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Mathematics
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Multiobjective Optimization: Methodology, Computational Implementation and Real Models
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Multiobjective Optimization: Methodology, Computational Implementation and Real Models

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 12158

Special Issue Editors

Prof. Dr. Mariano Luque

E-Mail Website
Guest Editor
Department of Applied Economics (Mathematics), University of Málaga, Calle Ejido 6, 29071 Málaga, Spain
Interests: multi-criteria decision making; interactive and evolutionary multi-objective optimization; metaheuristic algorithms; applications in fields such as economics of education, workers' satisfaction
Dr. Ana B. Ruiz

E-Mail Website
Guest Editor
Department of Applied Economics (Mathematics), University of Málaga, Calle Ejido 6, 29071 Málaga, Spain
Interests: multiobjective optimization; evolutionary algorithms; preference-based methods; multiple criteria decision making

Special Issue Information

Dear Colleagues,

A lot of real-world optimization problems are about maximizing or minimizing various objective functions which are often in conflict with each other. These functions are often complex and evaluating them can be computationally costly in real applications. Multiobjective optimization (MOP) is the discipline that tries to find solutions, called Pareto optimal or efficient, to this type of problems. Indeed, multiobjective optimization enables the decision-making task inherent to the process for finding a suitable final solution. Furthermore, computational developments and software become essential to implement the application in practice of new theory and methodology.

In this special issue, we invite papers with a significant amount of new scientific contribution in theory, computation, and practical applications that address new trends in multiobjective optimization, and the relationships among existing approaches. Relevant solution approaches include mathematical programming as well as heuristic and meta-heuristic approaches, such as these in evolutionary multiobjective optimization (EMO). Real applications in different research fields such as economy, management, science, health, or industry are welcome.

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

  • Theoretical aspects in MOP
  • Interactive MOP methods
  • Combinatorial MOP
  • Stochastic MOP
  • Dynamic MOP
  • Decomposition-based EMO approaches
  • Interactive and preference-based EMO algorithms
  • Multiple criteria decision making
  • Applications with real data
  • Software implementation

Prof. Dr. Mariano Luque
Dr. Ana B. Ruiz
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. 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

  • Multiple criteria decision making
  • Multiobjective programming
  • Evolutionary multiobjective optimization
  • Preferences
  • Interactive methods
  • Combinatorial optimization
  • Dynamic optimization
  • Real applications

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Published Papers (4 papers)

