Performance Evaluation of Alternative Jet Fuels using a hybrid MCDA method

Xu, Bing; Kolosz, Ben; Andrésen, John M.; Ouenniche, Jamal; Greening, Phil; Chang, Tsung‐Sheng; Maroto‐Valer, M. Mercedes

doi:10.1016/j.egypro.2019.01.275

Cited by 12 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Macharis et al (2004) were the first to recommend the integration of some useful AHP features with PROMETHEE. Xu et al (2019) also applied a hybrid AHP-PROMETHEE framework to evaluate competing alternative jet fuels. Burak et al (2022) applied HF-AHP-PROMETHEE II, so that "Hesitant Fuzzy Analytic Hierarchy Process" (HF-AHP) is first implemented to determine the importance weights of criteria, and then "Hesitant Fuzzy Preference Ranking Organization Method for Enriching Evaluations II" (HF-PROMETHEE II) is used to assess and rank the irrigation method alternatives, as examples.…”

Section: Hybrid Approach Methodologymentioning

confidence: 99%

Multicriteria Methodology for Selection of a Personal Electric Vehicle

Gomes

Sousa

Pereira

et al. 2023

BJO&PM

View full text Add to dashboard Cite

A methodology that can support the decision process for selecting an Electric Vehicle (EV) is crucial. Considering the quantity, diversity, and complexity of existing EV models, their advantages and disadvantages, and people's increasing concern for sustainability, selecting and buying a personal EV appropriate to one's needs and requirements is not a simple task. Currently, criteria such as price, consumption per 100 km, range, comfort, brand, safety, and technology can be used to analyse available EV models. In this context, the decision-making process can be supported by the Multicriteria Decision Analysis (MCDA), “a method that clearly structures and evaluates complex problems with several alternatives, conflicting criteria, and complex scenarios. The novelty of this paper is to present a hybrid MCDA approach, combining two powerful methods (AHP and PROMETHEE) into a single framework to assist EV selection by any given person who has no multicriteria knowledge. A survey was conducted to define relevant criteria for a private person selecting an EV. The approach was validated for EV selection, considering the available offer within a target value, based solely on available public data, and excluding qualitative criteria. The paper discusses a case study whereby the proposed approach, using both quantitative and qualitative criteria, assists a decision-maker in selecting an EV from a set of alternatives, validated by the decision-maker, proving the importance and relevance of supporting the decision-making process.

show abstract

Section: Hybrid Approach Methodologymentioning

confidence: 99%

Multicriteria Methodology for Selection of a Personal Electric Vehicle

Gomes

Sousa

Pereira

et al. 2023

BJO&PM

View full text Add to dashboard Cite

show abstract

“…The MCDA technique is utilized in a number of studies and applications such as the evaluation of challenges in reliable solar panel selection [29], measurement technique selection for particulate matter emission [30], sustainable material selection for construction projects [31], utility-scale solar photovoltaic siting with social considerations [32], performance assessment of alternative jet fuels [33], sustainability evaluation of the energy sector [34], analysis of waste-to-energy management strategies [35], sustainability analysis of the second generation of biofuels [36], and assessment of energy storage systems for grid applications [37]. Further, studies focusing on a country or location-specific assessment were also found to utilize MCDA methodology.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment of Clean Energy Transition Potential in Major Power-Producing States of India Using Multi-Criteria Decision Analysis

