Venture planning and analysis for large-scale energy technology developments : A policy perspective

Venture analysis of a major energy R&;D program involves a process whose objective is to provide insights into the policy question: “Should the government be in this particular energy-technology R&;D business?” It is critical to recognize the broader policy formulation and analysis process within which venture analysis seeks to provide insight. Although several venture evaluation approaches have been suggested which incorporate multiattribute decision criteria, they suffer from either: a) the weakness of collapsing the various dimensionally incompatible decision criteria (monetary, environmental, energy, etc.) into a single index or numeraire; or b) they fail to take decision criteria other than energy and dollars into account at all. Both approaches are inadequate for affective venture analysis.The “portfolio problem” of multiple competing/interacting energy-technology options at the policy-decision level suggests that venture analysis results for specific technologies should be provided with complete assessments of various relevant decision criteria in their original (and most appropriate) metrics. Costs and benefits measured in dollars, ergs, environmental damage units, plant safety units, and so on should not be collapsed into a single ranking or evaluation criterion at the venture analysis stage. To do so will totally muddle and confound the higher-level portfolio policy-decision process which compares the effects of multiple energy-technology developments and alternative wealth distribution and implementation approaches.It should be noted, moreover, that while this paper has focused on the specific policy-level issues of R&;D venture analysis for energy technologies, it appears that this methodology may be appropriate for a variety of public-policy decisions. Certainly most of the notions presented could be usefully applied to other public sector R&;D efforts. Additionally, however, many of the concepts apply to non-R&;D policy decisions where the emphasis is on the selection of a unified, time-phased program from a range of possible alternatives.     

Identification of safety and security critical systems and activities

There are many initiatives taken to identify safety and security critical systems and activities, at different levels and in different contexts, ranging from infrastructures at the societal level to equipment on the production plant level. Different approaches are implemented to define the critical systems and activities. Some of these relate to vulnerabilities, others incorporate the probability dimension and are risk based. We also see approaches taking into account values of the decision-maker and relevant stakeholders. In this paper, we discuss the rationale for these approaches. Is vulnerability an adequate measure to be used as a basis for determining criticality? Is it meaningful to specify safety and security critical systems and activities without addressing risk? How should we take into account the limitations of the risk assessments? Should we extend the concept of criticality to also cover utility aspects? We bring new insights into the discussion by being precise on the key risk concepts—including uncertainty, probability and expected value—and considering alternative risk perspectives. A novel approach is suggested based on expected values and uncertainties in underlying phenomena and processes. Our main concern is activities with potential severe consequences and large uncertainties.     

Economically optimal timing for crop disease control under uncertainty: an options approach

Severe large-scale disease and pest infestations in agricultural regions can cause significant economic damage. Understanding if and when disease control measures should be taken in the presence of risk and uncertainty is a key issue. We develop a framework to examine the economically optimal timing of treatment. The decision to treat should only be undertaken when the benefits exceed the costs by a certain amount and not if they are merely equal to or greater than the costs as standard net-present-value (NPV) analysis suggests. This criterion leads to a reduction in fungicide use. We investigate the effect of the model for disease progress on the value required for immediate treatment by comparing two standard models for disease increase (exponential and logistic growth). Analyses show that the threshold value of benefits required for immediate release of treatment varies significantly with the relative duration of the agricultural season, the intrinsic rate of increase of the disease and the level of uncertainty in disease progression. In comparing the performance of the delay strategy introduced here with the conventional NPV approach, we show how the degree of uncertainty affects the benefits of delaying control.     

