In today's world, selecting a suitable, reasonable option is challenging for any decision-maker. Where does this problem arise from? According to the decision-making literature, complexity “multiple criteria and interrelationship among them” and uncertainty “the vagueness of judgments and future uncertainty” have made decision-making one of the most significant problems for people. Though multiple-criteria decision-making (MCDM) methods can help decision-makers select the best alternative among multiple criteria and consider their interrelationships, they do not deal with uncertainty. Fuzzy set theory handles uncertainty arising from the vagueness of human thoughts and language in making decisions. Still, the MCDM or FMCDM techniques fail to formulate the probable futures since they use the present information and judgments to collect the data, while in the real world, choosing a method that can deal with the uncertain environment and reduce its impact on outcomes will be effective. Moreover, soft OR methods, such as robustness analysis, strategic choice approach, etc., can deal with future uncertainty but not complexity. Thus, we will use a combination of the Matrix Approach to Robustness Analysis (MARA) and Fuzzy DEMATEL-based ANP (FDANP) to address the weakness mentioned above. This hybrid method has two significant advantages: it can (i) consider numerous scenarios, criteria, and alternatives as well as the interdependency among criteria to address the complexity aspect, and (ii) examine the option performance among the different possible futures and the fuzzy nature of the problem owner's judgments to address the uncertainty aspect. This method is applied to a case related to starting a new business in Iran. The results show that by concerning the environmental situations and the possible future of Iran, education service is the most robust business to start.
Evaluating and ranking schools are noteworthy for parents of students and upstream institutions (in Iran, the Ministry of Education). In this process, quantitative criteria, including educational activities, human resources, space and equipment, and administrative-financial indicators, are commonly investigated. This process is carried out only by the upstream institutions and the view of the system from the perspective of another stakeholder, namely, the students’ parents, are ignored and qualitative-judgmental indicators do not involve the school evaluation results. Consequently, in this study, we used the opinions of five parents of students and five experienced school administrators to capture the perspectives of both key system stakeholders. In addition, to perform a more comprehensive analysis, we added three qualitative criteria that are less noticed within the problem (social environment, health, and students), along with their sub-criteria to the criteria obtained from the research background. We eliminated the less influential sub-criteria using the Delphi technique and continued the study with 10 criteria and 53 sub-criteria. Then, using two widely used methods in this field, AHP and TOPSIS, we determined the weight of the sub-criteria and the ranking based on the experts’ views. In addition, to deal with the ambiguity in experts’ judgments, we transformed the crisp data into fuzzy data. We applied the proposed methodology to rank 15 schools in Tehran, Iran. The results showed that the proposed quantitative criteria significantly impact the schools ranking. In addition, according to the sensitivity analysis results, it was found that ignoring the views of the system from another stakeholder can distort the results. Finally, directions for future research were suggested based on current research limitations.
Governments frequently partner with the private sector to provide infrastructure and public services. These cooperations, known as public–private partnerships (PPPs), have often failed. Sometimes, due to the problem’s complexity, the public sector cannot choose the right partner for these projects, which is one of the main reasons for failures. Complexity in such problems is associated with a large number of indicators, imprecise judgments of decision-makers or problem owners, and the unpredictability of the environment (under conditions of uncertainty). Therefore, presenting a simplified algorithm for this complicated process is the primary goal of the current research so that it can consider the problem’s various dimensions. While many researchers address the critical risk factors (CRFs) and others focus on key performance indicators (KPIs), this research has considered both CRFs and KPIs to choose the best private-sector partner. In addition, we used single-valued neutrosophic sets (SVNSs) to collect decision-makers’ views, which can handle ambiguous, incomplete, or imprecise information. Next, by defining the ideal alternative and using the similarity measure, we specified the ranks of the alternative. Additionally, to face the uncertain environment, we examined the performance of options in four future scenarios. The steps of the proposed algorithm are explained in the form of a numerical example. The results of this research showed that by employing a simple algorithm, even people who do not have significant operations research knowledge could choose the best option by paying attention to the dimensions of the problem complexity.
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