Indrajayanthan¹,

Mohanty²

2022

Sustainability

View full text Add to dashboard Cite

India has an ambitious target to promote clean energy penetration, but as of 2021, the electricity mix of India is dominated by coal to about 71%. Therefore, analyzing the clean energy potential and the ability of the individual states to entrench energy transition in the upcoming years will be supportive for policymakers. This study is propounded to assess the clean energy transition potential with a focused analysis on seven major power-producing states of India. These states include Maharashtra, Gujarat, Tamil Nadu, Uttar Pradesh, Karnataka, Madhya Pradesh, and Andhra Pradesh. The clean energy transition potential assessment is performed by utilizing multi-criteria decision analysis methodologies such as the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Objective Optimization Method by Ratio Analysis (MOORA). Further, the analysis is performed against four major criteria that include high carbon energy resource dependency, low carbon energy resource dependency, clean energy potential, and policy support. Altogether, the assessment criteria include four primary level criteria and fourteen secondary level parameters. In order to reflect the significance of each parameter and criterion to the characteristics of clean energy transition potential, appropriate weightage is provided using the Fuzzy Analytic Hierarchy Process (AHP). The results indicate that Gujarat has the highest clean energy transition potential in both the multi-criteria decision analysis methods. On the other hand, Uttar Pradesh exhibited the least performance, and a complete energy transition to clean energy resources is less likely in this state. The rest of the states obtained intermediate ranking, and a comparative analysis between the two methods was also accomplished. This study suggests that India should focus on the clean energy policy with vigorous efforts on top-performing states which will effectively accelerate the power sector decarbonization.

show abstract

“…MCDAbased DSTs involve evaluating alternative solutions to a problem through multiple weighted evaluation criteria with the weighting technique employed impacting results (See Wang et al, 2009). MCDA DSTs have been employed in fields that require balancing areas with conflicting objectives, such as sustainable energy and aviation biofuels, which require balancing across social, economic, and environmental activities (see Afgan and Carvalho, 2002;Jovanovic et al, 2009;Perimenis et al, 2011;Martinkus et al, 2018;Ghose et al, 2019;Xu et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Searching for Culture in “Cultural Capital”: The Case for a Mixed Methods Approach to Production Facility Siting

et al. 2022

View full text Add to dashboard Cite

Site selection modeling receives much attention in the aviation biofuels literature to ensure sustainability of the aviation biofuel supply chain. These models seek to reflect the multitude of factors and conditions necessary for supply chain success. Social factors impacting that success have received increasingly greater attention but are often excluded due to difficulties in obtaining accurate and standard measures. Some of the most promising work in this arena utilizes a “community capitals approach” to create statistically grounded decision support tools (DSTs) intended to provide rapid assessment of the social characteristics of potential facility locations. Despite the value of the community capitals approach, this methodology is still marked by inconsistent predictivity due to an inability to reliably assess the cultural and historical nuances of local communities that are so vitally important to the long-term viability of these costly projects. This paper more fully examines the Community Assets and Attributes Model (CAAM) that has been developed and applied in the Pacific Northwest to incorporate social assets in site selection modeling. Based on ethnographic fieldwork in Colorado and Wyoming dealing with biomass/bioenergy facility siting, we argue that cultural capital, a key component of the CAAM, is biased to urban locations due to the measurements incorporated. As a result of this bias, current site selection modeling based on the Community Capitals Framework (CCF) does not accurately reflect rural community assets. We assert that the CAAM does not actually measure cultural capital but a product of cultural capital, namely creativity, and innovation Our mixed methods approach that combines quantitative assessment with ethnographic research highlights the limits of the CAAM by revealing that local residents in largely rural counties showed willingness to innovate in some cases but in others referred to history with similar industries that may limit support. The quantitative cultural capital measurements of the CAAM for the four counties we examine, which range in scores from −0.53 to 2, do not capture these dynamics. These scores would generally suggest moderate to high levels of support for biomass/bioenergy facilities, but the ethnographic research provides nuance for or against support that are not reflected in the quantitative capital scores. This suggests that the quantitative CAAM scores could be misleading without added qualitative context. This work demonstrates that a mixed methods approach, combining ethnographic and historical methodologies with existing quantitative community capital approaches, will produce a more effective predictive methodology for facility siting due to its heightened ability to gather critical data on place-based values, beliefs, and historical legacies relating to natural resource development in general, and the timber industry specifically.

show abstract

Performance Evaluation of Alternative Jet Fuels using a hybrid MCDA method

Cited by 12 publications

References 2 publications

Multicriteria Methodology for Selection of a Personal Electric Vehicle

Multicriteria Methodology for Selection of a Personal Electric Vehicle

Assessment of Clean Energy Transition Potential in Major Power-Producing States of India Using Multi-Criteria Decision Analysis

Searching for Culture in “Cultural Capital”: The Case for a Mixed Methods Approach to Production Facility Siting

Contact Info

Product

Resources

About