Academic literature on the topic 'Multicriteria Decision Analysi'

Background Patient decision aids should help people make evidence-informed decisions aligned with their values. There is limited guidance about how to achieve such alignment. Purpose To describe the range of values clarification methods available to patient decision aid developers, synthesize evidence regarding their relative merits, and foster collection of evidence by offering researchers a proposed set of outcomes to report when evaluating the effects of values clarification methods. Data Sources MEDLINE, EMBASE, PubMed, Web of Science, the Cochrane Library, and CINAHL. Study Selection We included articles that described randomized trials of 1 or more explicit values clarification methods. From 30,648 records screened, we identified 33 articles describing trials of 43 values clarification methods. Data Extraction Two independent reviewers extracted details about each values clarification method and its evaluation. Data Synthesis Compared to control conditions or to implicit values clarification methods, explicit values clarification methods decreased the frequency of values-incongruent choices (risk difference, –0.04; 95% confidence interval [CI], –0.06 to –0.02; P < 0.001) and decisional conflict (standardized mean difference, –0.20; 95% CI, –0.29 to –0.11; P < 0.001). Multicriteria decision analysis led to more values-congruent decisions than other values clarification methods (χ2 = 9.25, P = 0.01). There were no differences between different values clarification methods regarding decisional conflict (χ2 = 6.08, P = 0.05). Limitations Some meta-analyses had high heterogeneity. We grouped values clarification methods into broad categories. Conclusions Current evidence suggests patient decision aids should include an explicit values clarification method. Developers may wish to specifically consider multicriteria decision analysis. Future evaluations of values clarification methods should report their effects on decisional conflict, decisions made, values congruence, and decisional regret.

Multicriteria decision analysis is a decision support aid touted for its ability to help participants overcome bias and make holistic assessments. However, few offer empirical tests of this thesis. This research examines the use of multicriteria decision analysis to implement the Southern Nevada Public Lands Management Act. The Act called upon federal, regional, and local agencies to develop a connected system of parks, trails, and natural areas throughout Nevada. The partners used multicriteria decision analysis to make decisions about which parks, trails, and natural areas projects to fund. We assess the extent of political and cognitive biases among the participants when using the multicriteria decision analysis process. We find no strong evidence of strategic behavior, a finding that highlights many of the celebratory claims made about multicriteria decision analysis. However, we also note a preference for projects adjacent to high-income areas as well as the presence of cognitive biases in the assignment of scores to projects.

A number of multicriteria decisions must be made during construction investment processes. A number of support systems for multicriteria tasks of construction investment processes are available. Part of them is reviewed in this article. Effectiveness of the construction investment process is greatly influenced by an appropriate formulation of a construction contract. In order to formulate effective construction contract, multicriteria techniques for evaluation and comparison of the contracts must be created. Beside technical, organisational and economic aspects of construction, legal aspects of a construction contract must be also analysed in order to create such techniques. Therefore legal decision‐making systems are also reviewed in the article. A conclusion can be made from the review that decision‐making systems for construction contracts are not available currently. One of the main tasks in the creation of multicriteria support systems is the formation of a multicriteria indicator system. In this paper the structure of construction contracts are analysed and a hierarchical model of a construction contract is presented. Construction contracts have to be made evaluated and compared to this model. On the basis of this model, significance of indicators should be determined and a multicriteria decision support system should be created in further research stages.

Decision making process is a though process not only for the management decisions but also for daily decisions. Multicriteria decision making methods were developed to make this process easier. There are many multicriteria decision making methods used in many areas at the present. In this study two of these methods were used, namely ARAS (Additive Ratio Assessment) and COPRAS (Complex Proportional Assessment), for fundamental analysis in investment decisions. Aim of this study is to implement and compare methods on fundamental analysis of firms to make an investment decision. In the study financial ratios of 20 firms from 5 different sectors and 4 different countries, sectoral data and country indicators were used. According to these data, ARAS and COPRAS methods were implemented and although exactly same results were not found, approximately similar results were obtained. The best and the worst companies were same for both methods, even though other rankings differed slightly. Also, same sector selected as best for both methods to invest in.

