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Purpose: This study aims to identify and accurately assess the risk factors of competitors' cooperation in the NPD project. Design/methodology/approach: New product development (NPD) is essential to the survival of companies and surpassing other competitors. A key prerequisite for the success of an NPD project is the timing of new product delivery to the market. The main challenge faced by many project managers is the delay in execution and completion phases due to the complex nature and uncertainty of these projects. Rival companies' cooperation reduces the time spent on an NPD project which is an excellent way to reduce the risk of losing the market, but it increases other risk factors. Findings: Based on the results, the security and confidentiality of innovation, the competitors attracting human resources and the company's brand credibility factors were ranked higher than other factors and should be predicted and managed before cooperating with competitors. Originality/value: This paper proposed a new model to assess risk factors in cooperation with rival companies in NPD projects. This model takes into account new parameters, for example, negative and positive risks, negative and positive passable risks and risk-based multi-objective optimization by ratio analysis plus full multiplicative form methodology for the rival companies cooperation in NPD projects. To evaluate the efficiency of the proposed model, a real case of the R&D unit of Iran Khodro Company was studied. [ABSTRACT FROM AUTHOR]

Abstract Multi-sensor plays an important role in monitoring the systems and equipment under consideration. The final integrated information obtained from these sensors is always associated with uncertainties and ambiguities, thus to eliminate these inaccuracies, various uncertainty theories, e.g., Dempster–Shafer theory (DST), have been proposed. In several models, it is optimistically assumed that the sensors always work properly, but in reality, they can fail like any other physical equipment. In the past decades, many researchers calculated the dynamic sensor reliability by comparing the information given by each sensor to those of other sensors. However, such methods are sometimes problematic because there are chances while the considered sensor is working properly, the others can fail. In order to address these problems, our proposed methods introduce a sensor risk factor in the sensor output to enhance the accuracy. In this paper, we present two pessimistic–optimistic models based on the interval-valued and fuzzy-valued DST to model this error factor. Finally, a numerical example is given to exemplify the application of the proposed models. Highlights • This research introduces and develops the concept of risk in multi-sensors fusion. • The concepts of pessimistic and optimistic risks are introduced for the sensors. • Two pessimistic–optimistic models are developed for multi-sensor fusion. • A fuzzy-valued evidence model is developed based on interval-valued DS theory. [ABSTRACT FROM AUTHOR]

Nowadays, a serious environmental challenge is the pollution emitted by combustion engine vehicles. Governments have found that replacing these with electric vehicles (EVs) is a viable solution for sustainable transportation. The sustainable operation of fast charging stations for EVs is a critical parameter in these vehicles' popularity. Herein, a propose a model for the sustainable evaluation of energy sources of charging stations based on the R-numbers and combined compromise solution method (R–COCOSO). The construction of EV charging stations is done based on new and developing technologies, and they are often expensive, complex, and high-risk. Thus, we developed the combinative distance-based assessment (CODAS) method based on R-numbers to assess the project risk. Moreover, a linear programming model was developed to assess the contractors' risk response and the optimal allocation of their workload based on R-numbers and the ε − constraint approach. A real case of building charging stations in Tehran, Iran, was presented to test the accuracy of the model. [ABSTRACT FROM AUTHOR]

In recent years, maintenance, especially its preventive type, has been seen as an effective and considerable factor in improving the functions of machines. Maintenance plays a vital role in organizations by keeping and increasing the reliability, accessibility, the quality of products, risk mitigation, return improvement, and safety. An effective maintenance programme can be realized by implementing a proper maintenance strategy. Therefore, maintenance and its strategies have a special status in industries. However, selecting a proper maintenance strategy has always been a complex process because maintenance is a nonrepetitive task. Likewise, the lack of failure records and constant changes in the conditions of machines makes it further complicated. Hence, the decision-making also depends on experts' opinions and because some kind of risk and uncertainty are always there in experts evaluations, the reliability of evaluations is questionable. The present study was aimed to develop a risk-based model grounded on the analytical hierarchical process, namely, RAHP, to meet this need of maintenance. A case study from a steel rolling company was considered to evaluate the effectiveness of this model. [ABSTRACT FROM AUTHOR]

Safe and economical solid and hazardous waste disposal is a serious global concern and challenge. Therefore, the production of new environmentally friendly products for safe waste disposal and its conversion into energy is a major need of governments and companies manufacturing waste management products. An R&D project mainly aims to present an innovation and solve a problem in the real world, and is thus very expensive and riddled with technical complexity and uncertainty. The accurate selection of the project and its risk assessment improve the project's success rate and limit the companies' costs. This article aims to review the important criteria for the accurate selection of new projects for safe municipal solid waste disposal and also to identify and evaluate the risk factors affecting these projects' performance. Herein, a new fuzzy risk-based decision model is presented to achieve the stated goals. This model utilizes MCDM methods and develops them based on R-numbers method which has recently been developed to model the risk of fuzzy numbers in dynamic environments. The proposed model is successfully applied in the Waste Management Organization of Tehran, Iran. The results reveal the superiority of the plasma pyrolysis system project over other projects, e.g., launching power plants based on landfill, plasma gasification, and waste pyrolysis system. The scores of the four technologies evaluated based on the R-TODIM method are1, 0.835, 0.789, and 0, respectively. The results of risk assessment of the plasma pyrolysis system also indicate the importance of risk factors, e.g., access to technology and laboratory equipment and new scientific resources, compared to other risk factors. The scores of the three mentioned risk factors are 4.864, 4.798, and 1.1, respectively. • We considered green NPD in Sustainable Municipal Waste Management. • A new model was developed to assess the risk factors of green NPD projects. • We compared waste-to-energy technologies from new aspects in developing countries. • R-TODIM and R-MULTIMOOSRAL methods were used to select waste-to-energy technologies. • Plasma pyrolysis was recognized as one of the best technologies for waste disposal. [ABSTRACT FROM AUTHOR]

Various unexpected, low-probability events can have short or long-term effects on organizations and the global economy. Hence there is a need for appropriate risk management practices within organizations to increase their readiness and resiliency, especially if an event may lead to a series of irreversible consequences. One of the main aspects of risk management is to analyze the levels of change and risk in critical variables which the organization's survival depends on. In these cases, an awareness of risks provides a practical plan for organizational managers to reduce/avoid them. Various risk analysis methods aim at analyzing the interactions of multiple risk factors within a specific problem. This paper develops a new method of variability and risk analysis, termed R.Graph, to examine the effects of a chain of possible risk factors on multiple variables. Additionally, different configurations of risk analysis are modeled, including acceptable risk, analysis of maximum and minimum risks, factor importance, and sensitivity analysis. This new method's effectiveness is evaluated via a practical analysis of the economic consequences of new Coronavirus in the electricity industry. [ABSTRACT FROM AUTHOR]