors as well as seasonal variations in energy production and consumption. Four algorithms, MOPSO, NSGA-II, NSGA-III, and MOEA/D, [...] Read more.
This study presents a multi-objective optimization approach for designing hybrid renewable energy systems for electric vehicle (EV) charging stations that considers both economic and reliability factors as well as seasonal variations in energy production and consumption. Four algorithms, MOPSO, NSGA-II, NSGA-III, and MOEA/D, were evaluated in terms of their convergence, diversity, efficiency, and robustness. Unlike previous studies that focused on single-objective optimization or ignored seasonal variations, our approach results in a more comprehensive and sustainable design for EV charging systems. The proposed system includes a 223-kW photovoltaic system, an 80-kW wind turbine, and seven Lithium-Ion battery banks, achieving a total net present cost of USD 564,846, a levelized cost of electricity of 0.2521 USD/kWh, and a loss of power supply probability of 1.21%. NSGA-II outperforms the other algorithms in terms of convergence and diversity, while NSGA-III is the most efficient, and MOEA/D has the highest robustness. The findings contribute to the development of efficient and reliable renewable energy systems for urban areas, emphasizing the importance of considering both economic and reliability factors in the design process. Our study represents a significant advance in the field of hybrid renewable energy systems for EV charging stations. Full article
(This article belongs to the Special Issue Multi-Objective Optimization and Decision Support Systems)
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23 pages, 28194 KiB  
Article
An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems
by Carlos Alberto Barrera-Diaz, Amir Nourmohammadi, Henrik Smedberg, Tehseen Aslam and Amos H. C. Ng
Mathematics 2023, 11(6), 1527; https://doi.org/10.3390/math11061527 - 21 Mar 2023
Cited by 2 | Viewed by 2006
Abstract
In today’s uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high [...] Read more.
In today’s uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high efficiency constitutes a challenging task for stakeholders and decision makers when they face the trade-off decisions inherent in these complex systems. This study addresses work task and resource allocations to workstations together with buffer capacity allocation in an RMS. The aim is to simultaneously maximize throughput and to minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach supports decision makers with knowledge discovery to further understand RMS design. In particular, this study presents a customized SMO approach combined with a novel flexible pattern mining method for optimizing an RMS and conducts post-optimal analyses. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision support and production planning of an RMS. Full article
(This article belongs to the Special Issue Multi-Objective Optimization and Decision Support Systems)
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