Selection of the best maintenance approach in the maritime industry under fuzzy multiple attributive group decision-making environment

Many maintenance approaches have been developed and applied successfully in a variety of sectors such as aviation and nuclear industries over the years. Some of those have also been employed in the maritime industry such as condition based maintenance; however, choosing the best maintenance approach has always been a big challenge due to the involvement of many attributes and alternatives which can also be associated with multiple experts and vague information. In order to accommodate these aspects, and as part of an overall novel Reliability and Criticality Based Maintenance strategy, an existing fuzzy multiple attributive group decision-making technique is employed in this study, which is further enhanced with the use of Analytical Hierarchy Process to obtain a better weighting of the maintenance attributes used. The fuzzy multiple attributive group decision-making technique has three distinctive stages, namely rating, aggregation and selection in which multiple experts’ subjective judgments are processed and aggregated to be able to arrive at a ranking for a finite number of maintenance options. To demonstrate the applicability in a real-life industrial context, the technique is exemplified by selecting the best maintenance approach for shipboard equipment such as the diesel generator system of a vessel. The results denote that preventive maintenance is the best approach closely followed by predictive maintenance, thus steering away from the ship corrective maintenance framework and increasing overall ship system reliability and availability

A cardinal dissensus measure based on the Mahalanobis distance

Producción CientíficaIn this paper we address the problem of measuring the degree of consensus/dissensus in a context where experts or agents express their opinions on alternatives or issues by means of cardinal evaluations. To this end we propose a new class of distance-based consensus model, the family of the Mahalanobis dissensus measures for profiles of cardinal values. We set forth some meaningful properties of the Mahalanobis dissensus measures. Finally, an application over a real empirical example is presented and discussed.Ministerio de Economía, Industria y Competitividad (Project CGL2008-06003-C03-03/CLI)Ministerio de Economía, Industria y Competitividad (Project ECO2012-32178)Ministerio de Economía, Industria y Competitividad (Project ECO2012-31933

A systematic review on multi-criteria group decision-making methods based on weights: analysis and classification scheme

Interest in group decision-making (GDM) has been increasing prominently over the last decade. Access to global databases, sophisticated sensors which can obtain multiple inputs or complex problems requiring opinions from several experts have driven interest in data aggregation. Consequently, the field has been widely studied from several viewpoints and multiple approaches have been proposed. Nevertheless, there is a lack of general framework. Moreover, this problem is exacerbated in the case of experts’ weighting methods, one of the most widely-used techniques to deal with multiple source aggregation. This lack of general classification scheme, or a guide to assist expert knowledge, leads to ambiguity or misreading for readers, who may be overwhelmed by the large amount of unclassified information currently available. To invert this situation, a general GDM framework is presented which divides and classifies all data aggregation techniques, focusing on and expanding the classification of experts’ weighting methods in terms of analysis type by carrying out an in-depth literature review. Results are not only classified but analysed and discussed regarding multiple characteristics, such as MCDMs in which they are applied, type of data used, ideal solutions considered or when they are applied. Furthermore, general requirements supplement this analysis such as initial influence, or component division considerations. As a result, this paper provides not only a general classification scheme and a detailed analysis of experts’ weighting methods but also a road map for researchers working on GDM topics or a guide for experts who use these methods. Furthermore, six significant contributions for future research pathways are provided in the conclusions.The first author acknowledges support from the Spanish Ministry of Universities [grant number FPU18/01471]. The second and third author wish to recognize their support from the Serra Hunter program. Finally, this work was supported by the Catalan agency AGAUR through its research group support program (2017SGR00227). This research is part of the R&D project IAQ4EDU, reference no. PID2020-117366RB-I00, funded by MCIN/AEI/10.13039/ 501100011033.Peer ReviewedPostprint (published version

Metodología para la búsqueda de consenso en toma de decisiones grupales multicriterio (MCGDM). Caso de estudio en gestión ambiental

