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Algorithms and Models for Dynamic Multiple Criteria Decision Making

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A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (1 August 2021) | Viewed by 20818

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Special Issue Editors

Dr. Francisco Javier Santos-Arteaga

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Faculty of Economics and Management, Free University of Bolzano, Piazza Università, 1, 39100 Bolzano BZ, Italy
Interests: decision theory; operations research; information sciences; innovation; economic growth; institutional economics; wine economics; multiple criteria decision making; organ transplantation
Special Issues, Collections and Topics in MDPI journals

Dr. Debora Di Caprio

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Department of Economics and Management, University of Trento, Trento, Italy
Interests: optimization; operations management; mathematical modelling; linear programming; social influence; mathematical programming; economic theory; heuristics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The substantial increment in the applications of multiple criteria decision making (MCDM) techniques observed in recent years illustrates both their malleability and acceptance as problem-solving methods when dealing with conflicting criteria. Despite the impressive evolution of this research area, there is still ample room for additional improvements dealing with unexplored—though essential—research aspects. The induction of standard MCDM methods within the domain of dynamical systems is one of the most immediate ones.

When dealing with conflicting objectives, the design of specific algorithms and heuristic methods seems restricted to their implementation within multiobjective optimization problems, particularly when analyzing complex structures involving dynamic or strategic interactions across variables. The current Special Issue aims at integrating MCDM methods, such as TOPSIS, VIKOR, PROMETHEE and ELECTRE, within the domain of dynamical systems. In particular, it aims at bridging the gap existing between standard MCDM methods—generally implemented within static environments—and the dynamic interactions taking place across variables in many real-life settings.

An intuitive example regarding the preferred type of research is provided by the different dynamic extensions of data envelopment analysis (DEA) introduced in the operational research literature. In addition to the inclusion of a temporal dimension across MCDM methods, any potential development in techniques such as dynamic DEA or novel extensions of multiobjective optimization problems involving dynamic interactions across variables are welcome as contributions to the current Special Issue.

Dr. Francisco Javier Santos-Arteaga
Dr. Debora Di Caprio
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. Algorithms 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 1600 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
Dynamical systems
Algorithms
Data envelopment analysis
TOPSIS
VIKOR
PROMETHEE
ELECTRE
Multiobjective optimization
Uncertainty
Artificial Intelligence
Machine learning

Published Papers (7 papers)

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Editorial

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3 pages, 166 KiB

Open AccessEditorial

Special Issue on Algorithms and Models for Dynamic Multiple Criteria Decision Making

by Debora Di Caprio and Francisco Javier Santos Arteaga

Algorithms 2021, 14(8), 233; https://doi.org/10.3390/a14080233 - 08 Aug 2021

Viewed by 1658

Abstract

The current Special Issue contains six papers focused on Multiple Criteria Decision Making (MCDM) problems and the formal techniques applied to derive consistent rankings of them [...] Full article

(This article belongs to the Special Issue Algorithms and Models for Dynamic Multiple Criteria Decision Making)

Research

Jump to: Editorial

20 pages, 469 KiB

Open AccessArticle

Constrained Eigenvalue Minimization of Incomplete Pairwise Comparison Matrices by Nelder-Mead Algorithm

by Hailemariam Abebe Tekile, Michele Fedrizzi and Matteo Brunelli

Algorithms 2021, 14(8), 222; https://doi.org/10.3390/a14080222 - 23 Jul 2021

Cited by 5 | Viewed by 2688

Abstract

Pairwise comparison matrices play a prominent role in multiple-criteria decision-making, particularly in the analytic hierarchy process (AHP). Another form of preference modeling, called an incomplete pairwise comparison matrix, is considered when one or more elements are missing. In this paper, an algorithm is proposed for the optimal completion of an incomplete matrix. Our intention is to numerically minimize a maximum eigenvalue function, which is difficult to write explicitly in terms of variables, subject to interval constraints. Numerical simulations are carried out in order to examine the performance of the algorithm. The results of our simulations show that the proposed algorithm has the ability to solve the minimization of the constrained eigenvalue problem. We provided illustrative examples to show the simplex procedures obtained by the proposed algorithm, and how well it fills in the given incomplete matrices. Full article

