Multi-Criteria

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Participatory flood vulnerability assessment: a multi criteria approach

Participatory flood vulnerability assessment: a multi criteria approach

multiple criteria to help individuals explore decisions (Bel- ton and Stewart, 2002). The aim of MCDM is not to find a final and optimal solution (Kowalski et al., 2009; Roy, 1985), but to deliver a set of alternatives to better inform decision makers by making subjective judgements explicit in a transparent way. Participatory MCDM refers to a pro- cess in which a multi-criteria tool is used within participa- tory settings, where a group of key experts and stakeholders is actively involved (Paneque Salgado et al., 2009). Partici- patory MCDM provides a promising and structured frame- work for integrating interdisciplinary knowledge in an effort to bring credibility to vulnerability indicators, participant sat- isfaction, and some degree of mutual learning (Sheppard and Meitner, 2005). It can improve the transparency and analytic rigour of flood vulnerability assessment since the choices of input criteria, data standardization, weighting, and aggrega- tion are explicitly expressed, leading to justifiable decisions and reproducible results.
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Multi criteria decision making methods for location selection of distribution centers   Pages 491-504
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Multi criteria decision making methods for location selection of distribution centers Pages 491-504 Download PDF

The domain of FLP has been deeply explored by a number of researchers (Tompkins, & Reed, 1976; Levary, & Kalchik, 1985; Kusiak & Heragu, 1987; Das, 1993; Meller & Gau, 1996; Russell et al., 1999; Lin & Sharp, 1999; Drira et al., 2007). Many researchers from the past have already applied various techniques to solve facility location selection problems. But most of those techniques were based on complex mathematical formulations, without considering the qualitative information regarding criteria values. Randhawa and West (1995) proposed a solution approach to facility location selection problems by integrating analytical and multi-criteria decision-making models.
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On Rough Interval Multi Criteria Decision Making

On Rough Interval Multi Criteria Decision Making

[5] combined with variable precision rough set and AHP for group decision-making. Guo and Zhang [6] integrated VIKOR and rough set theory in supplier selection. Li et al. [7] proposed an integrated model by combining with Kano's model, rough set and AHP to rank customer requirements importance. Aydogan [8] presented a hybrid rough-AHP and TOPSIS methods. Wang et al. [9] applied interval rough numbers in dealing with uncertainty when determining weight coefficients of decision attributes. Roy et al. [10] presented a hybrid rough-AHP and MABAC methods. This paper presents a novel three different Multi-Criteria Decision Making (MCDM) methods with rough interval; Rough Interval Operational Competitiveness Rating Analysis (RIOCRA), Rough Interval Multi Objective and Optimization on the basis of Ratio Analysis (RIMOORA) and Rough Interval Additive Ratio Assessment (RIARAS). The three methods are used to choose the best alternative among the various alternatives under the rough interval condition. The Subjective and Objective Weight Integrated Approach (SOWIA) method was presented to determine the weight of each evaluation criterion. Because of disagreement in the ranks obtained by the three different MCDM methods; a final ranking method based on REGIME method is given.
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Multi-criteria decision model for retrofitting existing buildings

Multi-criteria decision model for retrofitting existing buildings

Abstract. Decision is an element in the risk management process. In this paper the way how science can help in de- cision making and implementation for retrofitting buildings in earthquake prone urban areas is investigated. In such in- terventions actors from various spheres are involved. Their interests range among minimising the intervention for maxi- mal preservation or increasing it for seismic safety. Research was conducted to see how to facilitate collaboration between these actors. A particular attention was given to the role of time in actors’ preferences. For this reason, on decision level, both the processural and the personal dimension of risk man- agement, the later seen as a task, were considered. A sys- tematic approach was employed to determine the functional structure of a participative decision model. Three layers on which actors implied in this multi-criteria decision problem interact were identified: town, building and element. So- called ‘retrofit elements’ are characteristic bearers in the ar- chitectural survey, engineering simulations, costs estimation and define the realms perceived by the inhabitants. This way they represent an interaction basis for the interest groups con- sidered in a deeper study. Such orientation means for actors’ interaction were designed on other levels of intervention as well. Finally, an ‘experiment’ for the implementation of the decision model is presented: a strategic plan for an urban intervention towards reduction of earthquake hazard impact through retrofitting. A systematic approach proves thus to be a very good communication basis among the participants in the seismic risk management process. Nevertheless, it can only be applied in later phases (decision, implementation, control) only, since it serves verifying and improving solu- tion and not developing the concept. The ‘retrofit elements’ are a typical example of the detailing degree reached in the retrofit design plans in these phases.
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Hierarchical multi-criteria analysis for the selection of a triathlon

