970 research outputs found

    Research of the Classification Model Based on Dominance Rough Set Approach for China Emergency Communication

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    Ensuring smooth communication and recovering damaged communication system quickly and efficiently are the key to the entire emergency response, command, control, and rescue during the whole accident. The classification of emergency communication level is the premise of emergency communication guarantee. So, we use dominance rough set approach (DRSA) to construct the classification model for the judgment of emergency communication in this paper. In this model, we propose a classification index system of emergency communication using the method of expert interview firstly and then use DRSA to complete data sample, reduct attribute, and extract the preference decision rules of the emergency communication classification. Finally, the recognition accuracy of this model is verified; the testing result proves the model proposed in this paper is valid

    The treatment of uncertainty in multicriteria decision making

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    Bibliography: leaves 142-149.The nature of human decision making dictates that a decision must often be considered under conditions of uncertainty. Decisions may be influenced by uncertain future events, doubts regarding the precision of inputs, doubts as to what the decision maker considers important, and many other forms of uncertainty. The multicriteria decision models that are designed to facilitate and aid decision making must therefore consider these uncertainties if they are to be effective. In this thesis, we consider the treatment of uncertainty in multicriteria decision making (MCDM), with a specific view to investigating the types of uncertainty that are most relevant to MCDM, [and] how the uncertainties identified as relevant may be treated by various different MCDM methodologies

    STOCHASTIC MULTI-ATTRIBUTE UTILITY MODEL

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    In real situations, the attribute value (mostly variable)can be best represented by introducing the finite numberof attribute values level, to which the correspondingprobabilities should also be attached. Stochastic Multi-Attribute Utility Model has the ability to analyze suchstochastic multi-attribute problems. The choice of one,from the set of available options, is made by choosingthe best option based on the maximum expected utilitystructure. In this paper, we will mention some argumentsfor the development of the Stochastic Multi-AttributeUtility Model, its advantages (they are closer to reality),disadvantages (analytically difficult technique, subjectiveassessments of the values of variable attributes), aswell as the process of solving the problem

    Methodological review of multicriteria optimization techniques: aplications in water resources

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    Multi-criteria decision analysis (MCDA) is an umbrella approach that has been applied to a wide range of natural resource management situations. This report has two purposes. First, it aims to provide an overview of advancedmulticriteriaapproaches, methods and tools. The review seeks to layout the nature of the models, their inherent strengths and limitations. Analysis of their applicability in supporting real-life decision-making processes is provided with relation to requirements imposed by organizationally decentralized and economically specific spatial and temporal frameworks. Models are categorized based on different classification schemes and are reviewed by describing their general characteristics, approaches, and fundamental properties. A necessity of careful structuring of decision problems is discussed regarding planning, staging and control aspects within broader agricultural context, and in water management in particular. A special emphasis is given to the importance of manipulating decision elements by means ofhierarchingand clustering. The review goes beyond traditionalMCDAtechniques; it describes new modelling approaches. The second purpose is to describe newMCDAparadigms aimed at addressing the inherent complexity of managing water ecosystems, particularly with respect to multiple criteria integrated with biophysical models,multistakeholders, and lack of information. Comments about, and critical analysis of, the limitations of traditional models are made to point out the need for, and propose a call to, a new way of thinking aboutMCDAas they are applied to water and natural resources management planning. These new perspectives do not undermine the value of traditional methods; rather they point to a shift in emphasis from methods for problem solving to methods for problem structuring. Literature review show successfully integrations of watershed management optimization models to efficiently screen a broad range of technical, economic, and policy management options within a watershed system framework and select the optimal combination of management strategies and associated water allocations for designing a sustainable watershed management plan at least cost. Papers show applications in watershed management model that integrates both natural and human elements of a watershed system including the management of ground and surface water sources, water treatment and distribution systems, human demands,wastewatertreatment and collection systems, water reuse facilities,nonpotablewater distribution infrastructure, aquifer storage and recharge facilities, storm water, and land use

    Uncertain Multi-Criteria Optimization Problems

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    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

    Interactive procedure for multiobjective dynamic programming with the mixed ordered structure

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    The paper presents a multiobjective dynamic programming problem with the values of the criteria function in ordered structures. The first problem is a model with deterministic values; the second, one with triangular fuzzy numbers; and the third, one with discrete random variables with the k-th absolute moment finite. The fourth model is a product of the three models listed above. The aim of the paper is to present an interactive procedure which uses trade-offs and which allows to determine the final solution in the mixed ordered structure. The ordered structures and the proposed procedure are illustrated by numerical examples