The tasks of making the most appropriate decisions taking into account a number of criteria are dealt with in variousfields such as engineering, industry, finance, economics and others. If the aim is to arrange the alternatives in a priority lineaccording to quantitative attributes, then multiattribute decision-making methods are suitable. Analysts using these methods usuallydo not take into account initial data errors – deviations in attribute values, in which case the decision may be unreliable. In thisarticle, several statistical analysis methods are proposed for the multicriteria decision to measure reliability: formulation of statisticalhypotheses and calculation of confidence intervals for parameters. Based on statistical analysis results, conclusions about thereliability of a multicriteria decision obtained using the TOPSIS method are formulated.

This thesis aims to demonstrate how these issues can be solved using specific case studies as examples. The first chapter is dedicated to an introduction to the LCA methodology, in which it is also possible to find a literature review focused on the strengths and weaknesses that may characterize LCA. The second part of the chapter details the methods utilized to analyze uncertainty in LCA results, the state of the art for streamlined and predictive approaches and, finally, an overview of a multi-criteria analysis method useful for materials selection. In particular, the uncertainty analysis associated with LCA results may represent the starting point for the development of streamlined LCA approaches and possible methods of forecasting the environmental results of novel technologies. On the other hand, the multi-criteria analysis grounded in the uncertainty analysis presents a robust method of materials selection in support of Ecodesign. In the second chapter, the uncertainty analysis is used to develop a streamlined LCA method founded on the probabilistic underspecification approach, proposed to support the building design process. The case studies analyzed in this section represent a series of residential building assemblies (exterior walls, interior walls, foundations, roofs, floors, windows, doors, exterior finishes) that were used to test the streamlined method and obtain distributions of results using a cradle-to-gate approach along five phases of the building design process. The bill of materials (BOM) of a building assembly can be specified using different levels of information, which can be really generic during the concept design and fully detailed during the executive project. The low-fidelity characterization of a BOM and the uncertainty associated with these low levels of fidelity are systematically quantified through probabilistic underspecification using a hierarchical classification of materials. Quantitative environmental results, processed with uncertainty analysis, were obtained using low-fidelity categories for materials and building assemblies, demonstrating that LCA can be applied not only when a complete and detailed BOM is available but also when fewer details are known. Finally, decision-making at different stages of the design process is sustained by this approach and is based on the use of a comparison indicator. The third chapter advances the research aimed at streamlining the LCA of buildings with probabilistic underspecification and uncertainty analysis. In particular, it investigates whether LCA can be robustly streamlined through an effective and efficient triage of data collection and the consequent selected use of specific and resource-intensive information. In this context, tests were conducted with a series of building typologies (single-family detached houses and multi-family residential buildings), again analyzed with a cradle-to-gate approach. The probabilistic triage approach was tested to clarify how to use probabilistic underspecification and reduce the effort involved in specification by identifying the activities that require careful characterization. With this approach, by specifying only one part of the bill of materials to the highest level of specificity, the results proved to be both reasonably accurate and obtainable with less effort. Impacts such as global warming, acidification, eutrophication, and smog creation were assessed, and the results indicated that just 40-46% of the BOM components represent 75% of the total impacts of both single-family houses and multi-family buildings. Where the second and third chapters were devoted to the streamlined analysis of conventional products, the fourth chapter addresses the use of uncertainty analysis to forecast the environmental burden of an innovative material. Here, a scale-up protocol for an environmental impact assessment is proposed as a means to develop a streamlined ex-ante LCA approach. The novel element of this chapter consists of the adopted scale-up protocol. It does not rely on primary data collected by monitoring real industrial systems, as these data do not yet exist for the product of interest; instead, data measured in a plant at the pilot scale are used alongside data simulated from thermo-chemical considerations based on the stoichiometry of the considered reaction. The scale-up protocol is described and then applied to the case of polybutylene succinate (PBS), a biopolymer that is gaining attention (particularly as a replacement for polyolefins) and is obtained from bio-based succinic acid. Monte Carlo simulation was used to process the uncertainty data for all of the assessments, and a sensitivity analysis was performed to evaluate and compare the different renewable sources and chemical routes available for the production of bio-based succinic acid. The case study of PBS highlights how innovative products can be analyzed without the use of primary data, providing a way to forecast environmental impacts for novel technologies. The advantages of the adopted scale-up methodology consist of the ease of implementation and the possibility of strengthening the Ecodesign approach. In the fifth chapter, a multi-criteria analysis was used to complete the ex-ante LCA results for PBS. The purpose of this analysis was to compare PBS to alternative materials on the basis of more than one property and for use in a specific function. This approach led to the definition of a new concept of the system boundary of the assessment: from cradle to function. The motivation for this alternative strategy stems from the application of the LCA framework to a material to obtain an ecoprofile: the scope of the analysis is generally from cradle to the factory gate, while the unit of mass (or volume) of the material is usually taken as the functional unit for the analysis. However, these methodological choices place relevant limitations on the effectiveness of the assessment. In this chapter, a multi-criteria materials approach was tested using the PBS results to verify and validate the environmental viability of this material’s usage in packaging films. The most novel element of this research is the use of the customized ex-ante LCA and the uncertainty analysis, the latter of which is used to determine the uncertainty in material indices. The results were graphically represented with Ashby plots. When elongation at break and environmental performance were considered, PBS displayed a performance that was better than other traditional polyesters and comparable to the polyolefins considered; performance in terms of this set of properties is particularly beneficial in the case of secondary packaging. In the case of primary packaging, barrier properties acquire major relevance; in this regard, PBS presented among the best trade-offs for the simultaneous optimization of oxygen permeability, elongation at break and environmental impact. Finally, the sixth chapter is devoted to the review of the approaches that were implemented and tested to streamline LCA, highlighting the strengths and weaknesses for each analyzed system and discussing future methodological developments. In particular, the uncertainty analysis based on the Monte Carlo method was used not just to characterize the quality of results but also to develop and implement streamlined approaches. Moreover, the uncertainty analysis proved to be useful for forecasting environmental results for early-stage systems and innovative materials.