Hierarchical multi-criteria analysis for the selection of a triathlon

For triathletes it is essential to have a tool that allows them to efficiently select a triathlon event. The chosen tool was the method called Analytic Hierarchical Process (AHP), originally developed by Saaty (1978) which enables the analysis of complex problems, facilitating decision making under mathematical multi-criteria. It is a system that adds our experiences captured through science (Jiménez, 2002) and that consists of hierarchical ordering in levels weighted by importance (Saaty, 2004).

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MULTI CRITERIA DECISION MAKING MODEL

MULTI CRITERIA DECISION MAKING MODEL

In this paper, we explain the value of both Trapezoidal and Triangular Fuzzy Numbers and develop a new ranking method based on the value of fuzzy number which in turn will be very helpful in decision making situations. Then we propose a Multi Criteria Decision Making (MCDM) Model based on the proposed ranking method. Arithmetic mean operation of fuzzy numbers is used for aggregating experts’ judgments.

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Multi Criteria Framework for Surface Water Quality Management

Multi Criteria Framework for Surface Water Quality Management

Multi Criteria Analysis framework was developed based on four main criteria: technical, Environmental, Economic, Social and Community. Figure 14 shows the MCA framework criteria and indicators. These criteria were used for ranking the proposed water quality management scenario then find the most convenient water quality management scenario. MCA scoring system is formed based on the procedure developed by the USEPA (Okbah et al ., 2006) as following:

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Social vulnerability assessment using spatial multi-criteria analysis(SEVI model) and the Social Vulnerability Index (SoVI model) – a case study for Bucharest, Romania

Social vulnerability assessment using spatial multi-criteria analysis(SEVI model) and the Social Vulnerability Index (SoVI model) – a case study for Bucharest, Romania

Abstract. In recent decades, the development of vulnerabil- ity frameworks has enlarged the research in the natural haz- ards field. Despite progress in developing the vulnerability studies, there is more to investigate regarding the quantita- tive approach and clarification of the conceptual explanation of the social component. At the same time, some disaster- prone areas register limited attention. Among these, Roma- nia’s capital city, Bucharest, is the most earthquake-prone capital in Europe and the tenth in the world. The location is used to assess two multi-criteria methods for aggregat- ing complex indicators: the social vulnerability index (SoVI model) and the spatial multi-criteria social vulnerability in- dex (SEVI model). Using the data of the 2002 census we reduce the indicators through a factor analytical approach to create the indices and examine if they bear any resemblance to the known vulnerability of Bucharest city through an ex- ploratory spatial data analysis (ESDA). This is a critical issue that may provide better understanding of the social vulnera- bility in the city and appropriate information for authorities and stakeholders to consider in their decision making. The study emphasizes that social vulnerability is an urban pro- cess that increased in a post-communist Bucharest, raising the concern that the population at risk lacks the capacity to cope with disasters. The assessment of the indices indicates a significant and similar clustering pattern of the census ad- ministrative units, with an overlap between the clustering ar- eas affected by high social vulnerability. Our proposed SEVI model suggests adjustment sensitivity, useful in the expert- opinion accuracy.
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Multi-criteria Identification of VibroBot Dynamic Characteristics

