A comparative study of multiple-criteria decision-making methods under stochastic inputs

This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated methods, such as technique for the order of preference by similarity to the ideal solution (TOPSIS) and Preference Ranking Organization method for enrichment evaluation (PROMETHEE), better predict the optimum design alternative

Type-2 neutrosophic number based multi-attributive border approximation area comparison (MABAC) approach for offshore wind farm site selection in USA.

The technical, logistical, and ecological challenges associated with offshore wind development necessitate an extensive site selection analysis. Technical parameters such as wind resource, logistical concerns such as distance to shore, and ecological considerations such as fisheries all must be evaluated and weighted, in many cases with incomplete or uncertain data. Making such a critical decision with severe potential economic and ecologic consequences requires a strong decision-making approach to ultimately guide the site selection process. This paper proposes a type-2 neutrosophic number (T2NN) fuzzy based multi-criteria decision-making (MCDM) model for offshore wind farm (OWF) site selection. This approach combines the advantages of neutrosophic numbers sets, which can utilize uncertain and incomplete information, with a multi-attributive border approximation area comparison that provides formulation flexibility and easy calculation. Further, this study develops and integrates a techno-economic model for OWFs in the decision-making. A case study is performed to evaluate and rank five proposed OWF sites off the coast of New Jersey. To validate the proposed model, a comparison against three alternative T2NN fuzzy based models is performed. It is demonstrated that the implemented model yields the same ranking order as the alternative approaches. Sensitivity analysis reveals that changing criteria weightings does not affect the ranking order

ANALYTICAL HIERARCHY PROCESS AND TOPSIS APPROACH TO PERFORM SUPPLIER SELECTION IN CONSTRUCTION INDUSTRY

Suppliers play an important role as suppliers of production raw materials, making companies must choose suppliers correctly. This paper discusses the best supplier selection to avoid financial and non-financial losses This paper uses four criteria for determining the best supplier, such as price, lead time, payment terms, and quality & service. A reasonable price following initial planning is always expected by the customers. Lead time is an essential consideration to ensure on-time delivery. Failure to perform on-time delivery will direct in poor payment terms. Besides, any products that do not meet quality standards will always lead to waste. Analytical Hierarchy Process (AHP) and Technique Order Preference by Similarity to Ideal Solution (TOPSIS) are used to select and determine the best supplier for the company. Pairwise comparisons are performed by making comparisons between each criterion and the alternatives made at each level of the hierarchy in pairs to get the value of the importance of the elements in the form of a qualitative opinion. The weight values for price, lead time, payment terms, and quality and service criteria are 0.0709, 0.1409, 0.2682, and 0.5200, respectively. According to AHP, alternative weight values in each criterion are 0.3899, 0.3063, and 0.3038, namely supplier A (rank 1), supplier B (rank 2), and supplier C (rank 3), respectively. However, the results of supplier selection using the TOPSIS method are supplier B (rank 1), supplier C (rank 2), and supplier A (rank 3) with values of 518.4025, 469.2017, and 412.3928, respectively

Interval type-2 fuzzy sets based multi-criteria decision-making model for offshore wind farm development in Ireland.

Offshore wind energy takes up an important place in Ireland’s renewable generation portfolio thanks to its abundant offshore wind resource. Optimal offshore site selection and developing site-specific energy policy instruments are of key importance to the success of offshore wind energy investments. In this respect, this study aims at developing a multi-criteria decision-making (MCDM) model considering technical, economic, environmental and social criteria to assess Ireland’s most promising offshore wind sites in terms of their sustainable development. An interval type-2 fuzzy sets based MCDM model is developed that integrates the score function with positive and negative solutions to achieve better results. Moreover, advanced energy economic metrics such as levelized cost of electricity with higher resolution are integrated into the decision-making process to make more precise decisions. Case studies are conducted for the five of the offshore sites in development pipeline. Results are compared to those of other state-of-the-art MCDM methods. It is found that Arklow Bank-2 is the most favorable site while Sceirde is the least site. The ranking of other sites is found to be Oriel>Dublin Array>Codling Park. It is shown that the proposed approach is superior in terms of stability and implementation as compared to its counterparts

Selection of biogas, solar, and wind power plants’ locations: An MCDA approach

This study discusses a multi-criteria approach to locating biogas, solar and wind power plants that significantly addresses the challenge of global warming caused by power generation. Because the utility of locations to build renewable energy power plants depends on economic, social and environmental dimensions, after reviewing literature, the sustainable frameworks of criteria affecting the location of biogas, solar and wind power plants were examined in this paper. The offered frameworks are applied to determining the site of biogas, solar, and wind power plants in Iran. The provinces of Iran are assessed as alternatives in this paper. To compute the weight of criteria in the offered framework, data from a sample of experts in Iran are used via an online survey form designed based on the best-worst method (BWM). Using the results of the BWM and the performance data, the overall score are calculated for the various provinces of Iran. The results of this study indicate that energy saving, effect on resources and natural reserves and wind flow, respectively, are the most effective factors for determining the place of biogas, solar and wind power plants, and South Khorasan, Khuzestan, and Khuzestan show the best result for establishing biogas, solar, and wind power plants in Iran respectively

An overview of multi-criteria decision-making methods in dealing with sustainable energy development issues

