Decision-making model for designing telecom products/services based on customer preferences and non-preferences

The design of the packages of products/services to be offered by a telecom company to its clients is a complex decision-making process that must consider different criteria to achieve both customer satisfaction and optimization of the company’s resources. In this process, Intuitionistic Fuzzy Sets (IFSs) can be used to manage uncertainty and better represent both preferences and non-preferences expressed by people who value each proposed alternative. We present a novel approach to design/develop new products/services that combines the Lean Six Sigma methodology with IFSs. Its main contribution comes from considering both preferences and nonpreferences expressed by real clients, whereas existing proposals only consider their preferences. By also considering their non-preferences, it provides an additional capacity to manage the high uncertainty in the selection of the commercial plan that best suits each client’s needs. Thus, client satisfaction is increased while improving the company’s corporate image, which will lead to customer loyalty and increased revenue. To validate the presented proposal, it has been applied to a real case study of the telecom sector, in which 2135 users have participated. The results obtained have been analysed and compared with those obtained with a model that does not consider the non-preferences expressed by users.Spanish Ministry of Science and Innovation (State Research Agency)Junta de Andalucia PID2019-103880RB-I00 PID2019-109644RB-I00 PY20_0067

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

Intuitionistic linguistic multi-attribute decision making algorithm based on integrated distance measure

This study aims to integrate the intuitionistic linguistic multi-attribute decision making (MADM) method which builds upon an integrated distance measure into supplier evaluation and selection problems. More specifically, an intuitionistic linguistic integrated distance measure based on ordered weighted averaging operator (OWA) and weighted average approach is presented and applied. The desirable characteristics and families of the developed distance operator are further explored. In addition, based on the proposed distance measure, a supplier selection problem for an automobile factory is used to test the practicality of its framework. The effectiveness and applicability of the presented framework for supplier selection are examined by carrying comparative analysis against the existing techniques of aggregation

Fuzzy Analytic Hierarchy Process Utilization in Government Projects : A Systematic Review of Implementation Processes

Uncertain assessments challenge the aggregation of expert knowledge in the field of decision-making. Valuable, yet sometimes hesitant, insight of expert decision makers needs to be converted into numerically comparative form in the age of information management. . Fuzzy Analytic Hierarchy Process (FAHP) enables the comparison of decision elements through expert judgements, even when the information at hand is uncertain. The present study explores Fuzzy Analytic Hierarchy Process (FAHP) implementation in government projects in a systematic literature review. Theoretical framework for Analytic Hierarchy Process (AHP), Fuzzy Set Theory (FST) and their combination, namely Fuzzy Analytic Hierarchy Process (FAHP) is provided. The systematic literature review categorizes research results under three categories and examines each paper by utilizing review questions. Three main application purposes rise from the literature review; policy planning and assessment, project selection and project and performance evaluation. Overall implementation processes of the three application areas are discussed. The conclusion provides comprehensive evaluation of the approach and considerations for practitioners.Asiantuntijanäkemysten epävarmuus vaikeuttaa tiedon keräämistä päätöksenteossa. Päätöksentekoprosessin kannalta arvokkaat, vaikkakin joskus epävarmat, asiantuntijanäkemykset tulee voida muuttaa numerollisesti vertailtavaan muotoon tietojohtamisen aikakautena. Sumea Analyyttinen Hierarkiaprosessi mahdollistaa päätöksenteossa käytettävien elementtien vertailun asiantuntija-arviointien avulla, jopa silloin kun käytettävissä oleva tieto on epävarmaa. Opinnäytetyössä tutkitaan systemaattisen kirjallisuuskatsauksen keinoin Sumean Analyyttisen Hierarkiaprosessin, eng. Fuzzy Analytic Hierarchy Process (FAHP), implementointia julkishallinnon hankkeissa. Tutkimus sisältää teoreettisen viitekehyksen Analyyttisen Hierarkiaprosessin, Sumean joukko-opin, eng. Fuzzy Set Theory (FST) ja niiden yhdistelmän, Sumean Analyyttisen Hierarkiaprosessin, eng. Fuzzy Analytic Hierarchy Process (FAHP), ymmärtämisen tueksi. Systemaattisen kirjallisuuskatsauksen myötä valittu aineisto luokitellaan kolmeen kategoriaan ja jokaista tutkimusta tarkastellaan ennalta määrättyjen kysymysten avulla. Systemaattisen kirjallisuuskatsaukseen myötä valittujen tutkimusten kolme olennaisinta käyttötarkoitusta ovat; käytännön suunnittelu ja arviointi, hankevalinta sekä hankkeiden ja suoritusten arviointi. Aineiston luokittelun jälkeen tutkimus etenee tarkastelemaan erilaisiin käyttötarkoituksiin suunnattujen Sumean Analyyttisen Hierarkiaprosessi -metodin implementointiprosesseja. Johtopäätös -osio tarjoaa pohdintaa ja huomioita siitä, miten päätöksentekijät voivat suhtautua Sumean Analyyttisen Hierarkiaprosessin hyödyntämiseen julkishankkeiden yhteydessä