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores
The main objective of this dissertation is to perform a Comparative Analysis of different Multicriteria Decision Making Methods applied to real-world problems, in order to produce relevant information to enable the incorporation of those methods on computational platforms. The current document presents a simple case study concerning a decision support application targeted for a real problem regarding retrofitting alternatives of a building with energy efficiency impact. The application process was started with the selection of two Multicriteria Decision Making Methods guided by a preexisting framework, and resulted in the choice of AHP and PROMETHEE II methodologies. These two methods were then combined with three different decision maker profiles (Conservative, Moderate and Aggressive) created by means of risk assessment profiling techniques for portfolio allocation. Afterwards, the chosen decision criteria were disposed in a Risk Pyramid according to their inherent level of risk regarding project evaluation. A match was then performed between the decision maker profiles and each criterion, so as to define a proper set of weights for the decision criteria and preference functions, with corresponding preference and indifference thresholds. Finally, three different sets of results (one for each decision maker profile) were produced using appropriate software, and a Sensitivity Analysis was performed over the criteria to understand their influence on the solution. The general conclusion of this Comparative Analysis is that the increase in the preference modelling ability of the methods brings up the least expected alternatives as recommendations for the decision maker. Besides, we have concluded that the decision profiles that allocate bigger weights to the riskiest criteria are the ones that produce the more dispersed set of results within each method application and within each decision maker profile.

Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 227-231).
Due to their magnitude and longevity, transportation investments can determine the long term success or failure of a transportation system. Thus, it is vital for decision-makers to have deep understanding of the alternatives available before they chose to invest. In this thesis, we examine the current state of the practice for transportation investment decisions. We draw upon the literature and this existing state of the practice to develop a new decision aid which we believe is an improvement over existing aids. We then apply this new decision aid to a transportation investment decision facing the East Japan Railway Company (JR East) and draw conclusions about the usefulness of our new tool. Our decision aid, the CLIOSjre Process, is designed to help decision-makers compare multiple alternatives and make an informed transportation investment decision. The process examines the decision from multiple perspectives where each of these perspectives represents one of the priorities of the decision-maker. By considering each priority separately, the CLIOSjre Process provides a detailed understanding of each alternative. The CLIOSjre Process also combines these individual evaluations into a single overall evaluation of each alternative. This overall evaluation provides the decision-maker with an actionable ranking of the alternatives. In combination, these perspective-specific and overall evaluations of each alternative provide a detailed and holistic understanding of the decision facing the decision-maker. Unlike many other decision aids, the CLIOSjre Process accounts for both the multistakeholder nature of transportation investments and the uncertainty inherent to these decisions. The multifaceted nature of the CLIOSjre Process examines each alternative from multiple perspectives. This approach better facilitates negotiation between stakeholders. In addition, the CLIOSjre Process formally identifies and addresses uncertainty in the analysis - the primary source of risk in transportation investment decisions. Thus, the CLIOSjre Process is a unique multicriteria, multistakeholder decision aid which addresses uncertainty. We hope that this thesis provides the reader with a better understanding of the application, challenges, and opportunities of multicriteria multistakeholder decision aids.
by Timothy Patton Doyle.
S.M. in Transportation

This dissertation presents an application of multicriteria decision analysis to understand and support pricing decisions in fields where goods are unique and described by their characteristics. The specific application area of this research is antique silver objects, where a complete iteration of the multicritia decision process is performed. This includes two problem structurings using SODA which provide rich detail into this application area. Multi-attribute additive models are constructed, with attribute partial value functions elicited using different methods: directly (bisection methods), indirectly (MACBETH and linear interpolation) and with discrete choice experiments. The applicability and advantages of each method is discussed. Additionally, an open source R package to implement the design of discrete choice experiments is created. The multi-attribute models provide key insights into decision maker's reasoning for price; and contrasting different decision maker's models explains the market. A risk adverse relationship between multicriteria model score and price is characterised and various inverse utility functions investigated. Two decision support systems are fully developed to address the needs of Cape silver decision makers in South Africa.

A aplicação de metodologias de análise multicriterial já é utilizada há décadas para análise de investimentos de alto capital, e continuam sendo ferramentas de referência para tomadas de decisão nas organizações em todo mundo. Contudo, não só grandes investimentos são influênciados por variáveis que interagem entre si e precisam ser levadas em consideração numa tomada de decisão acertada. No dia-a-dia de uma empresa, em diversos setores, decisões de menores impactos são tomadas sem que um método de análise multicriterial seja adotado para tal escolha. No entanto, quando somadas, essas decisões de menores impactos acabam por influênciar no resultado geral da empresa. O objetivo dessa pesquisa foi desenvolver um método ágil de análise multicriterial que possa ser facilmente adaptado e aplicado em processos operacionais dinâmicos, comuns na rotina de grandes empresas, auxiliando nas tomadas de decisões em níveis mais baixos da organização. O método proposto foi aplicado em dois diferentes processos de uma empresa multinacional do setor de mineração. No primeiro processo foi avaliada a priorização da implantação de projetos de melhoria, onde ideias de empregados são transformadas em projetos que geram ganhos para a empresa. No segundo processo de programação da manutenção, o método auxiliou na priorização da execução de serviços de manutenção quando existem limitações de recursos. O método proposto combina a utilização de ferramentas de diversos métodos já consagrados com a adoção de soluções criativas para atender a dinamicidade dos processos avaliados.
Multicriteria analysis methodologies have been used for decades to analyze substantial capital investment alternatives, and continue to be a reference tool for capital budgeting decision making in organizations. Small investments are also influenced by several variables that need to be taken into account in assertive decision making. Minor impacts decisions are taken without using a method of multicriteria analysis and when all this decisions are combined, they influence the overall result of the company. The objective of this research is to develop a multi-criteria analysis method agile that can be easily adapted and applied to different operation dynamic process, usual in large businesses routine, helping decision making at lower levels of the organization. The proposed method was applied in two different processes of a multinational company in the mining sector. In the first case the process of prioritization projects method was studied, where ideas of employees were transformed into smaller projects that generate profit for the company. In the second case, maintenance scheduling, the method helped to prioritize the execution of maintenance services when there are resource limitation. The proposed method combines tools of renowned methods with creative solutions to help a decision making in dynamics processes.

