A stochastic fuzzy multi-criteria decision-making model for optimal selection of clean technologies

Promentilla, Michael Angelo B.; Janairo, Jose Isagani B.; Yu, Derrick Ethelbhert C.; Pausta, Carla Mae J.; Beltran, Arnel B.; Huelgas-Orbecido, Aileen; Tapia, John Frederick D.; Aviso, Kathleen B.; Tan, Raymond R.

doi:10.1016/j.jclepro.2018.02.183

Cited by 35 publications

(26 citation statements)

References 21 publications

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“…Intelligent manufacturing based on big data linked to computer numerical control (CNC) machine are of great importance to save resources (Chen et al, 2015). Fuzzy Analytic Hierarchical Network Process were implemented to reduce the vagueness and complexity presented in the green technology selection process (Promentilla et al, 2018). Hierarchical optimization drive a combination of a systematic modelling together with an efficient optimization model on the machine tools that facilitate solving two main goals: reducing the processing time and power consumption (Wójcicki, Bianchi, & Tolio, 2018).…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of green technology thermal drilling process using grey-fuzzy logic method

Kumar

Hynes²,

Pruncu

et al. 2019

Journal of Cleaner Production

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Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of green technology thermal drilling process using grey-fuzzy logic method

Kumar

Hynes²,

Pruncu

et al. 2019

Journal of Cleaner Production

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“…When interdependencies between objects in the hierarchy need to be taken into account, Analytic Network Process (ANP) (Saaty, 2008a,b), which is a generalisation of the AHP, must be used. Promentilla et al (2018) proposed a combination of ANP, FAHP, FST, a beta-PERT distribution, and a Monte Carlo simulation.…”

Section: By Combining Integrated Value Model For Sustainablementioning

confidence: 99%

“…The FSAHP methods discussed above (Jato-Espino et al, 2014;Promentilla et al, 2018) allow the DMs to emphasise the most likely preference. However, when DMs do not want to give too much emphasis on the most likely preference, proposed another type of language expression, namely interval judgement, that was combined with a probabilistic distribution, lexicographic goal programming, and a Monte Carlo simulation.…”

Section: By Combining Integrated Value Model For Sustainablementioning

confidence: 99%

An integrated constrained fuzzy stochastic analytic hierarchy process method with application to the choice problem

Sitorus

Cilliers

2019

Expert Systems with Applications

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The ability of the Analytical Hierarchy Process (AHP) when applied to the choice problem in the context of group decision making under uncertainty has been often criticised. AHP is not able to fully capture the various opinions and the uncertainty associated with the lack of information. This work develops an integrated constrained fuzzy stochastic analytic hierarchy process (IC-FSAHP) method in order to deal with the aforementioned drawbacks. IC-FSAHP combines two existing fuzzy AHP (FAHP) methods and further extends its applicability by implementing stochastic simulations. A case study has been conducted in order to assess the ability of IC-FSAHP; the results showed that IC-FSAHP is able to capture the uncertainty and multiple DMs' opinions. This paper also discusses the effect that the number of DMs has in enhancing rank discrimination. Besides, the possibility of the occurrence of rank reversal because of the use of IC-FSAHP has been analysed. The results showed that the ranking of alternatives was preserved throughout the changes in the number of alternatives, however, rank reversal occurred in the case of changes in judgements scales. By comparing the U-uncertainty in fuzzy global priorities obtained using IC-FSAHP to that obtained using an existing FSAHP method, we show that our method is capable of minimising the risk of losing important knowledge during the computations. We also discuss how IC-FSAHP can decrease the uncertainty and increase the reliability of the decisions by means of robust computations.

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“…However, in the real world, many economic parameters and energy demands often exhibit ambiguities, which can be shown as fuzzy sets [27,28]. Fuzzy possibilistic programming (FPP) theory can effectively address the fuzzy uncertainties of goals and constraints [29].…”

Section: Literature Reviewmentioning

confidence: 99%

“…This cost is calculated in terms of the unit-price and the amount of importing electricity. (28) where N E t (NE t 1 , NE t 2 , NE t 3 ) is the price for unit of imported electricity; ED th is the amount of imported electricity. 7Operation cost for first-stage electricity generation.…”

Section: Development Of Drsfo-ees Modelmentioning

confidence: 99%

Robust Planning of Energy and Environment Systems through Introducing Traffic Sector with Cost Minimization and Emissions Abatement under Multiple Uncertainties

et al. 2019

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Motor vehicles have been identified as a growing contributor to air pollution, such that analyzing the traffic policies on energy and environment systems (EES) has become a main concern for governments. This study developed a dual robust stochastic fuzzy optimization - energy and environmental systems (DRSFO-EES) model for sustainable planning EES, while considering the traffic sector through integrating two-stage stochastic programming, robust two-stage stochastic optimization, fuzzy possibilistic programming, and robust fuzzy possibilistic programming methods into a framework, which can be used to effectively tackle fuzzy and stochastic uncertainties as well as their combinations, capture the associated risks from fuzzy and stochastic uncertainties, and thoroughly analyze the trade-offs between system costs and reliability. The proposed model can: (i) generate robust optimized solutions for energy allocation, coking processing, oil refining, heat processing, electricity generation, electricity power expansion, electricity importation, energy production, as well as emission mitigation under multiple uncertainties; (ii) explore the impacts of different vehicle policies on vehicular emission mitigation; (iii) identify the study of regional atmospheric pollution contributions of different energy activities. The proposed DRSFO-EES model was applied to the EES of the Beijing-Tianjin-Hebei (BTH) region in China. Results generated from the proposed model disclose that: (i) limitation of the number of light-duty passenger vehicles and heavy-duty trucks can effectively reduce vehicular emissions; (ii) an electric cars’ policy is enhanced by increasing the ratio of its power generated from renewable sources; and (iii) the air-pollutant emissions in the BTH region are expected to peak around 2030, because the energy mix of the study region would be transformed from one dominated by coal to one with a cleaner pattern. The DRSFO-EES model can not only provide scientific support for the sustainable managing of EES by cost-effective ways, but also analyze the desired policies for mitigating pollutant emissions impacts with a risk adverse attitude under multiple uncertainties.

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A stochastic fuzzy multi-criteria decision-making model for optimal selection of clean technologies

Cited by 35 publications

References 21 publications

Multi-objective optimization of green technology thermal drilling process using grey-fuzzy logic method

Multi-objective optimization of green technology thermal drilling process using grey-fuzzy logic method

An integrated constrained fuzzy stochastic analytic hierarchy process method with application to the choice problem

Robust Planning of Energy and Environment Systems through Introducing Traffic Sector with Cost Minimization and Emissions Abatement under Multiple Uncertainties

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