Multi-criteria Identification of VibroBot Dynamic Characteristics

The solution of the task (10) is based on the principle of coherent optimality of V. Pareto as suggested in [11]. The optimal solution (u*, f*) consists of two sets of Pareto – multitudes D*= {u*: u* = arg Pmin uD f (u)} and P*= {f*: f* = f (u*)} with un-improving points u*D* and f*P*. The choice of a compromised solution can be facilitated significantly if it is implemented by reasonably reduced subsets of D* and P*. The task (10) is solved with the MATLAB program “psims” for multi-criteria parametric optimization by means of -selection procedure, documented in [14]. Optimization is carried out in two stages.
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Evaluation of landfill gas plant siting problem: a multi-criteria approach

Evaluation of landfill gas plant siting problem: a multi-criteria approach

In order to prevent the release of wastes into streams, groundwaters, rivers, drinking water sources and other major water bodies, safe distance of the site from these waters bodies would be a goal to be achieved. Economic implications such as the cost of access to landfill which generates the gas to be processed in the LFG plant, cost of a required facility, cost of manpower, and cost of transport are also calculated for sustainability. Other criteria include geology, the location of residential settlements, the location of industrial areas, and climate characteristics. The geology, among other criteria, is related to the soil types, parent materials, capacities, and migration of leachate. Some environmental regulations prohibit the location of LFG plants close to residential and industrial areas mainly because of the nature of the major sources of raw materials for the plant, the effluents from the plant, and hazards characterizing the operation of the plant. Inappropriate landfill and LFG generation site is more likely to have negative environmental, economic, and ecological impacts on the host community. The selection of a site for landfill waste disposal and subsequent landfill LFG production plant using conventional methods usually involves combining several data layers, and is time-consuming and error-prone. This awareness has led to the development of properly designed environmental information systems to facilitate the flow of environmental information from data sources to stakeholders and also to transform organization and management of spatial data for the enhanced environmental decision-making process. Combining well-developed environmental information systems with various multi-criteria decision-making methods has led to significant advances in decision making on the landfill site. As nearness to the sources of raw materials is crucial for siting of any plant, significant advances in decision making on the selection of landfill site may be expected extend to LFG plant location. Meeting environmental risk management objectives having its basis from environmental regulations, has been the focus of most decision-making support tool for landfill sites (21). Chabuk et al (22) identified two suitable sites which could accommodate solid waste from 2020 to 2030 in Al-Hashimiyah Qadhaa, Babylon, and Iraq using
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A Survey on Multi Criteria Decision Making Methods in Software Engineering

A Survey on Multi Criteria Decision Making Methods in Software Engineering

The decision support methods have been implemented in the various applications satisfying the constraints to the major extent. These methods came into existence in early 1960 and the work continued with the different application. The complexity in decision making, increased with the number of alternatives and the stakeholder involvement resulting in the implementation of MCDM. Depending on the functional requirement different techniques can be used for the attainment of the solution using either linear programming or non-linear programming or discrete optimization technique. Abbas Mardani et.al [4] published study on the MCDM techniques and their applications in Energy, environmental and sustainability, Operation research and soft computing, Knowledge management etc. Vaidya, O. S. & Kumar, S [5] shows survey on AHP used in Energy management, E-commerce, Government sectors etc. Achimugu P.et.al [6] gives details on a literature review of Software Requirements Prioritization. Vicent Penades-Pla et.al [7] work details about a review of Multi-Criteria Decision-Making Methods Applied to the Sustainable Bridge Design. This study mainly highlights MCDM application in different areas of software engineering from 2001 to 2018. The sources referred are IEEE, Science Direct, Research Gate, Conferences and Journals. Some of the applications are tabulated in Table I. and are discussed as follows.
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Multi Criteria Decision Making and its Applications

Multi Criteria Decision Making and its Applications

Multi criteria decision making becomes an important operational research part, which supports the decision makers with structuring and resolving decision making problems. Multi criteria decision making (MCDM) is an approach of selecting the perfect alternative among all the possible alternatives on the basis of different decision criteria (Işıklar et al. 2007). MCDM process works on the common principle which are

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Data Mining For Multi-Criteria Energy Predictions