La toma de decisiones grupal busca agregar opiniones de varios individuos en una decisión consensuada. Muchos autores coinciden en que nuestra sociedad moderna es en esencia un problema de decisiones grupal. El conflicto en este tipo de decisiones se presenta debido a la asimetría de información presente en las motivaciones y actitudes de cada miembro del grupo mientras se intenta llegar a una decisión grupal agregada. Esta tesis presenta un aporte original al proponer una metodología novedosa para búsqueda de consenso suave (El consenso bajo un cierto grado de acuerdo) en decisiones grupales CGDM, incluyendo la medición y análisis de influencias entre ellos como herramienta para asesorar el cambio de sus preferencias y persuadir a los expertos en situaciones con alta discordancia. Mediante el desarrollo de dos estudios de caso, esta tesis propone un método para reducir las inconsistencias en las matrices de comparación por pares utilizando un algoritmo de censo de tríadas. Además, el documento presenta dos métodos para calcular el peso de los decisores: el primero a través del cálculo de la influencia con una medida de centralidad inversa normalizada, obtenida del tema formal del análisis de redes sociales. El segundo, a través de la modificación sobre la medida de influencia propuesta en el método DEMATEL.Group decision making seeks to add opinions for several individuals in a consensual decision. Many authors agree that our modern society is essentially a problem of group decisions. The conflict in this type of decisions arises due to the symmetry of information present in the motivations and attitudes of each member of the group while trying to reach an aggregate group decision. This thesis presents an original contribution by proposing a novel methodology to seek soft consensus (The consensus under a certain degree of consensus) in CGDM group decisions, including the measurement and analysis of influences between them as a tool to advise the change of their preferences and persuade experts in situations with high discordance. Through the development of two case studies, this thesis proposes a method for reducing inconsistencies in pairwise comparison matrices using a triad census algorithm. Additionally, the document introduces two methods for calculating the weight of decision-makers: the first through the calculation of influence with a normalized inverse centrality measure, obtained from the formal topic of social network analysis. The second one, through the modification over the influence measure proposed in the DEMATEL method.Doctor en Ingeniería - Industria y Organizaciones. Línea de Investigación: Métodos y modelos de optimización, logística y estadística en Ingeniería Industrial y Administrativa.Doctorad

Integrated Frameworks for Effective Multi-criteria Decision Making in Reliability Centred Maintenance of Industrial Machines

No abstract availabl

An attribute weight based feedback model for multiple attributive group decision analysis problems with group consensus requirements in evidential reasoning context

In an evidential reasoning context, a group consensus (GC) based approach can model multiple attributive group decision analysis problems with GC requirements. The predefined GC is reached through several rounds of group analysis and discussion (GAD) in the approach. However, the GAD with no guidance may not be the most appropriate way to reach the predefined GC because several rounds of GAD will spend a lot of time of all experts and yet cannot help them to effectively emphasize on the assessments which primarily damage the GC. In this paper, an attribute weight based feedback model is constructed to effectively identify the assessments primarily damaging the GC and accelerate the GC convergence. Considering important attributes with the weights more than or at least equal to the mean of the weights of all attributes, the feedback model constructs identification rules to identify the assessments damaging the GC for the experts to renew. In addition, a suggestion rule is introduced to generate appropriate recommendations for the experts to renew their identified assessments. The identification rules are constructed at three levels including the attribute, alternative and global levels. The feedback model is used to solve an engineering project management software selection problem to demonstrate its detailed implementation process, its validity and applicability, and its advantages compared with the GC based approach.Decision analysis Multiple attributive group decision analysis Evidential reasoning approach Group consensus Attribute weight Feedback model

Dynamics under Uncertainty: Modeling Simulation and Complexity

The dynamics of systems have proven to be very powerful tools in understanding the behavior of different natural phenomena throughout the last two centuries. However, the attributes of natural systems are observed to deviate from their classical states due to the effect of different types of uncertainties. Actually, randomness and impreciseness are the two major sources of uncertainties in natural systems. Randomness is modeled by different stochastic processes and impreciseness could be modeled by fuzzy sets, rough sets, Dempster–Shafer theory, etc