Multi-criteria selection of electric power plants using analytical hierarchy process

Akash, Bilal; Mamlook, Rustom; Mohsen, Mousa S.

doi:10.1016/s0378-7796(99)00004-8

Cited by 188 publications

(72 citation statements)

References 24 publications

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“…The weight of the costs objective was slightly higher when it was a main objective (mean 0.15, n=127) than a sub-objective (mean 0.10, n=29). In three cases, costs were not included in the weight elicitation procedure but they were compared to the results of MCDA in a two dimensional graph (Bana e Costa et al, 2004, Neckles et al, 2014 or by calculating cost-to-benefit ratios of alternatives (Akash et al, 1999). Analysis 1b.…”

Section: Costs Objectivementioning

confidence: 99%

Are objectives hierarchy related biases observed in practice? A meta-analysis of environmental and energy applications of Multi-Criteria Decision Analysis

Marttunen

Belton

Lienert

2018

European Journal of Operational Research

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Procedural and behavioural biases have received little attention in recent Multi-Criteria Decision Analysis (MCDA) research. Our literature review shows that most research on biases was done 15-30 years ago.This study focuses on biases that are introduced at an early stage of MCDA when building objectives hierarchies and their effect on the weights. The main objective is to investigate whether prior findings regarding such biases, which were mostly based on laboratory experiments, can be found in real-world applications. We conducted a meta-analysis of the objectives hierarchies and weight elicitation procedures in 61 environmental and energy MCDA cases. Relationships between the structural characteristics of the objectives hierarchy and assigned objectives' weights were analysed with statistical tests. Our main research questions were: (i) how does hierarchy size and structure affect the objectives' weights? (ii) how are weights distributed across economic, social and environmental objectives? (iii) is there support for the equalising bias? Our findings are mostly aligned with earlier research and suggest that the hierarchy structure and content can substantially influence weight distributions. For example, hierarchical weighting seems to be sensitive to the asymmetry bias, which can occur when a hierarchy has branches that differ in the number of sub-objectives. We found no evidence for the equalising bias. We highlight issues deserving more attention when developing objectives hierarchies and eliciting weights.The research demonstrates the potential to use meta-analysis, which has not previously been used in this way in the MCDA field, to learn from a collection of applications.

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Section: Costs Objectivementioning

confidence: 99%

Are objectives hierarchy related biases observed in practice? A meta-analysis of environmental and energy applications of Multi-Criteria Decision Analysis

Marttunen

Belton

Lienert

2018

European Journal of Operational Research

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“…based on multiple criteria (Akash et al 1999;Gamboa and Munda 2007;Georgopoulu et al 2003;Erol and Kilkis 2012). In this research, AHP is used to rank following alternatives:…”

Section: Decision Support Systemmentioning

confidence: 99%

A visual energy performance assessment and decision support tool for dwellings

et al. 2013

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Background: The target for carbon dioxide (CO 2 ) emissions reduction in the UK is set at 20% by 2020 and 80% by 2050. The UK housing stock is one of the least energy efficient in Europe. The energy used in homes accounts for more than a quarter of energy use and carbon dioxide emissions in Great Britain. Therefore, it is imperative to improve the energy performance of the existing housing stock and fully exploit energy efficiency and renewable energy interventions. The UK has developed several policies and initiatives to improve the energy performance of the housing stock and there are a number of databases that hold information about the condition of the housing stock. However, existing approaches and tools do not allow decision makers to assess the environmental and economic effectiveness of CO 2 reduction strategies at the neighbourhood level.

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“…AHP (Akash et al, 1999;Chatzimouratidis and Pilavachi, 2007;Kablan, 2004;Mohsen and Akash, 1997) as well as pair-wise comparison (Mamlook et al, 2001;Wang et al, 2008a; are known as the most frequently used subjective weighting methods (rank-order weights methods) in sustainable energy decision making. Different applications of AHP can be found and classified according to theme, including: selection, evaluation, benefit-cost analysis, allocations, planning and development, priority and ranking, and decision-making.…”

Section: Multicriteria Evaluation Approach and Gis In Energy Related mentioning

confidence: 99%

A multicriteria approach to evaluate offshore wind farms siting in Greece

2013

Global NEST Journal

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Wind energy offers significant potential for greenhouse gas emissions reductions. Most applications have been developed onshore but the planning and siting conflicts with other land uses have created considerable interest and motivated research to offshore wind energy establishments. In this paper, a systematic methodology in order to investigate the most efficient areas of offshore wind farms' siting in Greece is performed, integrating multi-criteria decision making (MCDM) methods and Geographic Information Systems (GIS) tools. In the first level of analysis, all coastal areas that don't fulfill a certain set of criteria (wind velocity, protected areas, water depth) are identified with the use of Geographical Information Systems (GIS) and excluded from further analysis. The Analytical Hierarchy Process is performed in the evaluation phase and pairwise comparisons provide the most appropriate sites to locate offshore wind farms. Information concerning evaluation criteria (average wind velocity, distance to protected areas, distance to ship routes, distance to the shore and distance of possible connection to the existing electricity network) is retrieved through GIS, eliminating the subjectivity in judgments. The whole methodology contributes to the portrait of the geographic analysis and stands as the last image of the space characteristics suitable for offshore wind farms. KEYWORDS:Analytical Hierarchy Process, Geographical Information Systems, offshore establishments, pairwise comparisons, renewable energy resources. INTRODUCTIONAdoption of renewable energy technologies and promotion of green energy have internationally been recognized as a way towards independency from fuel oils. Green energy is provided by the natural environment and one of the most widely exploited and rapidly evolved types of renewable energy is the wind energy. Onshore wind energy technology is more mature than offshore, but nowadays, there is a considerable trend to the establishment of offshore wind farms. Although offshore wind farms present higher investment, operational and maintenance cost, the significant offshore wind resource potential, the higher quality wind resources located at sea, the ability to use even larger wind turbines due to avoidance of certain land and the ability of construction of even larger power plants than onshore, as there is no geographical "limit", form the primary motivations of developing offshore wind energy.

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Multi-criteria selection of electric power plants using analytical hierarchy process

Cited by 188 publications

References 24 publications

Are objectives hierarchy related biases observed in practice? A meta-analysis of environmental and energy applications of Multi-Criteria Decision Analysis

Are objectives hierarchy related biases observed in practice? A meta-analysis of environmental and energy applications of Multi-Criteria Decision Analysis

A visual energy performance assessment and decision support tool for dwellings

A multicriteria approach to evaluate offshore wind farms siting in Greece

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