Data Mining For Multi-Criteria Energy Predictions

Abstract—We present a data mining technique for multi-criteria predictions of wind energy. A multi-criteria (MC) evolutionary computing method has been applied for the optimization of an Artificial Intelligence learning methodology “Support Vector Machines” (SVM). The multi-criteria SVM method is applied and tested on a dataset within North America, for predictions of wind energy using climate variables. The SVM training employs Swarm Intelligence method for multi-criteria optimization. The National Center for Environmental Prediction (NCEP)’s global reanalysis gridded dataset has been employed in this study. The gridded dataset for this particular application consists of 4- points each consisting of five variables. In order to study the impact of higher dimensions on the performance of SVM, Principal Component Analysis (PCA) is applied on the input data to reduce the dimensionality of the data. The results of multi-criteria SVM for the prediction of wind energy are reported with and without the pre-processing using PCA.
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Multi Criteria Decision Analysis for Usage Based Optimization of Powertrains

Multi Criteria Decision Analysis for Usage Based Optimization of Powertrains

The assessment procedure for the multi-criteria decision analysis used for the search for the optimal powertrain for a usage profile is based on the Analytic Hierarchy Process by Saaty [1]. The AHP structures the issue under consideration and permits the rating on the basis of measureable factors through a mathematical procedure [11]. With a consistent use of objective criteria subjective ratings become dispensable with the effect that an objective decision making process can be obtained. This qualifies the AHP in relation to other decision making techniques which rely on subjective assessments by the decision-maker [12]. The aim by using AHP is an objective ranking of the different powertrains. Therefore, characteristic values are extracted from the usage profile and the results of the optimization process respectively the results from the determination of the driving performance are used. The assessment structure forms an objective decision making process on the basis of usage based data.
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Multi Criteria Decision Making Using ELECTRE

Multi Criteria Decision Making Using ELECTRE

Requirements prioritization is one of the key factors in deciding the success of the project and hence the software industry. One of the major concerns in software prioritization techniques is that the existing ranking techniques have a very modest support to different criteria used by stakeholders to present their ranking. The current techniques are not suitable for arriving at an optimized view of multiple stakeholders using multiple criteria. This research analyzes the issues in existing techniques. A web based decision support model using ELECTRE as the method for pri- oritization is proposed. ELECTRE is a multi-criteria decision making model that is proved to be ef- fective in ranking several decision making problems. The proposed system takes input from mul- tiple stakeholders using 100-point method. An optimized ranking is obtained using ELECTRE me- thod. The developed system is validated using a pilot project and is found to be efficient in terms of saving cost of implementation and man-hours needed for implementation.
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On Rough Interval Multi-Criteria Group Decision Making

On Rough Interval Multi-Criteria Group Decision Making

When MCDM is done by more than one decision maker, it is called multi-criteria group decision making (MCGDM), each decision maker considers the same sets of alternatives and criteria [1]. A group decision situation involves multiple decision makers, each with different skills, experience and knowledge relating to different aspects (criteria) of the problem [2]. In brief, MCDM is the art of compromising conflicting criteria for a single decision maker. The group decision making is the art of compromising different opinions of group members, in a sense, group decision making can be a multi-dimensional MCDM problem, but more issues should be studied to treat the conflicting factors aforementioned [3]. Group decision making can be approached from two points of view. In the first approach, individual multi-criteria models are developed based on individual’s preferences. Each decision maker formulates a multi-criteria problem defining the parameters according to these preferences and solves the problem getting an individual solution set. Next, the separate solutions are aggregated by aggregation of operations resulting in the group solution. In the second approach, each decision maker provides a set of parameters which are aggregated by appropriate operators, providing finally a set of group parameters. Upon this set the multi-criteria method is applied and the solution expresses group preference [4].There are few publications in literature used applications of MCDM methods based on rough numbers. Song et. al., [5] used a rough TOPSIS approach for failure mode and effects analysis in uncertain environments. Pamucar and Cirovie [6] Combination of rough AHP and MABAC . Roy et. al., [7] presented a hybrid rough-AHP and MABAC methods for selection of medical tourism sites. Gigovie et. al., [8] Combination of interval rough AHP and GIS is proposed for flood hazard mapping. Zaher et. al. [9] introduced three types of multi-criteria decision making methods based on the rough interval concept.
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Paradox of Multi Criteria Decision Making Processes