(This article belongs to the Special Issue Algorithms and Models for Dynamic Multiple Criteria Decision Making)

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22 pages, 3679 KiB

Open AccessArticle

Development of Multi-Actor Multi-Criteria Analysis Based on the Weight of Stakeholder Involvement in the Assessment of Natural–Cultural Tourism Area Transportation Policies

by Heru Purboyo Hidayat Putro, Pradono Pradono and Titus Hari Setiawan

Algorithms 2021, 14(7), 217; https://doi.org/10.3390/a14070217 - 20 Jul 2021

Cited by 5 | Viewed by 2639

Abstract

Multi-actor multi-criteria analysis (MAMCA) was developed with a process involving the participation of various stakeholders. Stakeholders express various criteria as measures for the achievement of their respective goals. In general, the assessment of each stakeholder is considered to have the same weight. In reality, the weight of each stakeholder’s involvement in policy decision making is not the same. For example, the government’s assessment weight will be different from those of local business actors. In this study, the authors developed a multi-actor multi-criteria analysis method by adding the weight of stakeholder involvement when making decisions about transportation policies that support sustainable mobility in protected natural–cultural tourism areas. The weight of involvement was developed through stakeholder participation. Stakeholders were asked to provide weights for all stakeholders other than themselves using the AHP method. The results of this weighting were then averaged and considered as the stakeholder assessment weights. Adding stakeholder weighting can also improve the quality of decisions by avoiding bias and following the principle of fairness in the assessment. Full article

(This article belongs to the Special Issue Algorithms and Models for Dynamic Multiple Criteria Decision Making)

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17 pages, 6765 KiB

Open AccessArticle

Design Optimization of Interfacing Attachments for the Deployable Wing of an Unmanned Re-Entry Vehicle

by Francesco Di Caprio, Roberto Scigliano, Roberto Fauci and Domenico Tescione

Algorithms 2021, 14(5), 141; https://doi.org/10.3390/a14050141 - 28 Apr 2021

Cited by 3 | Viewed by 2334

Abstract

Re-entry winged body vehicles have several advantages w.r.t capsules, such as maneuverability and controlled landing opportunity. On the other hand, they show an increment in design level complexity, especially from an aerodynamic, aero-thermodynamic, and structural point of view, and in the difficulties of housing in operative existing launchers. In this framework, the idea of designing unmanned vehicles equipped with deployable wings for suborbital flight was born. This work details a preliminary study for identifying the best configuration for the hinge system aimed at the in-orbit deployment of an unmanned re-entry vehicle’s wings. In particular, the adopted optimization methodology is described. The adopted approach uses a genetic algorithm available in commercial software in conjunction with fully parametric models created in FEM environments and, in particular, it can optimize the hinge position considering both the deployed and folded configuration. The results identify the best hinge configuration that minimizes interface loads, thus, realizing a lighter and more efficient deployment system. Indeed, for such a category of vehicle, it is mandatory to reduce the structural mass, as much as possible in order to increase the payload and reduce service costs. Full article

(This article belongs to the Special Issue Algorithms and Models for Dynamic Multiple Criteria Decision Making)

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26 pages, 2275 KiB

Open AccessArticle

PROMETHEE-SAPEVO-M1 a Hybrid Approach Based on Ordinal and Cardinal Inputs: Multi-Criteria Evaluation of Helicopters to Support Brazilian Navy Operations

by Miguel Ângelo Lellis Moreira, Igor Pinheiro de Araújo Costa, Maria Teresa Pereira, Marcos dos Santos, Carlos Francisco Simões Gomes and Fernando Martins Muradas

