This paper explores the strategic behavior of power generators under green certificate trading policies, considering both renewable and conventional energy generators. Using game theory, we construct a Nash equilibrium model that incorporates the unit price of green certificates, the required quantity of certificates, and the cap on the quantity. By applying the Karush-Kuhn-Tucker conditions, we reform this Nash equilibrium problem as a mixed complementarity system, which can be solved by MATLAB software. Furthermore, we conduct sensitivity analysis and numerical tests on a number of important parameters. The results reveal that, under certain conditions, the unit price of green certificates does not affect the number obtained by renewable energy generators or purchased by conventional energy generators. However, as the required number of certificates for conventional energy generators increases, both the quantity of certificates that renewable generators obtained and conventional generators purchased increase proportionally. Additionally, the outcomes of limiting the quantity of green certificates awarded to renewable energy generators align with government regulations on the purchase requirements for conventional energy generators. This research provides new insights for power generators in ensuring financial viability and optimizing operations under green certificate trading policies. By enhancing carbon emission reduction capacity, these findings may contribute to the effective management of the electrical supply chain.

The current research addresses the inventory classification problem of community pharmacies, which have a dual role as both a vital component of the pharmaceutical supply chain and a typical retail store. Despite the existing literature indicating that pharmacists may lack knowledge on inventory management, it seems that the MCIC literature is weak in explaining how pharmacists can benefit from MCDA techniques in all aspects. To bridge this gap, the study aims to demonstrate that pharmacists can utilize MCDA techniques to gain deeper insights beyond mere classification in the context of inventory management. Real-world data from a community pharmacy in Turkey was classified using the EDAS method. Sensitivity analysis was performed for MCDA inputs, about which pharmacists may lack information. Scenario findings based on criterion weights and threshold values offer important managerial implications for pharmacists. This study provides a critical contribution to the literature on inventory management in community pharmacies by highlighting the potential of MCDA techniques to support decision-making beyond mere classification. The sensitivity analysis also sheds light on areas where pharmacists may lack knowledge and suggests ways to address these gaps. Overall, the study underscores the need for pharmacists to have a deeper understanding of inventory management and highlights the potential benefits of MCDA techniques in addressing this challenge.

The decision to purchase the best available electric power wheelchair (EPWC) for a person with a disability in a low-resource context is very stressful, whether it is based on financial circumstances or the availability of medical solutions. The study's objective is to assess the EPWC options available on the market, focused on a set of conflicting criteria. In this research, three multi-criteria decision-making (MCDM) approaches are used to make decisions. ENTROPY method for weightage calculation of various parameters, COPRAS and EDAS methods for evaluating and ranking alternatives are applied. Both COPRAS and EDAS are applied separately for ranking of selected wheelchair models, and to check the robustness of the applied method, sensitivity analysis on cost criterion is carried out. The result shows that for both methods, EPWC-1 is the top priority model to buy, whereas EPWC-7 is the worst model for COPRAS, and EPWC-10 is the worst model for EDAS among the ten alternatives.

There are huge amounts of emissions associated with construction industry during its different stages from cradle till building demolition. This study presents a methodology that integrates multi-objective optimization and multi-criteria decision making (MCDM) in order to enable construction decision-makers to select the most sustainable construction alternatives. Four objectives functions are investigated, which are: construction time, lifecycle cost, environmental impact and primary energy in order to construct the Pareto front. A novel hybrid MCDM is designed based on seven multi-criteria decision making techniques to select the best solution among the set of the Pareto optimal solutions. Sensitivity analysis is performed in order to determine the most sensitive attribute and construction stages that influence environmental emissions. The analysis illustrates that WSM, COPRAS and TOPSIS provided the best rankings of the alternatives, primary energy is the most sensitive attribute for different MCDM methods. Moreover, PROMETHEE II is the most robust MCDM method.