Selective critique of risk assessments with recommendations for improving methodology and practise

Risk assessments are often criticised for defending activities that could harm the environment and human health. The risk assessments produce numbers which are used to prove that the risk associated with the activity is acceptable. In this way, risk assessments seem to be a tool generally serving business. Government agencies have based their regulations on the use of risk assessment and the prevailing practise is supported by the regulations. In this paper, we look more closely into this critique. Are risk assessments being misused or are risk assessments simply not a suitable tool for guiding decision-making in the face of risks and uncertainties? Is the use of risk assessments not servicing public interests? We argue that risk assessments may provide useful decision support but the quality of the risk assessments and the associated risk assessment processes need to be improved. In this paper, three main improvement areas (success factors) are identified and discussed: (1) the scientific basis of the risk assessments needs to be strengthened, (2) the risk assessments need to provide a much broader risk picture than what is typically the case today. Separate uncertainty analyses should be carried out, extending the traditional probabilistic-based analyses and (3) the cautionary and precautionary principles need to be seen as rational risk management approaches, and their application would, to a large extent, be based on risk and uncertainty assessments.     

An adaptive degradation‐based maintenance model taking into account both imperfect adjustments and AGAN replacements

This paper presents an adaptive maintenance model for equipment that can be adjusted (minor preventive maintenance, imperfect state) or replaced (major preventive maintenance, as good as new) at specific scheduled times based on degradation measurements. An initial reliability law that uses a degradation‐based model is built from the collection of hitting times of a failure threshold. Inspections are performed to update the reliability, the remaining useful life, and the optimum time for preventive maintenance. The case of both as good as new replacements and imperfect adjustments is considered. The proposed maintenance model is based on the optimization of the long‐term expected cost per unit of time. The model is then tested on a numerical case study to assess its effectiveness. This results in an improvement for the occurrences of maintenance tasks that minimizes the mean cost per unit of time as well as an optimized number of adjustments that can be considered before replacing an item. The practical application is a decision aid support to answer the 2 following questions: Should we intervene now or wait for the next inspection? For each intervention, should we adjust or replace the item of equipment? The originality is the presence of 2 criteria that help the maintainer to decide to postpone or not the preventive replacement time depending on the measured degradation and to decide whether the item should be adjusted or replaced.     

The use of random-fuzzy variables for the implementation of decision rules in the presence of measurement uncertainty

The practical, everyday final applications of measurement processes are mostly aimed at making a decision, on the basis of a comparison between the measured value and a reference value. If uncertainty in measurement is considered, this comparison must be performed between an interval of confidence (the measurement result) and a scalar quantity (the reference value). The result of such a comparison is quite often not univocal, so that making a decision may become quite troublesome. This paper shows how the use of the random-fuzzy variables in the expression of uncertainty in measurement allows the implementation of simple decision rules capable of taking into account the measurement uncertainty correctly. The proposed decision rules are applied to measurement procedures based on measurement algorithms that contain if ... then ... else structures where the if condition is applied to intermediate measurement results. An example of implementation of these decision rules is reported and discussed.     

The Effectiveness of the Expected Value-Variance-Skewness Criterion in the Multi-Stage Capital Rationing Problem

It has been suggested that the skewness of the probability distribution of the net present value of an investment should be a consideration in judging its desirability. This paper presents an index, referred to as the Expected Value - Variance - Skewness criterion, that incorporates skewness of net present value with its expected value and variance as a measure of investment worth under risk. The effectiveness of this criterion was compared to the expected value and the expected value-variance decision criteria by applying each criterion in a long sequence of investment projects.     

Incorporating risk measures in closed-loop supply chain network design

This paper considers a location-allocation problem in a closed-loop supply chain (CLSC) with two extensions: first, demand and prices of new and return products are regarded as non-deterministic parameters and second, the objective function is developed from expected profit to three types of mean-risk ones. Indeed, design and planning an integrated CLSC in real-world volatile markets is an important and necessary issue. Further, risk-neutral approaches, which are considered expected values, are not efficient for such uncertain conditions. Hence, this paper, copes with the design and planning problem of a CLSC in a two-stage stochastic structure. Besides, risk criteria are considered through using three types of popular and well-behaved risk measures: mean absolute deviation, value at risk and conditional value at risk (CVaR). Consequently, three types of mean-risk models are developed as objective functions and decision-making procedures are undertaken based on the expected values and risk adversity criteria. Finally, performances of the developed mean-risk models are evaluated in various aspects. Results reveal that the inefficiencies of risk-neutral approaches can be overcome. In addition, in terms of quality of solutions, the acceptability of CVaR is proved when it is compared to other risk measures.     