© 2018 by the authors. The measurement of sustainability is actively used today as one of the main preventative instruments in order to reduce the decline of the environment. Sustainable decision-making in solving energy issues can be supported and contradictory effects can be evaluated by scientific achievements of multi-criteria decision-making (MCDM) techniques. The main goal of this paper is to overview the application of decision-making methods in dealing with sustainable energy development issues. In this study, 105 published papers from the Web of Science Core Collection (WSCC) database are selected and reviewed, from 2004 to 2017, related to energy sustainability issues and MCDM methods. All the selected papers were categorized into 9 fields by the application area and into 10 fields by the used method. After the categorization of the scientific articles and detailed analysis, SWOT analysis of MCDM approaches in dealing with sustainable energy development issues is provided. The widespread application and use of MCDM methods confirm that MCDM methods can help decision-makers in solving energy sustainability problems and are highly popular and used in practice

Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: a mini review

Abstract:Site suitability problems in renewable energy studies have taken a new turn since the advent of geographical information system (GIS). GIS has been used for site suitability analysis for renewable energy due to its prowess in processing and analyzing attributes with geospatial components. Multi-criteria decision making (MCDM) tools are further used for criteria ranking in the order of influence on the study. Upon location of most appropriate sites, the need for intelligent resource forecast to aid in strategic and operational planning becomes necessary if viability of the investment will be enhanced and resource variability will be better understood. One of such intelligent models is the adaptive neuro-fuzzy inference system (ANFIS) and its variants. This study presents a mini-review of GIS-based MCDM facility location problems in wind and solar resource site suitability analysis and resource forecast using ANFIS-based models. We further present a framework for the integration of the two concepts in wind and solar energy studies. Various MCDM techniques for decision making with their strengths and weaknesses were presented. Country specific studies which apply GIS-based method in site suitability were presented with criteria considered. Similarly, country-specific studies in ANFIS-based resource forecasts for wind and solar energy were also presented. From our findings, there has been no technically valid range of values for spatial criteria and the analytical hierarchical process (AHP) has been commonly used for criteria ranking leaving other techniques less explored. Also, hybrid ANFIS models are more effective compared to standalone ANFIS models in resource forecast, and ANFIS optimized with population-based models has been mostly used. Finally, we present a roadmap for integrating GIS-MCDM site suitability studies with ANFIS-based modeling for improved strategic and operational planning

Multi-criteria evaluation of renewable energy alternatives for electricity generation in a residential building.

The residential sector is well known to be one of the main energy consumers worldwide. The purpose of this study is to select the best renewable energy alternatives for electricity generation in a residential building by using a new integrated fuzzy multi-criteria group decision-making method. In renewable energy decision-making problems, the preferences of experts and decision-makers are generally uncertain. Furthermore, it is challenging to quantify the reel performance of renewable energy alternatives using a set of exact values. Fuzzy logic is commonly applied to deal with those uncertainties. The method proposed in this paper combines different methods. First, the Delphi method is used in order to select a preliminary set of renewable energy alternatives for electricity generation as well as a preliminary set of criteria (economic, environmental, social, etc.). Then, the questionnaire is used to study the renewable energy alternatives preferences of the residents of the residential building’. Later, the FAHP (Fuzzy Analytical Hierarchy Process) is implemented to obtain the weighs of the criteria taking into consideration uncertainties in expert's judgments. Finally, the FPROMETHEE (Fuzzy Preference Ranking Organization Method for Enrichment Evaluation) global ranking is performed in order to get a complete ranking of the renewable energy alternatives taking into account uncertainties related to the alternatives' evaluations. The originality of this paper comes from the application of the proposed integrated Delphi- FAHP- FPROMETHEE methodology for the selection of the best renewable energy alternatives for electricity generation in a residential building. A case study has validated the effectiveness and the applicability of the proposed method. The results reveal that the proposed integrated method helps to formulate the problem and is particularly effective in handling uncertain data. It facilitates the selection of the best renewable energy alternatives in a manner that is participatory, comprehensive, robust, and reliable

Ranking and aggregation-based multiple attributes decision making method for sustainable energy planning

In sustainable energy planning, the selection of a suitable Renewable Energy Sources (RES) for energy supply and evaluation of different RES technologies is a complex decision-making process. This is because there are many conflicting criteria that need to be considered. It becomes more complicated when qualitative data is involved in addition to quantitative data. Previous studies use Multiple Attribute Decision Making (MADM) methods for decision making, which work well with quantitative data but not with qualitative data. There are some MADM methods that can handle with both qualitative and quantitative data but suffer from complex computation burden. It becomes more difficult when more than one MADM method or more than one Decision Maker (DM) need to be considered. Different results will be obtained since different MADM methods or different DMs provide different results. This thesis proposes a new MADM method to overcome the limitations of previous methods. It consists of two parts which are ranking and aggregation techniques. The proposed ranking technique able to deal with quantitative and qualitative data through sorting process according to beneficial and non-beneficial criteria without normalizing the data. Then the proposed aggregation technique able to overcome the problem of different rankings due to different MADM methods or different DMs. The idea is to modify the preference ranking organization method for enrichment evaluations, where a preference index is assigned when comparing two alternatives at one time with respect to their ranking position instead of the criteria. Four case studies are examined to illustrate the effectiveness of the proposed ranking method while three case studies are evaluated to demonstrate the applications of the proposed aggregation method. For verification, Spearman’s rank correlation coefficient is utilized to determine an agreement of the proposed method with the existing MADM methods. The results show the strength of the proposed method as it yields a correlation coefficient of more than 0.87 in all case studies. The results show an excellent correlation with those obtained by past researchers, which specifically prove the applicability of the proposed method for solving sustainable energy planning decision problem