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We examine an option contract from a supplier’s perspective and apply mean-variance method to analyze the supplier’s risk. Compared with the newsvendor model without an option contract, we theoretically prove that the option contract can also benefit the supplier. We find for a given option exercise price, there exists an option price such that the contract with the option price dominates those with smaller option price in terms of mean variance of the supplier’s profit. Computational studies have also been conducted in the paper.

Nowadays, the online-to-offline (O2O) retailers provide on-demand delivery service for online orders by their own fleets and riders. An intelligent delivery network lays an important foundation to support cost-effective delivery service in the long run. Drones have great potential to revolutionize the instant delivery industry regarding cost and timeliness, while the hazard risks to humans and the environment should be seriously considered through sophisticated network design. In this paper, we propose a framework for a drone-driven intelligent delivery network design problem with the consideration of the multi-dimensional risk map, which needs to determine store location, drone fleet size and allocation, customer assignment, customer delivery mode selection, and delivery routing. A bi-objective non-linear programming model is formulated to maximize profit and minimize integrated risks as well. To tackle large instances, a modified NSGA-III algorithm is developed, which is incorporated with problem-specific search operators and Pareto local search to obtain Pareto solutions efficiently. Real-world data-based numerical experiments are conducted to verify the performance of the modified NSGA-III algorithm compared to the modified NSGA-II. A case study based on the geographical information in Shanghai is analyzed to validate the effectiveness of the proposed model. Moreover, sensitivity analysis is presented to evaluate the effects of multiple parameters on the drone delivery service network design. Some managerial insights are obtained for the O2O retailer who offers on-demand delivery service through online platform.

In many systems, randomness and uncertainty are present simultaneously. Uncertain random variables provide a tool to deal with these inexact phenomena. This paper proposes a concept of risk index to quantify the risk of an uncertain random system. In addition, a risk index theorem is proved in order to calculate the risk index, and is applied to series systems, parallel systems and standby systems. Finally, a concept of expected loss is suggested for an uncertain random system.

Straightforward methods to evaluate risks arising from several sources are specially difficult when risk components are dependent and, even more if that dependence is strong in the tails. We give an explicit analytical expression for the probability distribution of the sum of non-negative losses that are tail-dependent. Our model allows dependence in the extremes of the marginal beta distributions. The proposed model is flexible in the choice of the parameters in the marginal distribution. The estimation using the method of moments is possible and the calculation of risk measures is easily done with a Monte Carlo approach. An illustration on data for insurance losses is presented.

In this paper we discuss the scientific basis of risk analysis, when a Bayesian approach is the foundation of the analysis. We argue that the analysis cannot be judged by reference to the traditional science paradigms alone, such as the natural sciences, social sciences, mathematics and probability theory. There is a need for recognitions of a risk analysis science which is related to the establishment of principles, methods and models to analysis, describe and communicate risk, in a decision-making context. The "goodness" of these principles, methods and models cannot be evaluated by reference to the accuracy in describing the world, as risk analysis is a tool for expressing and communicating uncertainty about the world. Empirical control is only relevant to some degree. An example of a risk analysis of an offshore installation is used to illustrate important issues.

This paper presents a new method for group decision-making to evaluate the rate of aggregative risk in a fuzzy environment using fuzzy sets theory during any phase of the life cycle. The proposed algorithm is easier and more efficient than the existing methods.

This paper presents the multi-based experts Failure Effects Analysis (FEA). The experts' opinions differ substantially because the experts do not often agree on the level of the failure factors (failure probability, non-detection probability, severity of effect, and expected cost) and the functions/subsystems attributes (e.g., importance). Therefore, conflict always occurs in Group-based Failure Effects Analysis (GFEA). The approach uses fuzzy Risk Priority Category (RPC) and group decision-making techniques to study both the failure effects on the functions/subsystems and the failure risk category with uncertain information. In addition, the approach uses the compensated operators to allow the tradeoffs either among failure factors or among functions/subsystems attributes. A solved example is presented to demonstrate the Group-based Failure Effects Analysis (GFEA) application.

The non-invasive Blood Pressure Estimation (BPE) utilizing the technology of photoplethysmography (PPG) gains significant interest because PPG could be extensively employed to wearable sensors. Here, a method for estimating Systolic Blood pressure (SBP), as well as Diastolic Blood pressure (DBP), grounded only on a PPG signal utilizing the Image Denoising Algorithms (IDA) algorithms is proposed. Also, a classification methodology to execute the risk analysis (RA) of the BP patients utilizing Moore–Penrose Pseudo-Inverse Matrix-Deep Learning Neural Network (MPPIW-DLNN) is proposed. The preprocessing is then done on the input PPG signal utilizing the Modified–Chebyshev Filter (CF) to eradicate the unwanted information existent in the signal. Afterward, the BPE is done utilizing IDA, which categorizes those components into (i) SBP and (ii) DBP. The MPPIW-DLNN provides the results of four sorts of risk classes like (i) stroke, (ii) heart failure (HF), (iii) heart attack (HA), and (iv) aneurysm identified from the inputted PPG signal.