The complexity of financial analysis, particularly on selection process or decision making problems, has increased rapidly over several decades. As a result, much attention has been focused on developing and implementing the efficient mathematical models for supporting this kind of problems. Multiple criteria decision analysis, an advanced field of operations research provides analysts or decision makers a broad range of methodologies, which are all suited to the complexity of financial decision analysis. In the financial modeling, uncertainty problems are inevitable, owing to the fact that the consequences of events are not precisely known. In addition, human judgments as part of analysis also contribute to it intricacy. Correspondingly, many studies have been concentrated on integrating uncertainty theories in modeling the real financial problems. One area of interest is on the inclusion of the element of human behavior or attitudinal character of decision makers. Aggregation operator in this case can offer a wide spectrum of analysis or flexibility in modeling the human behavior in financial decision analysis. In general, the main purpose of this work is on the study of financial selection problems from the perspective of decision analysis, uncertainty theories and aggregation operators. To be specific, the decision problems under a finite or discrete case and multidimensional factors are studied. The emphasis is given on the group decision making models, notably, the Dempster-Shafer theory (DST) of belief structure, the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS). Moreover, the uncertainty theories based on fuzzy set theory and imprecise probability are employed, together with information fusion based on the ordered weighted average (OWA) operators. Quantitative and qualitative preferences, decision strategies based on the attitudinal character of decision makers, and majority concepts for group consensus are highlighted. The specific contributions of this work are summarized as the following: • The first contribution is on developing the multi-expert multi-criteria decision making (ME-MCDM) model with respect to two-stage aggregation processes. In specific, the aggregation of criteria is based on the integration of weighted arithmetic mean (WA) and OWA. The main attention is given on the proposed alternative OWAWA operator as an extension of immediate WA and OWAWA operators. Two approaches for modeling the majority opinion of experts are studied, in which based on the induced OWA (IOWA) operators. Some modifications to the support functions are suggested as to derive the order inducing variables. The analysis of ME-MCDM model based on these aggregation processes then is conducted. In this study the selection of investment strategy is used as to exemplify the model. • The weighted-selective aggregated majority-OWA operator may be considered as the second contribution. It is as an extension of the SAM-OWA operator, where the reliability of information sources is considered. The WSAM-OWA then is generalized to the quantified WSAM-OWA by incorporating the concept of linguistic quantifier, mainly for the group fusion strategy. The QWSAM-IOWA with an ordering step is proposed for the individual fusion strategy. These aggregation operators are then implemented to the case of alternative scheme of heterogeneous group decision analysis, in particular for a selection of investment problem. • Third contribution is represented by the development of linguistic group decision making with Dempster-Shafer belief structure. Different type of linguistic aggregation operator such as the 2-tuple induced linguistic OWA operator is suggested. Specifically, it is based on order-inducing variables in which the ordering of the arguments and uncertain situations can be assessed with linguistic information. Then, by using the 2-TILOWA in the D-S framework, the belief structure-2-TILOWA operator can be formed. Some of its main properties are studied. This model is applied in a selection of financial strategies. • The extension of AHP for group decision making model is given as the fourth contribution, notably, based on the inclusion of IOWA operators. Two-stage aggregation processes used in the AHP-GDM model are extended. Firstly, a generalization of weighted maximal entropy OWA under the IOWA operator is proposed as to aggregate the criteria. Further, the majority concept based on the IOWA and Minkowski OWA-based similarity measure is suggested to determine a consensus among experts. This model provides a variant of decision strategies for analyzing the individual and the majority of experts. The application in investment selection problem is presented to test the reliability of the model. • The fifth contribution is on the integration of heavy ordered weighted geometric (HOWG) aggregation operators in AHP-GDM model. In the sense of heavy OWA operator (HOWA), the heavy weighted geometric (HWG) and HOWG are introduced as extensions of the normal weighted geometric mean (WG) and the OWG by relaxing the constraints on the associated weighting vector. These HWG and HOWG operators then are utilized in the aggregation process of AHP-GDM, specifically on the aggregation of individual judgments procedure. The main advantage of the model, besides the complete overlapping of information such in classical methods, is that it can also accommodate partial and non-overlapping information in the formulation. An investment selection problem is applied to demonstrate the model. • The extension of TOPSIS for group decision making model by the inclusion of majority concept may be considered as the sixth contribution. The