Fuzzy Techniques for Decision Making 2018

Zadeh's fuzzy set theory incorporates the impreciseness of data and evaluations, by imputting the degrees by which each object belongs to a set. Its success fostered theories that codify the subjectivity, uncertainty, imprecision, or roughness of the evaluations. Their rationale is to produce new flexible methodologies in order to model a variety of concrete decision problems more realistically. This Special Issue garners contributions addressing novel tools, techniques and methodologies for decision making (inclusive of both individual and group, single- or multi-criteria decision making) in the context of these theories. It contains 38 research articles that contribute to a variety of setups that combine fuzziness, hesitancy, roughness, covering sets, and linguistic approaches. Their ranges vary from fundamental or technical to applied approaches

Fuzzy Logic in Decision Support: Methods, Applications and Future Trends

During the last decades, the art and science of fuzzy logic have witnessed significant developments and have found applications in many active areas, such as pattern recognition, classification, control systems, etc. A lot of research has demonstrated the ability of fuzzy logic in dealing with vague and uncertain linguistic information. For the purpose of representing human perception, fuzzy logic has been employed as an effective tool in intelligent decision making. Due to the emergence of various studies on fuzzy logic-based decision-making methods, it is necessary to make a comprehensive overview of published papers in this field and their applications. This paper covers a wide range of both theoretical and practical applications of fuzzy logic in decision making. It has been grouped into five parts: to explain the role of fuzzy logic in decision making, we first present some basic ideas underlying different types of fuzzy logic and the structure of the fuzzy logic system. Then, we make a review of evaluation methods, prediction methods, decision support algorithms, group decision-making methods based on fuzzy logic. Applications of these methods are further reviewed. Finally, some challenges and future trends are given from different perspectives. This paper illustrates that the combination of fuzzy logic and decision making method has an extensive research prospect. It can help researchers to identify the frontiers of fuzzy logic in the field of decision making

Managing Incomplete Preference Relations in Decision Making: A Review and Future Trends

In decision making, situations where all experts are able to efficiently express their preferences over all the available options are the exception rather than the rule. Indeed, the above scenario requires all experts to possess a precise or sufficient level of knowledge of the whole problem to tackle, including the ability to discriminate the degree up to which some options are better than others. These assumptions can be seen unrealistic in many decision making situations, especially those involving a large number of alternatives to choose from and/or conflicting and dynamic sources of information. Some methodologies widely adopted in these situations are to discard or to rate more negatively those experts that provide preferences with missing values. However, incomplete information is not equivalent to low quality information, and consequently these methodologies could lead to biased or even bad solutions since useful information might not being taken properly into account in the decision process. Therefore, alternative approaches to manage incomplete preference relations that estimates the missing information in decision making are desirable and possible. This paper presents and analyses methods and processes developed on this area towards the estimation of missing preferences in decision making, and highlights some areas for future research

A heterogeneous multi-criteria multi-expert decision-support system for scoring combinations of flood mitigation and recovery options

In this study, we developed an innovative operational decision-support system (DSS) based on flood data and mitigation or recovery options, that can be used by both naïve and expert users to score portfolios of flood mitigation or recovery measures. The DSS combines exposure (i.e., economic, social, or environmental values at risk) and resilience (i.e., protection of the main equilibrium functions of human and physical systems). Experts from different fields define indices and functions, stakeholders express their attitudes towards risk, relative weights, and risk perceptions, and both groups use a shared learning process for risk assessment. The DSS algorithms include the "technique for order performance by similarity to ideal solution" (TOPSIS) and the "basic linguistic term set" (BLTS) methods for heterogeneous multi-criteria multi-expert decision-making. Decisions are illustrated using fixed or bounded values of flood depth, duration, and frequency, with plausible parameter values, for a case study of Cesenatico. The best mitigation option was construction of sand dunes and development of evacuation plans, which achieved 32% of the potential net benefit. The best recovery option was construction of sand dunes and development of evacuation plans and insurance schemes, which achieved 42% of the potential net benefit. Mitigation options outperformed recovery options whenever the relative importance of exposure with respect to resilience was greater than 95%. Sensitivity analysis revealed that the best mitigation option was most robust with respect to flood duration and depth; the best recovery option was most robust with respect to the relative weights attached to economic, social, and environmental factors. Both options were similarly robust with respect to interdependencies between the options