Improved Multiple Attribute Decision Making Methods

Rao, R. Venkata

doi:10.1007/978-1-4471-4375-8_2

Cited by 19 publications

(7 citation statements)

References 39 publications

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“…Generally, in a decision‐making scenario, there may be disagreement between the stakeholders as to the relative importance to criteria and preference of an alternative with respect to the selected criteria. In such circumstances, MCDA is useful to examine many scenarios and provide simple tools for comparison . Furthermore, outranking methods are applicable, under certain conditions, e.g.…”

Section: Discussionmentioning

confidence: 90%

Multi‐criteria assessment of yellow, green, and woody biomasses: pre‐screening of potential biomasses as feedstocks for biorefineries

Parajuli

Knudsen

Dalgaard

2015

Biofuels Bioprod Bioref

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The need for sustainable production of food, feed, and fuel for a growing global population with minimum pressure on land use and other ecological impacts is a major challenge. The aim of the current study is to pre-screen available biomass types to prepare for a system-wide sustainability assessment in related biorefi nery value chains. The study describes the use of multiple-criteria decision-making techniques as tools for assessing criteria and to draw preferences among the alternatives. Thirteen biomass types (classifi ed as yellow, green, and woody) are evaluated with respect to 15 criteria. The criteria are classifi ed under the three assessment parameters: supply potential, biomass properties, and potential environmental gain/losses. Pair-wise comparisons of the alternatives are carried out in an outranking method (PROMETHEE) on the basis of their ratings (on a 1-5 scale) and weights assigned to the criteria. Weighting factors of the criteria are calculated by using the Analytical Hierarchy Process (AHP). The study shows that the net outranking fl ow for the most preferred biomass types are: grass-clover 2.8, wheat-straw 1.2 and willow 1, representing the biomass categories green, yellow and woody, respectively. The overall order of the preferred biomass types is grass-clover, pure grass, alfalfa from the green category; straw based on wheat and barley from the yellow category; and willow from the woody category. Furthermore, irrespective of whether or not the raw materials are processed in integrated biorefi neries, preferences on biomass types are also important in the context of their sustainable conversions in related biorefi nery platforms (e.g. straw in a sugar-based lignocellulosic biorefi nery and grass-clover in a green biorefi nery).

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Section: Discussionmentioning

confidence: 90%

Multi‐criteria assessment of yellow, green, and woody biomasses: pre‐screening of potential biomasses as feedstocks for biorefineries

Parajuli

Knudsen

Dalgaard

2015

Biofuels Bioprod Bioref

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“…Additionally, it integrates the user's preference through getting the relative importance of the attributes (criteria) according to the user perception [1]. Accordingly, the quality attributes are represented as a hierarchal relationship, that matches the decision makers form of thinking [34]. Our recommended cloud service selection hierarchy is shown in Figure 5.…”

Section: Cloud Service Selection Problem Formulationmentioning

confidence: 99%

“…An attribute can be compared to itself and the related importance is set to 1. Therefore, the matrix diagonals are all set to 1 [34]. The importance level is within the range between 1 to 9, where 1 refers to the lowest importance attribute and 9 refers to the most important attribute having the highest value.…”

Section: Step2: Attributes Weights and Rankingmentioning

confidence: 99%

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Towards an Efficient Federated Cloud Service Selection to Support Workflow Big Data Requirements

Serhani¹,

Kassabi²,

Taleb³

2018

Adv. sci. technol. eng. syst. j.

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Cloud Computing is considered nowadays an attractive solution to serve the Big Data storage, processing, and analytics needs. Given the high complexity of Big Data workflows and their contingent requirements, a single cloud provider might not be able alone to satisfy these needs. A multitude of cloud providers that offer myriad of cloud services and resources can be selected. However, such selection is not straightforward since it has to deal with the scaling of Big Data requirements, and the dynamic cloud resources fluctuation. This work proposes a novel cloud service selection approach which evaluates Big Data requirements, matches them in real time to most suitable cloud services, after which suggests the best matching services satisfying various Big Data processing requests. Our proposed selection scheme is performed throughout three phases: 1) capture Big Data workflow requirements using a Big Data task profile and map these to a set of QoS attributes, and prioritize cloud service providers (CSPs) that best fulfil these requirements, 2) rely on the pool of selected providers by phase 1 to then choose the suitable cloud services from a single provider to satisfy the Big Data task requirements, and 3) implement multiple providers selection to better satisfy requirements of Big Data workflow composed of multiples tasks. To cope with the multi-criteria selection problem, we extended the Analytic Hierarchy Process (AHP) to better provide more accurate rankings. We develop a set of experimental scenarios to evaluate our 3-phase selection schemes while verifying key properties such as scalability and selection accuracy. We also compared our selection approach to well-known selection schemes in the literature. The obtained results demonstrate that our approach perform very well compared to the other approaches and efficiently select the most suitable cloud services that guarantee Big Data tasks and workflow QoS requirements.

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“…Higher the composite performance score, higher the rank of the alternative. Different steps associated with AHP algorithm have been explained [7].…”

Section: Analytic Hierarchy Process (Ahp)mentioning

confidence: 99%

Comparison of Customer Preference for Bulk Material Handling Equipment through Fuzzy-AHP Approach

Sen

Ghosh

Sarkar

2016

J. Inst. Eng. India Ser. C

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In the present study, customer's perception has played one of the important roles for selection of the exact equipment out of available alternatives. The present study is dealt with the method of optimization of selection criteria of a material handling equipment, based on the technical specifications considered to be available at the user end. In this work, the needs of customers have been identified and prioritized, that lead to the selection of number of criteria, which have direct effect upon the performance of the equipment. To check the consistency of selection criteria, first of all an AHP based methodology is adopted with the identified criteria and available product categories, based upon which, the judgments of the users are defined to derive the priority scales. Such judgments expressed the relative strength or intensity of the impact of the elements of the hierarchy. Subsequently, all the alternatives have ranked for each identified criteria with subsequent constitution of weighted matrices. The same has been compared with the normalized values of approximate selling prices of the equipments to determine individual cost-benefit ratio. Based on the cost-benefit ratio, the equipment is ranked. With same conditions, the study is obtained again with a Fuzzy AHP concept, where a fuzzy linguistic approach has reduced the amount of uncertainty in decision making, caused by conventional AHP due to lack of deterministic approach. The priority vectors of category and criteria are determined separately and multiplied to obtain composite score. Subsequently, the average of fuzzy weights was determined and the preferences of equipment are ranked.

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Improved Multiple Attribute Decision Making Methods

Cited by 19 publications

References 39 publications

Multi‐criteria assessment of yellow, green, and woody biomasses: pre‐screening of potential biomasses as feedstocks for biorefineries

Multi‐criteria assessment of yellow, green, and woody biomasses: pre‐screening of potential biomasses as feedstocks for biorefineries

Towards an Efficient Federated Cloud Service Selection to Support Workflow Big Data Requirements

Comparison of Customer Preference for Bulk Material Handling Equipment through Fuzzy-AHP Approach

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