majority concept is derived based on the induced generalized OWA (IGOWA) operators. Two fusion schemes in TOPSIS model are designed. First, an external fusion scheme to aggregate the experts’ judgments with respect to the concept of majority opinion on each criterion is suggested. Then, an internal fusion scheme of ideal and anti-ideal solutions that represents the majority of experts is proposed using the Minkowski OWA distance measures. The comparison of the proposed model with some other TOPSIS models with respect to distance measures is presented. Here, a general case of selection problem is presented, specifically on the human resource selection problem. • Finally, the group decision making model based on conflicting bifuzzy sets (CBFS) is proposed. Precisely, the subjective judgments of experts, mainly from positive and negative aspects are considered simultaneously in the analysis. Moreover, the weighting method for the attribute (or sub-attribute) is subject to the integration of subjective and objective weights. The synthesis of CBFS in the model is naturally done by extending the fuzzy evaluation in parallel with the intuitionistic fuzzy set. A new technique to compute the similarity measure is proposed, in which, being the degree of agreement between the experts. The model then is applied in the case study of flood control project selection problem. To sum up, the presented thesis dealt with the extension of multi-criteria decision analysis models for the financial selection problems (as a specific scope) and also the general selection problems with the inclusion of attitudinal character, majority concept and fuzzy set theory. In particular, the group decision making model, Dempster-Shafer belief structure, AHP and TOPSIS are proposed to overcome the shortcoming of the existing models, i.e., related to the financial decision analysis. The applicability and robustness of the developed models have been demonstrated and some sensitivity analyses are also provided. The main advantages of the proposed models are to provide a more general and flexible models for a wider analysis of the decision problems.
La tesis, a través del análisis y desarrollo del Análisis de decisiones, Teorías de incertidumbre y Operadores de agregación, busca contribuir al estado del arte y nuevas propuesta de las necesidades y demandas que los decisores, responsables o e inversores financieros se encuentran por la creciente complejidad de sus análisis y estrategias, sobre todo en los procesos de selección o en los problemas de decisión. Así, el objetivo principal de esta tesis es el estudio de los problemas de selección financiera desde la perspectiva del análisis de decisiones, las teorías de la incertidumbre y los operadores de agregación. En concreto, se estudian los problemas de decisión en virtud de un conjunto finito de alternativas (caso discreto) y de factores multidimensionales. En el trabajo se desarrolla una extensión de los modelos de análisis de decisiones multicriterio y multiexperto que se utilizan en la resolución de los problemas de selección financiera (como ámbito específico), pero también en los problemas de selección generales, con la inclusión del carácter actitudinal, el concepto de mayoría y la teoría de los conjuntos borrosos. En particular, el énfasis se sitúa en los modelos de toma de decisiones en grupo y en la estructura de creencias Dempster-Shafer (D-S), el proceso analítico jerárquico (AHP) i la técnica de orden de preferencia por similitud con la solución ideal (TOPSIS). Además, se aplican las teorías de incertidumbre basadas en conjuntos borrosos y de probabilidades imprecisas juntamente con la fusión de la información basada en operadores OWA. También se destaca las preferencias cuantitativas y cualitativas, las estrategias de decisión basadas en el carácter actitudinal de los decisores, y el concepto de mayoría en el consenso grupal, de forma que se propone el desarrollo de operadores OWA, la generalización de los modelos AHP y TOPSIS, juntamente con el modelo de toma de decisiones grupal y la estructura de creencias Dempster-Shafer, con el fin de superar las deficiencias de los modelos existentes en relación con el análisis de decisiones financieras. En particular, la investigación realizada se puede sintetizar en siete aportaciones específicas al state-of-the-art del Análisis de decisiones y los operadores de agregación, con aplicaciones en diferentes problemas de decisión financiera: 1. Operadores de agregación basados en los OWA en los modelos de decisión Multiexpertos y Multicriterio. 2. Operadores ponderados SAM-OWA y su aplicación en modelos GDM con operadores lingüísticos. 3. Modelos GDM con operadores lingüísticos adaptados a la teoría de Dempster-Shafer con la aplicación de operadores de agregación inducidos lingüísticos. 4. Generalización del modelo AHP para decisiones grupales usando operadores OWA inducidos. 5. Introducción de operadores OWA geométricos y pesados en los modelos GDM y AHP. 6. Ampliación de los modelos TOPSIS con operadores de agregación basados en los OWA. 7. Desarrollo y aplicación del Conflicting bifuzzy a modelos de decisión MAGDM En la tesis se demuestra la aplicabilidad y la robustez de los modelos desarrollados, tanto con un esquema de agregación de expertos clásicos como con un esquema alternativo que separa por criterios de decisión. Las principales ventajas de los modelos propuestos son que se tratan modelos más generales y flexibles para un análisis más amplio de los problemas de decisión, en particular de los de selección financiera, que incorporen diversos criterios, expertos y componentes de incertidumbre y lingüísticos.