Quality by design compliant analytical method validation

The concept of quality by design (QbD) has recently been adopted for the development of pharmaceutical processes to ensure a predefined product quality. Focus on applying the QbD concept to analytical methods has increased as it is fully integrated within pharmaceutical processes and especially in the process control strategy. In addition, there is the need to switch from the traditional checklist implementation of method validation requirements to a method validation approach that should provide a high level of assurance of method reliability in order to adequately measure the critical quality attributes (CQAs) of the drug product. The intended purpose of analytical methods is directly related to the final decision that will be made with the results generated by these methods under study. The final aim for quantitative impurity assays is to correctly declare a substance or a product as compliant with respect to the corresponding product specifications. For content assays, the aim is similar: making the correct decision about product compliance with respect to their specification limits. It is for these reasons that the fitness of these methods should be defined, as they are key elements of the analytical target profile (ATP). Therefore, validation criteria, corresponding acceptance limits, and method validation decision approaches should be settled in accordance with the final use of these analytical procedures. This work proposes a general methodology to achieve this in order to align method validation within the QbD framework and philosophy. β-Expectation tolerance intervals are implemented to decide about the validity of analytical methods. The proposed methodology is also applied to the validation of analytical procedures dedicated to the quantification of impurities or active product ingredients (API) in drug substances or drug products, and its applicability is illustrated with two case studies.     

Combining the cost of reducing uncertainty with the selection of risk assessment models for remediation decision of site contamination

The decision as to whether a contaminated site poses a threat to human health and should be cleaned up relies increasingly upon the use of risk assessment models. However, the more sophisticated risk assessment models become, the greater the concern with the uncertainty in, and thus the credibility of, risk assessment. In particular, when there are several equally plausible models, decision makers are confused by model uncertainty and perplexed as to which model should be chosen for making decisions objectively. When the correctness of different models is not easily judged after objective analysis has been conducted, the cost incurred during the processes of risk assessment has to be considered in order to make an efficient decision. In order to support an efficient and objective remediation decision, this study develops a methodology to cost the least required reduction of uncertainty and to use the cost measure in the selection of candidate models. The focus is on identifying the efforts involved in reducing the input uncertainty to the point at which the uncertainty would not hinder the decision in each equally plausible model. First, this methodology combines a nested Monte Carlo simulation, rank correlation coefficients, and explicit decision criteria to identify key uncertain inputs that would influence the decision in order to reduce input uncertainty. This methodology then calculates the cost of required reduction of input uncertainty in each model by convergence ratio, which measures the needed convergence level of each key input's spread. Finally, the most appropriate model can be selected based on the convergence ratio and cost. A case of a contaminated site is used to demonstrate the methodology.     

考虑风险偏好的定价决策与需求信息共享价值分析

为了分析零售商的风险偏好对定价决策及信息共享价值的影响,以期望利润与条件风险估值的加权平均作为目标准则来刻画零售商的决策目标函数,构建供应链的需求信息共享决策模型,通过该模型,深入地分析和研究零售商不同风险偏好下供应链的最优定价决策以及需求信息共享的价值。研究表明,信息不共享情况下最优批发价与λ无关;当市场不确定信息显示需求增加时,最优批发价、最优零售价、零售商信息共享价值随着λ的增大而增大,而供应商和供应链的信息共享价值随着λ的增大而减小;而当市场不确定信息显示需求减少时,最优批发价、最优零售价、零售商信息共享价值随着λ的增大而减小,而供应商和供应链的信息共享价值随着λ的增大而增大。     

Comparison of evidence theory and Bayesian theory for uncertainty modeling

This paper compares Evidence Theory (ET) and Bayesian Theory (BT) for uncertainty modeling and decision under uncertainty, when the evidence about uncertainty is imprecise. The basic concepts of ET and BT are introduced and the ways these theories model uncertainties, propagate them through systems and assess the safety of these systems are presented. ET and BT approaches are demonstrated and compared on challenge problems involving an algebraic function whose input variables are uncertain. The evidence about the input variables consists of intervals provided by experts. It is recommended that a decision-maker compute both the Bayesian probabilities of the outcomes of alternative actions and their plausibility and belief measures when evidence about uncertainty is imprecise, because this helps assess the importance of imprecision and the value of additional information. Finally, the paper presents and demonstrates a method for testing approaches for decision under uncertainty in terms of their effectiveness in making decisions.     