Algorithms 2021, 14(5), 140; https://doi.org/10.3390/a14050140 - 27 Apr 2021

Cited by 40 | Viewed by 3506

Abstract

This paper presents a new approach based on Multi-Criteria Decision Analysis (MCDA), named PROMETHEE-SAPEVO-M1, through its implementation and feasibility related to the decision-making process regarding the evaluation of helicopters of attack of the Brazilian Navy. The proposed methodology aims to present an integration of ordinal evaluation into the cardinal procedure from the PROMETHEE method, enabling to perform qualitative and quantitative data and generate the criteria weights by pairwise evaluation, transparently. The modeling provides three models of preference analysis, as partial, complete, and outranking by intervals, along with an intra-criterion analysis by veto threshold, enabling the analysis of the performance of an alternative in a specific criterion. As a demonstration of the application, is carried out a case study by the PROMETHEE-SAPEVO-M1 web platform, addressing a strategic analysis of attack helicopters to be acquired by the Brazilian Navy, from the need to be evaluating multiple specifications with different levels of importance within the context problem. The modeling implementation in the case study is made in detail, first performing the alternatives in each criterion and then presenting the results by three different models of preference analysis, along with the intra-criterion analysis and a rank reversal procedure. Moreover, is realized a comparison analysis to the PROMETHEE method, exploring the main features of the PROMETHEE-SAPEVO-M1. Moreover, a section of discussion is presented, exposing some features and main points of the proposal. Therefore, this paper provides a valuable contribution to academia and society since it represents the application of an MCDA method in the state of the art, contributing to the decision-making resolution of the most diverse real problems. Full article

(This article belongs to the Special Issue Algorithms and Models for Dynamic Multiple Criteria Decision Making)

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17 pages, 503 KiB

Open AccessEditor’s ChoiceArticle

Multiple Criteria Decision Making and Prospective Scenarios Model for Selection of Companies to Be Incubated

by Altina S. Oliveira, Carlos F. S. Gomes, Camilla T. Clarkson, Adriana M. Sanseverino, Mara R. S. Barcelos, Igor P. A. Costa and Marcos Santos

Algorithms 2021, 14(4), 111; https://doi.org/10.3390/a14040111 - 30 Mar 2021

Cited by 47 | Viewed by 3474

Abstract

This paper proposes a model to evaluate business projects to get into an incubator, allowing to rank them in order of selection priority. The model combines the Momentum method to build prospective scenarios and the AHP-TOPSIS-2N Multiple Criteria Decision Making (MCDM) method to rank the alternatives. Six business projects were evaluated to be incubated. The Momentum method made it possible for us to create an initial core of criteria for the evaluation of incubation projects. The AHP-TOPSIS-2N method supported the decision to choose the company to be incubated by ranking the alternatives in order of relevance. Our evaluation model has improved the existing models used by incubators. This model can be used and/or adapted by any incubator to evaluate the business projects to be incubated. The set of criteria for the evaluation of incubation projects is original and the use of prospective scenarios with an MCDM method to evaluate companies to be incubated does not exist in the literature. Full article

(This article belongs to the Special Issue Algorithms and Models for Dynamic Multiple Criteria Decision Making)

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16 pages, 2843 KiB

Open AccessArticle

Number of Financial Indicators as a Factor of Multi-Criteria Analysis via the TOPSIS Technique: A Municipal Case Study

by Roman Vavrek, Jiří Bečica, Viera Papcunová, Petra Gundová and Jana Mitríková

Algorithms 2021, 14(2), 64; https://doi.org/10.3390/a14020064 - 19 Feb 2021

Cited by 8 | Viewed by 2733

Abstract

Multi-criteria analysis is a decision-making and efficiency assessment tool for application in both the private and public sectors. Its application is preceded by the selection of suitable indicators and a homogenous set of variants, as well as suitable methods based on the nature of the input data. The goal of the submitted research is to highlight the importance of selecting suitable indicators using a case study assessment of the financial health of a municipality—more precisely, the efficiency of management of this municipality. Four key indicators, thirty-two homogenous subjects, and one multi-criteria analysis method were identified in this study based on the theoretical foundations of the specific issue. These elements were processed into a total of 14 variants depending on the number of assessed indicators. Then, these results were subjected to statistical verification alongside verification using the Jaccard index. Based on the acquired results, we highlight the need for correct and expert identification of the relevant sets of alternatives (the criteria matrix) and expert discussion, which should precede the selection of the assessed indicators and objectify this selection process as much as possible. Assessment based on a low number of indicators was shown to be insufficient, highly variable, and diverse, and these differences were partially eliminated as the number of assessed indicators increased. Full article

(This article belongs to the Special Issue Algorithms and Models for Dynamic Multiple Criteria Decision Making)

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