Les problematiques de decision multicritere ont ete l'objet d'un interet croissant au cours des vingt dernieres annees. Plus recemment, les travaux de resolution de tels problemes ont vu le developpement d'algorithmes interactifs s'appuyant sur diverses strategies d'optimisation. Apres avoir etabli quelques resultats theoriques sur les notions d'efficacite et de prudence au sens de arrow-raynaud, nous aborderons dans cette these l'etude d'une methode interactive de prise de decision utile a la resolution de problemes complexes tels que les problematiques de planification spatiale ou d'amenagement. Cette methode est fondee sur l'identification d'une axiomatique efficace et prudente

It can be a hard and time consuming task for a decision maker to decide which parts of a network to reinvest in. There are a lot of parameters to take into consideration regarding reinvestments, for example age, number of outages, number of inspection remarks and the degree of inspection remarks. Without any visualization, it is difficult to detect patterns in the data. Therefor, the decision maker is required to really know the network he/she is working with and to have a gut feeling of where to reinvest. The purpose of this thesis is to show that the decision making process can be much simpler and better supported when using GIS tools for analysis and visualization. This is done by designing a prototype of a web application that can produce multicriteria decision analysis on the parameters of interest for reinvestments in a power network.Traditionally, heavy desktop clients are for expert users while web-based clients are better for layman users. One of the greatest advantages of a web-based client over a desktop client is that it can be reached externally from any device that has access to internet. Because of this, the prototype is developed as a web-based client. Customer data can be sensitive information, this means that the data needs to be secure and directly accessible for the users of the application. Therefor, a 3-tier architecture with client, server and database is used. The result is visualized in a map, which makes it easy for anyone to interpret the result. Since the prototype is developed to be used by none GIS experts, the weighted linear combination method is used for the analysis. The prototype is not fully automated and does not deliver an absolute decision, the goal is rather for it to function as an aid for the decision maker when deciding on the final reinvestment area. The prototype is evaluated by the prospective users of the application through a questionnaire and the results show that a tool like this would be very useful for reinvestments decisions. Since the prototype does not rely on topology or network structure, it can be adapted to other spatial decision problems than just reinvestments in power networks.