Stroke is an acute cerebrovascular disease characterized by a high incidence, disability rate, recurrence rate, and mortality rate. Depression, as one of the main types of mood disorders, can manifest as symptoms such as a loss of interest in daily activities, reduced energy, diminished self-esteem, insomnia, and decreased appetite. There exists a significant relationship between these two conditions in terms of their pathogenesis. To delve deeper into the factors associated with the occurrence of both diseases, particularly examining the influence of depression on stroke risk, the authors conducted a large-scale investigation and data collection in the population of Shanghai, obtained 5599 valid data points, including information on hypertension, abnormal blood lipid levels, diabetes, atrial fibrillation, body mass index (BMI), a history of stroke, family history of stroke, previous transient ischemic attacks, smoking history, and exercise habits. Ultimately, the authors employed the decision tree C4.5 algorithm to construct a risk analysis model for both stroke and depression, analyzing the risk factors contributing to their occurrence and inferring the correlation between depression and stroke. The results revealed a close association between depression and a history of stroke, BMI, gender, and physical activity. Additionally, in the nonelderly population, psychological factors such as anxiety, depression, mood disorders, and stress were found to be closely linked to stroke onset. In many instances, regular exercise may mitigate the adverse effects of depression on stroke risk.

The main objective of the paper is to investigate the risk analysis problems when a precise but unknown probability distribution of states of nature belongs to a set of continuous probability distributions restricted by some known lower and upper distributions and when utility functions are nonmonotone. Methods for choosing "optimal" distributions among the set of distributions and for computing the expected utilities are proposed. Some special cases of sets of distributions, including possibility distributions, step functions, belief functions are studied under the same conditions. Various numerical examples illustrate the proposed methods.

To evaluate whether the investments of e-logistics systems may increase financial risks, models of Bayesian networks are constructed in this study with the mechanism of structural learning and parameter learning. Empirical findings from the transport and logistics sectors suggest that the e-logistics investments generally do not increase the financial risks of companies except the implementation of computer aided picking systems and radio frequency identification. Meanwhile, only the investment of enterprise resource planning can reduce the financial risks and enhance the profitability at the same time. Generally speaking, most advanced e-logistics investments do not yield financial advantages for the transport and logistics companies from the perspective of Bayesian inference. Empirical study based on the proposed models also demonstrates the practicability of Bayesian models.

Risk matrices are adopted and recommended by many organizations, but the way they are usually constructed violates some basic theoretical principles, giving rise to inconsistent risk ratings. This paper studies ways in which multiple criteria and portfolio decision analyses can improve the design and deployment of risk matrices, using MACBETH (the “Measuring Attractiveness by a Categorical Based Evaluation TecHnique”). Firstly, it introduces ‘value risk-matrices’, built with MACBETH in the following modeling steps: (1) building a value measurement scale on each impact dimension and constructing a subjective probability scale, (2) additive aggregation of the value scales into a cumulative value scale, and (3) design of the value risk-matrix. The value and probability scores of risks are plotted in the matrix and its analysis informs the identification of mitigation actions, which can then be prioritized making use of the recent portfolio module of the MACBETH decision support system. Taken all together, the paper sketches a new modeling approach for Improving Risk Matrice s (IRIS).

Projects are, by definition, risky and uncertain ventures. Therefore, the performance and risk of major projects should be carefully controlled in order to increase their probability of success. Quantitative project control techniques assist project managers in detecting problems, thus responding to them early on, by comparing the baseline plan with the project progress. However, project risk and uncertainty are rarely considered by these techniques. This paper proposes a project control framework that integrates the project uncertainty and associated risk factors into project control. Our framework is based on earned value management (EVM), which is an effective and widely used quantitative project control technique. The framework uses hybrid Bayesian Networks (BNs) to enhance EVM with the ability to compute the uncertainty associated with its parameters and risk factors. The framework can be applied to projects from different domains, and we illustrate its use with a simple example and a case study of a construction project.

Risk assessment analysis for investment decisions largely depends on expert judgment using traditional approaches and is lacking in considering investors’ different preferences and limitations. This paper proposes an adaptive personalized property investment risk analysis (APPIRA) method to identify the property investment determinants using a data-driven and personalized approach to weight the risk factors using the multicriteria decision model for optimal solutions. Result for predictive modeling using value prediction technique that measures the median house price depicts that the best method used was nonseasonal ARIMA. Furthermore, classification technique indicates that in each of the three selected suburbs, different property characteristics determined the rental properties desirable. As shown in result, for the investors who plan to invest in property for rental purposes, they need to choose townhouse type or property to make it rentable while for Vaucluse, terrace houses. These results can be applied into practice and will benefit the property industry directly.

The purpose of this paper is to study how Jordanian companies use the knowledge process to support risk analysis and how they deal with and foster it. The present empirical study is based on a sample of the data collected from 180 respondents, drawn randomly from Jordanian Telecommunication Companies. It provides a contribution to the literature about knowledge-based risk analysis in one of the developing countries as a framework to keep organisations competitive within the global business environment.