    The Application of Dominance-based Rough Sets Theory to Evaluation of Transportation Systems

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    AbstractThe paper presents an original procedure of evaluation of a transportation system, resulting in its assignment into a predefined class, representing the overall standard of the considered system and the level of transportation service. The method relies on the application of the dominance-based rough set theory (DRST), allows for thorough data exploration, evaluation of informational content of the considered characteristics and generation of certain decision rules that support t he evaluation process. In the analysis different characteristics (criteria and attributes) describing various aspects of a transportation system operations are taken into account. The assignment of a transportation system to a specific quality class is performed based on the values of characteristics which are compared with the evaluation pattern, i.e. the set of decision rules generated through the analysis of customers’ opinions and expectations concerning a transportation system. The method is composed of three major steps, including: 1) identification of the most important characteristics, 2) generation of the evaluation pattern, and 3) assignment of the transportation system to the appropriate class. In the evaluation process five key components of a transportation system, including: transportation means, human resources, informational resources, transportation infrastructure and technical equipment as well as organizational rules are considered

    Simplified models for multi-criteria decision analysis under uncertainty

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    Includes abstract.Includes bibliographical references.When facilitating decisions in which some performance evaluations are uncertain, a decision must be taken about how this uncertainty is to be modelled. This involves, in part, choosing an uncertainty format {a way of representing the possible outcomes that may occur. It seems reasonable to suggest {and is an aim of the thesis to show {that the choice of how uncertain quantities are represented will exert some influence over the decision-making process and the final decision taken. Many models exist for multi-criteria decision analysis (MCDA) under conditions of uncertainty; perhaps the most well-known are those based on multi-attribute utility theory [MAUT, e.g. 147], which uses probability distributions to represent uncertainty. The great strength of MAUT is its axiomatic foundation, but even in its simplest form its practical implementation is formidable, and although there are several practical applications of MAUT reported in the literature [e.g. 39, 270] the number is small relative to its theoretical standing. Practical applications often use simpler decision models to aid decision making under uncertainty, based on uncertainty formats that `simplify' the full probability distributions (e.g. using expected values, variances, quantiles, etc). The aim of this thesis is to identify decision models associated with these `simplified' uncertainty formats and to evaluate the potential usefulness of these models as decision aids for problems involving uncertainty. It is hoped that doing so provides some guidance to practitioners about the types of models that may be used for uncertain decision making. The performance of simplified models is evaluated using three distinct methodological approaches {computer simulation, `laboratory' choice experiments, and real-world applications of decision analysis {in the hope of providing an integrated assessment. Chapter 3 generates a number of hypothetical decision problems by simulation, and within each problem simulates the hypothetical application of MAUT and various simplified decision models. The findings allow one to assess how the simplification of MAUT models might impact results, but do not provide any general conclusions because they are based on hypothetical decision problems and cannot evaluate practical issues like ease-of-use or the ability to generate insight that are critical to good decision aid. Chapter 4 addresses some of these limitations by reporting an experimental study consisting of choice tasks presented to numerate but unfacilitated participants. Tasks involved subjects selecting one from a set of five alternatives with uncertain attribute evaluations, with the format used to represent uncertainty and the number of objectives for the choice varied as part of the experimental design. The study is limited by the focus on descriptive rather than real prescriptive decision making, but has implications for prescriptive decision making practice in that natural tendencies are identified which may need to be overcome in the course of a prescriptive analysis

    An Approach to Evaluate a Supply Chain Network

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    Designing the supply chain network (SCN) is the first step to creating a chain sourcing for results. The process identifies the change that will differentiate an organization from its competitors, to contact a customer with a successful value proposition, reduce costs and boost profitability. The most effective way to ensure perfect fluidity is to appoint an employee responsible for supervising the entire process. The manager will inform and coordinate the activities of the heads of different departments, from shipping to sales, focusing on communication and identification of potential problems, as well as correcting faults before they lead to disruption. This paper proposes an evaluation approach for the supply chain network design problems under uncertainty. Existing approaches to this problem are either the deterministic environments or can only address a modest number of scenarios for the uncertain problem parameters. Our solution approach integrates both features; the collective evaluation and the selection of one
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