Paradox of Multi Criteria Decision Making Processes

Several studies examine the issue of MCDM (Multi Criteria Decision Making). Kabir et al. [10] identified and classified 300 researches in the field of infrastructure management, which are related to the field of MCDM. Monterio Gomes et al. [18] present a model for analysis of the behavioral aspects of MCDM. They also analyze the implications of using the TODIM method. Othman et al. [19] deals with the use of Geographical Information System (GIS) and Multi-criteria Decision Making. Durban and Stewart [9] present methods of MCDA (Multi Criteria Decision Analysis), where attribute evaluations are uncertain. Based on the Prospect Theory, Meng et al. [16] develop a method of the multi-attribute decision making using Atanassov's interval-valued intuitionistic fuzzy information. Rezaei [20] uses the Best Worst Method (BWM) and calculate a weight of each criterion in order to rank criteria from the most important criterion to the least important one, which are set up beforehand.
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LOCALISATION OF WASTE THERMAL TREATMENT PLANT
WITH MULTI-CRITERIA ANALYSYS

LOCALISATION OF WASTE THERMAL TREATMENT PLANT WITH MULTI-CRITERIA ANALYSYS

When solving a multi-criteria problem, we often strive to express our expectations with the help of just one criterion, which aggregates all significant consequences of the problem. Thus, we deal with a single-criterion analysis, in which every potential variant is assessed relative to just one criterion, selected a priori, for example costs, outputs, time needed to execute the project, profit, profitability, benefits. A single criterion, howev- er, is not recommended in a case of complicated investment processes. It is neither fully reliable nor does it possess such properties that would al- low us to demonstrate and analyse a whole spec- trum of quetsions and problems associated with planning, designing and building constructions. Multi-criteria decision making, in contrast to an
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Multi-Objective and Multi-Criteria Analysis for Optimal Pump Scheduling in Water Systems

Multi-Objective and Multi-Criteria Analysis for Optimal Pump Scheduling in Water Systems

This contribution focuses on the problem of optimal pump scheduling, a fundamental element in pursuing operation optimization of water distribution systems. A combined approach of multi-objective optimization and multi-criteria analysis is herein suggested to first find the Pareto front of non-dominated solutions and then to rank them based on a set of weighted criteria. The Non-Dominated Sorting Genetic Algorithm (NSGA-II) is proposed to solve the multi-objective problem, while the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to achieve the final ranking.
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Interconnectivity Multi Criteria For Sustainable Development Of Beef Cattle

Interconnectivity Multi Criteria For Sustainable Development Of Beef Cattle

Abstract :This research is aimed to analyze the multi criteria stakeholders in the development of sustainable beef cattle by using Participatory Rural Appraisal (PRA). There were 35 samples were observed. Data and information were collected through focus group discussion amon g stakeholders. By using a multi-criteria sustainability which was promoted by the Indonesian National Council on Climate Change as guidance, stakeholders were participating to pay close attention deeply, until they found criterion and subcriterion level of their choices about sustain able beef cattle development. The analysis of criterion-subcriterion level were conducted by using Analytical Hierarchy Process (AHP). The results indicate that Economic criteria (E) becomes the primary priority, followed by other criteria. While subcriterion and interconnectivity analysis reveals that the priority of sustainable beef cattle development are interconnection of addition of investment on govemment budget (E2), establishment of cattle farmer com munity (S3), ecology functions are preserved (L1) with criteria and subcriteria, leaving out experimental technology and outdated technology (T2). Although the development of sustainable beef cattle has complexity (multi stakeholder and multi criteria), should be facilitated in order to contributing each other towards one mission and the same purpose namely, development of sustainable beef cattle.
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