This paper makes the following contributions. First, it demonstrates that according to project stakeholders, individual risk management activities, like for instance risk identification, are able to contribute to project success.

Second, this paper provides insight in how knowledge process to support risk analysis and how they deal with and foster risk management activities to contribute in IT project success.

And third, based on this new perspective, this paper provides new directions for further research into the mechanisms on how knowledge process support risk analysis in risk management to contribute in IT project success.

The results of the survey show that the four selected factors (knowledge-based risk rationalise, knowledge-based risk comprehension, knowledge-based risk examination, and knowledge-based risk validation) have a significant impact on risk analysis.

Due to the literature limitation about the KM model in developing countries, the current study will contribute to this field by addressing the knowledge-based risk. The findings will certainly help both researchers and practitioners in future knowledge management (KM) process, and risk analysis research. In order to get a better understanding of the knowledge processes on risk analysis, future research endeavours should focus on several other countries for comparative purposes.

This paper describes an integrated spatial modeling concept for flood losses which has been developed within the joint research project "XtremRisK". For the final step of an integrated coastal flood risk analysis based on the "risk source-pathway-receptor" approach, the "Cellbased Risk Assessment" (CRA) concept is implemented for the spatial modeling of both tangible and intangible flood losses and their aggregation into the so-called "integrated risk". Finally, all results are utilized for the hazard and risk mapping, which serve as a basis for the decision making on risk management strategies. The different steps of the CRA concept and its applicability for different types of spatial input data are shown in the paper. Furthermore, advantages and limitations of this spatial modeling concept for integrated flood risk analysis are discussed. The practical implementation of the approach is described for the study area Hamburg-Wilhelmsburg (Germany) and the related categories of flood losses. The results show that the newly developed CRA concept is a suitable spatial modeling framework with respect to the comprehensive requirements in this integrated coastal flood risk analysis.

The 2011 Tōhoku event showed the massive destruction potential of tsunamis. The Euro-Japan “Risk assessment and design of prevention structures for enhanced tsunami disaster resilience (RAPSODI)” project aimed at using data from the event to evaluate tsunami mitigation strategies and to validate a framework for a quantitative tsunami mortality risk analysis. Coastal structures and mitigation strategies against tsunamis in Europe and Japan are compared. Failure mechanisms of coastal protection structures exposed to tsunamis are discussed based on field data. Knowledge gaps on failure modes of different structures under different tsunami loading conditions are identified. Results of the wave-flume laboratory experiments on rubble mound breakwaters are used to assess their resilience against tsunami impact. For the risk analysis, high-resolution digital elevation data are applied for the inundation modeling. The hazard is represented by the maximum flow depth, the exposure is described by the location of the population, and the mortality is a function of flow depth and building vulnerability. A thorough search for appropriate data on the 2011 Tōhoku tsunami was performed. The results of the 2011 Tōhoku tsunami mortality hindcast for the city of Ishinomaki substantiate that the tsunami mortality risk model can help to identify high-mortality risk areas and the main risk drivers.

This article attempts to adapt the Monte Carlo method to the quantitative risk management of environmental pollution. In this context, the feasibility of stochastic models to quantitatively evaluate the risk of chemical pollution is first discussed and then linked to a case study in which Monte Carlo simulations are applied. The objective of the case study is to develop a Monte Carlo scheme for evaluating the pollution in a lake environment. It is shown that the results can be of interest as they define the risk margins that are important to the sustainability of the ecosystem in general, and human health in particular. Moreover, assessing the environmental pollution with the help of the Monte Carlo method can be feasible and serve the purpose of investigating and controlling the environmental pollution, in the long and short terms.

Brazil is the second largest producer of genetically modified crops (GM crops) and the National Technical Commission on Biosafety (CTNBio) the decision making agency on this matter. The country uses Risk Analysis (RA) and project EIA as tools for biosafety evaluation. This paper aims to review the appropriateness of these tools for evaluating the environmental impacts of GM crops, also considering institutional aspects. An overview of the process of release of GM crops in Brazil along with important operational and institutional aspects is provided. The results indicate that project EIA could be applied to GM crops in specific sites and RA could give support to the evaluation of GM crop itself. Regarding institutional issues, it is concluded that decisions should be made by the environmental bodies, and not by the CTNBio.

Sustainability assessment at a national scale is a complex task that ought to be seen with a range of conflicting indicators and multicriteria analysis (MCA) is the best approach that can address these conflicts. The study aims for a comprehensive sustainability assessment of renewable energy (RE) technologies in India based on MCA and examines the impact of associated social and environmental risks on the overall sustainability ranking. Large hydropower is evaluated as the most sustainable RE technology in context to India under selected indicators. Contrary to this, large hydropower has also been identified with the highest social and environmental risks. Therefore, in a developing country like India, the techno-economic advantages of large hydropower with its significant untapped potential cannot be overlooked. Hence, there arises a need to enhance the environmental impact assessment (EIA) process. Thus, the study recommends a transparent process for public participation in EIA for comprehensive and effective social and environmental risk management of the project to achieve the goal of sustainable development.