The mobility sector including all kinds of transportation systems are facing global challenges in re-spect of green environmental issues. There has been a paradigm shift in the concept of design and manufacturing of automotive vehicles keeping in mind the scarcity of fossil fuel and the impact of emission on environment due to burning of it. The addition of hybrid and electric vehicles in pas-senger car segment has got significant momentum to address the global challenges. This research investigates the performance of a group of hybrid vehicles from customers’ perspective. Among the different brands that are available in the hybrid vehicle market, smart customers have given pri-ority to vehicle cost, mileage, tail pipe emission, comfortness and high tank size volume for long drive. Considering these attributes, selection strategy for hybrid vehicles has been developed using entropy based multi-attributive border approximation area comparison (MABAC) method. This research highlights the best hybrid vehicle which reduces air pollution in cities with other significant environmental benefits, reduces dependence on foreign energy imports and minimizes the annual fuel cost.

Education is the future. Education is the only way for a country to start developing and reducing poverty. In countries with medium incomes like Peru, the resources to spend on education is not unlimited. Therefore, it is necessary to have quality in investment. However, risks and uncertainty can make a project surpass its initial budget. Therefore, statistic based methods like Monte Carlo simulation is a powerful tool to forecast possible events that might endanger the profitability and sustainability of a project. Although there is not plenty of academic literature about Monte Carlo empirical usage, many projects employ this method to manage the possible risks the project could have. In consequence, the current research analyzed both risk and sensitivity of an elementary school building project. Both analyses showed that this project had huge probabilities to surpass the current profit and return estimations. However, the sensitivity analysis portrayed that the project could be endangered because of infrastructure overspending. Moreover, it indicated that students’ attendance is also a critical factor to ensure the sustainability of the project.

In this paper, the ranking performance of six most popular and easily comprehensive multi-criteria decision-making (MCDM) methods, i.e. weighted sum method (WSM), weighted product method (WPM), weighted aggregated sum product assessment (WASPAS) method, multi-objective optimization on the basis of ratio analysis and reference point approach (MOORA) method, and multiplicative form of MOORA method (MULTIMOORA) is investigated using two real time industrial robot selection problems. Both single dimensional and high dimensional weight sensitivity analyses are performed to study the effects of weight variations of the most important as well as the most critical criterion on the ranking stability of all the six considered MCDM methods. The identified local weight stability interval indicates the range of weights within which the rank of the best alternative remains unaltered, whereas, the global weight stability interval determines the range of weights within which the overall rank order of all the alternatives remains unaffected. It is observed that for both the problems, multiplicative form of MOORA is the most robust method being least affected by the changing weights of the most important and the most critical criteria.

The present paper studies the reliability analysis of the casting process in foundry work using a probabilistic approach. As foundry industries in many developing countries suffer from poor quality of casting due to improper management, lack of resources and wrong working methods followed, which results in the decrement of productivity. Hence, to ensure the quality and productivity, favorable steps must be taken. The considered casting system has four main types of defects; namely mold shift, shrinkage, cold shut and blowholes. The complete casting system can fail due to the misalignment of the mold and combination of defects such as shrinkage and blow holes and can also fail by defects of shrinkage, blow holes and cold shut, simultaneously. The system is analyzed with the help of the supplementary variable technique and Laplace transformation. The availability, reliability, mean time to failure, sensitivity analysis and cost-effectiveness have been evaluated for the considered system. The results have been shown with the help of graphs, which predicts the behavior of the casting process system when any one of the defect or more than one defect appears.

The primary purpose of this study is to measure the technical efficiency of 30 doctorate-granting universities, the universities or the higher education institutes with PhD training programs, in Vietnam, applying the sensitivity analysis of data envelopment analysis (DEA). The study uses eight sets of input-output specifications using the replacement as well as aggregation/disaggregation of variables. The measurement results allow us to examine the sensitivity of the efficiency of these universities with the sets of variables. The findings also show the impact of variables on their efficiency and its “sustainability”.

The construction process is inherently prone to risks. Risk management is an essential and integral part of project management on all construction projects. Risk analysis is one of the core components of risk management that enables professionals to quantify and analyze risks that may pose potential threats to project performance in terms of various parameters. This research was conducted to identify and analyze risks associated with residential construction in Bangalore. In this study, risk and its impact on time and cost was identified and analyzed. Schedule impacts of project risks were supplemented by conducting quantitative risk analysis such as Monte Carlo simulation and sensitivity analysis using the Primavera risk analysis software. In case of cost, the cost variance was found out and mitigation measures were given. Thus, by effectively managing the risks, organization has more timely, comprehensive and deeper understanding of risks which in turn facilitates better decision making and confidence to take on new ventures or even to accept higher level of risk.