Economic, technological, social, and political developments stressed the need for shifts in energy-mix. Therefore it is important to provide a rationale for sustainable decision making in energy policy. The aim of this paper is to develop the multi-criteria decision support framework for choosing the most sustainable electricity production technologies. Given selection of sustainable energy sources involves many conflicting criteria, multi-criteria decision methods MULTIMOORA and TOPSIS were employed for the analysis. The indicator system covering different approaches of sustainability was established. The analysis proved that the future energy policy should be oriented towards the sustainable energy technologies, namely water and solar thermal ones. It is the proposed multi-criteria assessment framework that can constitute a basis for further sub-regional optimization of sustainable energy policy.
Many individual attributes considered for personnel selection such as organizing ability, creativity, personality, and leadership exhibit vagueness and imprecision. The fuzzy set theory appears as an essential tool to provide a decision framework that incorporates imprecise judgments inherent in the personnel selection process. In this paper, a fuzzy multi-criteria decision making (MCDM) algorithm using the principles of fusion of fuzzy information, 2-tuple linguistic representation model, and technique for order preference by similarity to ideal solution (TOPSIS) is developed. The proposed method is apt to manage information assessed using both linguistic and numerical scales in a decision making problem with multiple information sources. Furthermore, it enables managers to deal with heterogeneous information. The decision making framework presented in this paper employs ordered weighted averaging (OWA) operator that encompasses several operators as the aggregation operator since it can implement different aggregation rules by changing the order weights. The aggregation process is based on the unification of information by means of fuzzy sets on a basic linguistic term set (BLTS). Then, the unified information is transformed into linguistic 2-tuples in a way to rectify the problem of loss information of other fuzzy linguistic approaches. The computational procedure of the proposed framework is illustrated through a personnel selection problem reported in an earlier study.
A fuzzy multi-criteria group decision making approach that makes use of quality function deployment (QFD), fusion of fuzzy information and 2-tuple linguistic representation model is developed for supplier selection. The proposed methodology seeks to establish the relevant supplier assessment criteria while also considering the impacts of inner dependence among them. Two interrelated house of quality matrices are constructed, and fusion of fuzzy information and 2-tuple linguistic representation model are employed to compute the weights of supplier selection criteria and subsequently the ratings of suppliers. The proposed method is apt to manage non-homogeneous information in a decision setting with multiple information sources. The decision framework presented in this paper employs ordered weighted averaging (OWA) operator, and the aggregation process is based on combining information by means of fuzzy sets on a basic linguistic term set. The proposed framework is illustrated through a case study conducted in a private hospital in Istanbul.
This paper focuses on standards battles for photovoltaic technological systems. Five photovoltaic technologies are currently commercially available and it is still unclear which of these technologies will emerge as the dominant design. Based on the literature and on expert interviews, we develop and analyze categories and factors for technology dominance for these systems. By applying the analytic hierarchy process, we determine the importance of these factors and the chances of one of these five technologies becoming the dominant photovoltaic technology. The crisp and fuzzy (logarithmic fuzzy preference programming) analytic hierarchy process method is used to analyze the data. The results show that the standard support strategy is the most important category, and that pricing strategy and technological superiority are the most influential factors in the dominance process. Furthermore, the first generation technology mono-crystalline silicon photovoltaic has the best chance of achieving dominance (30% chance). The results of this study are useful for multiple stakeholders (e.g. energy policy makers and photovoltaic module companies) who have to make the decision as to which standard should be supported for photovoltaic technology.
Reverse logistics (RL) has been regarded as a key driving force for remanufacturing. However, there are great uncertainties in terms of quality and quantity of used components for RL. There are also complexities in suppliers and operations. These make decision-making of RL very complex. In order to identify the best collection mode for used components, a demand-matching oriented Multiple Criteria Decision Making (MCDM) method is established. In this method, the damage level and remaining service life are firstly incorporated into the evaluation criteria of reuse modes, then a hybrid method (AHP-EW) that integrates Analytic Hierarchy Process (AHP) and Entropy Weight (EW) method is applied to derive criteria weights and the grey Multi-Attributive Border Approximation Area Comparison (MABAC) is adopted to rank the collection modes. Finally, sensitivity analysis is implemented to test the stability of the proposed method, and a demands-matching method is proposed to validate and evaluate the feasibility of the optimal alternative. The collection of used pressurizers is taken as case study to validate the applicability of the proposed model. The results showed the effectiveness of the proposed method in MCDM of RL.
► This study proposes a hybrid fuzzy multi criteria decision making model that can assist in evaluating green suppliers. ► The proposed model integrates DEMATEL, ANP and TOPSIS methods in a fuzzy context. ► Ford Otosan is selected as a case company in this study for the evaluation of green supplier alternatives. ► The supplied case study provides additional insights for research and practical applications. It is well known that “green” principles and strategies have become vital for companies as the public awareness increased against their environmental impacts. A company’s environmental performance is not only related to the company’s inner environmental efforts, but also it is affected by the suppliers’ environmental performance and image. For industries, environmentally responsible manufacturing, return flows, and related processes require green supply chain (GSC) and accompanying suppliers with environmental/green competencies. During recent years, how to determine suitable and green suppliers in the supply chain has become a key strategic consideration. Therefore this paper examines GSC management (GSCM) and GSCM capability dimensions to propose an evaluation framework for green suppliers. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The identified components are integrated into a novel hybrid fuzzy multiple criteria decision making (MCDM) model combines the fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), the Analytical Network Process (ANP), and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) in a fuzzy context. A case study is proposed for green supplier evaluation in a specific company, namely Ford Otosan.
► A novel hybrid model is created for the professional selection problem. ► The weights of criteria related with sniper are calculated by fuzzy ANP. ► Fuzzy TOPSIS is used to determine the most suitable candidates. ► Final ranking is obtained with fuzzy ELECTRE. Personnel selection is an important process in management. Sniper selection as a subset of personnel selection contains different characteristics compared to selection of other personnel. The multi criteria nature and the presence of both qualitative and quantitative factors make it considerably more complex. This study proposes a fuzzy hybrid multicriteria decision making approach enabling the combination of both qualitative and quantitative factors. The use of a combination of Fuzzy ANP, Fuzzy TOPSIS, Fuzzy ELECTRE techniques, proposing a MCDM approach for sniper selection, and applying these to a real case are the unique features of this study.
Green material selection is a crucial step for the material industry to comprehensively improve material properties and promote sustainable development. However, because of the subjectivity and conflicting evaluation criteria in its process, green material selection, as a multi-criteria decision making (MCDM) problem, has been a widespread concern to the relevant experts. Thus, this study proposes a hybrid MCDM approach that combines decision making and evaluation laboratory (DEMATEL), analytical network process (ANP), grey relational analysis (GRA) and technique for order performance by similarity to ideal solution (TOPSIS) to select the optimal green material for sustainability based on the product's needs. A nonlinear programming model with constraints was proposed to obtain the integrated closeness index. Subsequently, an empirical application of rubbish bins was used to illustrate the proposed method. In addition, a sensitivity analysis and a comparison with existing methods were employed to validate the accuracy and stability of the obtained final results. We found that this method provides a more accurate and effective decision support tool for alternative evaluation or strategy selection.