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26 pages, 1627 KiB  
Article
A DANP-Based NDEA-MOP Approach to Evaluating the Patent Commercialization Performance of Industry–Academic Collaborations
by Chi-Yo Huang, Min-Jen Yang, Jeen-Fong Li and Hueiling Chen
Mathematics 2021, 9(18), 2280; https://doi.org/10.3390/math9182280 - 16 Sep 2021
Cited by 4 | Viewed by 2194
Abstract
The industry–academic collaboration (IAC) in developed and developing countries enables these economies to gain momentum in continuous innovation and, thus, economic growth. Patent commercialization is one major channel of knowledge flow in IAC. However, very few studies consider the flow of knowledge between [...] Read more.
The industry–academic collaboration (IAC) in developed and developing countries enables these economies to gain momentum in continuous innovation and, thus, economic growth. Patent commercialization is one major channel of knowledge flow in IAC. However, very few studies consider the flow of knowledge between industrial firms and universities. Moreover, ways that the patent commercialization performance of IACs can be evaluated are rarely discussed. Therefore, defining an analytic framework to evaluate the performance of IAC from the aspect of patent commercialization is critical. Traditionally, data envelopment analysis (DEA) models have widely been adopted in performance evaluation. However, traditional DEA models cannot accurately evaluate the performance of IACs with complex university–industry interconnections, the internal linkages, or linking activities of knowledge-flow within the decision-making units (DMUs), i.e., the IACs. In order to solve the abovementioned problems, this study defines a multiple objective programming (MOP)-based network DEA (NDEA), with weighting derived from the decision-making trial and evaluation laboratory (DEMATEL)-based analytic network process (ANP), or the DANP. The proposed analytic framework can evaluate the efficiency of decision-making units (DMUs) with a network structure (e.g., supply chains, strategic alliances, etc.) based on the weights that have been derived, based on experts’ opinions. An empirical study based on the performance of the patent commercialization of Taiwanese IACs was used to demonstrate the feasibility of the proposed framework. The results of the empirical research can serve as a basis for improving the performance of IAC. Full article
(This article belongs to the Special Issue Multiobjective Optimization: Methodology, Computational Implementation and Real Models)
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25 pages, 21983 KiB  
Article
Modeling, Simulation and Uncertain Optimization of the Gun Engraving System
by Tong Xin, Guolai Yang, Fengjie Xu, Quanzhao Sun and Alexandi Minak
Mathematics 2021, 9(4), 398; https://doi.org/10.3390/math9040398 - 18 Feb 2021
Cited by 5 | Viewed by 3408
Abstract
The system designed to accomplish the engraving process of a rotating band projectile is called the gun engraving system. To obtain higher performance, the optimal design of the size parameters of the gun engraving system was carried out. First, a fluid–solid coupling computational [...] Read more.
The system designed to accomplish the engraving process of a rotating band projectile is called the gun engraving system. To obtain higher performance, the optimal design of the size parameters of the gun engraving system was carried out. First, a fluid–solid coupling computational model of the gun engraving system was built and validated by the gun launch experiment. Subsequently, three mathematic variable values, like performance evaluation indexes, were obtained. Second, a sensitivity analysis was performed, and four high-influence size parameters were selected as design variables. Finally, an optimization model based on the affine arithmetic was set up and solved, and then the optimized intervals of performance evaluation indexes were obtained. After the optimal design, the percent decrease of the maximum engraving resistance force ranged from 6.34% to 18.24%; the percent decrease of the maximum propellant gas temperature ranged from 1.91% to 7.45%; the percent increase of minimum pressure wave of the propellant gas ranged from 0.12% to 0.36%. Full article
(This article belongs to the Special Issue Multiobjective Optimization: Methodology, Computational Implementation and Real Models)
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22 pages, 3461 KiB  
Article
Multi-Objective Two-Stage Stochastic Programming Model for a Proposed Casualty Transportation System in Large-Scale Disasters: A Case Study
by Nadide Caglayan and Sule Itir Satoglu
Mathematics 2021, 9(4), 316; https://doi.org/10.3390/math9040316 - 5 Feb 2021
Cited by 14 | Viewed by 3616
Abstract
Disaster management is a process that includes mitigation, preparedness, response and recovery stages. Operational strategies covering all stages must be developed in order to alleviate the negative effects of the disasters. In this study, we aimed at minimizing the number of casualties that [...] Read more.
Disaster management is a process that includes mitigation, preparedness, response and recovery stages. Operational strategies covering all stages must be developed in order to alleviate the negative effects of the disasters. In this study, we aimed at minimizing the number of casualties that could not be transported to the hospitals after the disaster, the number of additional ambulances required in the response stage, and the total transportation time. Besides, we assumed that a data-driven decision support tool is employed to track casualties and up-to-date hospital capacities, so as to direct the ambulances to the available hospitals. For this purpose, a multi-objective two-stage stochastic programming model was developed. The model was applied to a district in Istanbul city of Turkey, for a major earthquake. Accordingly, the model was developed with a holistic perspective with multiple objectives, periods and locations. The developed multi-objective stochastic programming model was solved using an improved version of the augmented ε-constraint (AUGMECON2) method. Hence, the Pareto optimal solutions set has been obtained and compared with the best solution achieved according to the objective of total transportation time, to see the effect of the ambulance direction decisions based on hospital capacity availability. All of the decisions examined in these comparisons were evaluated in terms of effectiveness and equity. Finally, managerial implication strategies were presented to contribute decision-makers according to the results obtained. Results showed that without implementing a data-driven decision support tool, equity in casualty transportation cannot be achieved among the demand points. Full article
(This article belongs to the Special Issue Multiobjective Optimization: Methodology, Computational Implementation and Real Models)
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19 pages, 289 KiB  
Article
Approximate Efficient Solutions of the Vector Optimization Problem on Hadamard Manifolds via Vector Variational Inequalities
by Gabriel Ruiz-Garzón, Rafaela Osuna-Gómez, Antonio Rufián-Lizana and Beatriz Hernández-Jiménez
Mathematics 2020, 8(12), 2196; https://doi.org/10.3390/math8122196 - 10 Dec 2020
Cited by 2 | Viewed by 1518
Abstract
This article has two objectives. Firstly, we use the vector variational-like inequalities problems to achieve local approximate (weakly) efficient solutions of the vector optimization problem within the novel field of the Hadamard manifolds. Previously, we introduced the concepts of generalized approximate geodesic convex [...] Read more.
This article has two objectives. Firstly, we use the vector variational-like inequalities problems to achieve local approximate (weakly) efficient solutions of the vector optimization problem within the novel field of the Hadamard manifolds. Previously, we introduced the concepts of generalized approximate geodesic convex functions and illustrated them with examples. We see the minimum requirements under which critical points, solutions of Stampacchia, and Minty weak variational-like inequalities and local approximate weakly efficient solutions can be identified, extending previous results from the literature for linear Euclidean spaces. Secondly, we show an economical application, again using solutions of the variational problems to identify Stackelberg equilibrium points on Hadamard manifolds and under geodesic convexity assumptions. Full article
(This article belongs to the Special Issue Multiobjective Optimization: Methodology, Computational Implementation and Real Models)
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