Abstract We envision a new class of methods, constituting what we call a cooperative design methodology, to support group decision making in design practice. As a first step, we model the cooperative design process as a multicriteria, multilevel decision problem and focus on understanding the relationships between design structure and decision strategies. In this paper, we present methods to characterize sequential decision strategies. We first introduce the notion of Quality Loss to quantify the loss of design freedom in the downstream stages of a sequential scheme. Towards identifying the sequence with the lowest quality loss (the “optimal sequence”), which is of practical significance in the design of time-critical products, we partition design variables into exclusive groups based on their connectivities. Next, the sensitivities and connectivities are combined to characterize invariant design decisions. Sufficient conditions, relating exclusive group structure to invariant decisions, are presented to reduce the complexity of identifying the optimal sequence. These ideas are illustrated using a dc motor design example.

A procedure based on fuzzy multicriteria decision making is developed for evaluation and ranking of tribomaterials. Tribomaterial properties which influence performance of triboelements at operational stage are considered as criteria for evaluation of tribomaterial. Tribomaterial properties, weighting factors and desired values are converted into fuzzy membership functions called trapezoidal fuzzy numbers. Tribomaterial ranking index is evaluated from weighted fuzzy tribomaterial suitability value for various degrees of optimism through a series of fuzzy arithmetic operations. Higher the value of index better is ranking of tribomaterial. A step by step procedure for evaluation and ranking of tribomaterial is suggested. An example of plain bearing has been presented for illustration of the procedure.

The world merchant fleet has increased in the last decade producing an increase of fuel consumption and greenhouse gas emissions (GHGs). Thus, the concerns of ship-owners to implement alternatives to improve the fleet efficiency are growing. However, shipowners are facing barriers to implement energy efficiency technologies mainly due to reliability, financial and economic constraints as well as complexity of change. Actually several shipowners are using onboard data measurements systems that collect navigation and propulsion information of their ships. Therefore, after being sent via satellite and stored in data warehouse, these data are being made available to assess the performance of their fleets. This paper describes the use of these data to generate models in order to answer to the following questions: What is the ship with least efficiency in my fleet? What is the best strategy to improve the overall efficiency of my fleet? What is the ship that I should sell in priority? What is the influence of this maintenance policy on the performance of my fleet? The application case of this paper is based on one fleet of 13 ships containing 223 trips that gather approximately 6,844 traveling days. After the definition of the key performance indicators (KPIs), a data envelopment analysis (DEA) models is discussed. Then, a multicriterion decision analysis (MCDA) model is compared to the DEA outputs. The results suggest that this new methodology can efficiently provide a multicriteria decision framework to shipowners avoiding engineers’ subjectivity. These findings offer a new way to address efficiency and performance in ship management.

This article considers that the most cost-effective way to transport products of fossil fuels, liquids or gases, is by a pipeline system which provides high operational reliability, lower power consumption and less environmental impact consequently. It becomes more and more necessary implementing new pipeline projects in Brazil. In the early seventies, discussions have started regarding the pressure for reduction of costs and pursuit of sustainable development, linked to the Triple Bottom Line, which uses economic, environmental and social dimensions. So far, the decisions in several sectors of the oil industry have been taken based on just one criteria, usually economical. This study proposes the establishment of a multicriteria analysis to be used in future pipeline deployments in Brazil, aiming to support the decision makers by supplying them quantifiable and unquantifiable criteria. For the development of this analysis, it will be used the Analytic Hierarchy Process (AHP) in which the various relevant criteria are represented through relative weigh. A project of a new pipeline will be used as a case study to demonstrate the application of the proposed model. The organizations can use the result of the analysis as an input to decision-making on future ductwork ventures in Brazil, especially in terms of environmental and social dimensions.