Managing uncertainty in multiple-criteria decision making related to sustainability assessment

In real life, decisions are usually made by comparing different options with respect to several, often conflicting criteria. This requires subjective judgements on the importance of different criteria by DMs and increases uncertainty in decision making. This article demonstrates how uncertainty can be handled in multi-criteria decision situations using Compromise Programming, one of the Multi-criteria Decision Analysis (MCDA) techniques. Uncertainty is characterised using a probabilistic approach and propagated using a Monte Carlo simulation technique. The methodological approach is illustrated on a case study which compares the sustainability of two options for electricity generation: coal versus biomass. Different models have been used to quantify their sustainability performance for a number of economic, environmental and social criteria. Three cases are considered with respect to uncertainty: (1) no uncertainty, (2) uncertainty in data/models and (3) uncertainty in models and decision-makers’ preferences. The results shows how characterising and propagating uncertainty can help increase the effectiveness of multi-criteria decision making processes and lead to more informed decision.     

Multicriteria analysis based on constructing payoff matrices and applying methods of decision making in?fuzzy environment

There exist two classes of problems, which need the use of a multicriteria approach: problems whose solution consequences cannot be estimated with a single criterion and problems that, initially, may require a single criterion or several criteria, but their unique solutions are unachievable, due to decision uncertainty regions, which can be contracted using additional criteria. According to this, two classes of models (??X,M?? and ??X,R?? models) can be constructed. Analysis of ??X,M?? and ??X,R?? models (based on applying the Bellman-Zadeh approach to decision making in a fuzzy environment and using fuzzy preference modeling techniques, respectively) serves as parts of a general scheme for multicriteria decision making under information uncertainty. This scheme is also associated with a generalization of the classic approach to considering the uncertainty of information (based on analyzing payoff matrices constructed for different combinations of solution alternatives and states of nature) in monocriteria decision making to multicriteria problems. The paper results are of a universal character and are illustrated by an example.     

Risk control for staff planning in e-commerce warehouses

Internet sale supply chains often need to fulfil quickly small orders for many customers. The resulting high demand and planning uncertainties pose new challenges for e-commerce warehouse operations. Here, we develop a decision support tool to assist managers in selecting appropriate risk policies and making staff planning decisions in uncertain conditions. Multistage stochastic modelling has been used to analyse risk optimisation approaches and expected value-based optimisation. Exhaustive numerical and practical validations have been performed to test the tool’s applicability. We demonstrate, using a Dutch e-commerce warehouse, that the multi-period conditional value at risk appears to be most applicable.     

A real options approach to project management

When scheduling an uncertain project, project management can wait for additional (future) information to serve as the basis for rescheduling the project. This flexibility enhances the project's value by improving its upside potential while limiting downside losses relative to the initial expectations. Using traditional techniques such as net present value (NPV) or decision tree analysis (DTA) can often lead to misleading results. Instead, a real options analysis should be preferred. The potentials of a real options approach to project management are discussed with an example and future research directions are highlighted.     

Survey of methods to visualize alternatives in multiple criteria decision making problems

When solving decision problems where multiple conflicting criteria are to be considered simultaneously, decision makers must compare several different alternatives and select the most preferred one. The task of comparing multidimensional vectors is very demanding for the decision maker without any support. Different graphical visualization tools can be used to support and help the decision maker in understanding similarities and differences between the alternatives and graphical illustration is a very important part of decision support systems that are used in solving multiple criteria decision making problems. The visualization task is by no means trivial because, on the one hand, the graphics must be easy to comprehend and not too much information should be lost but, on the other hand, no extra unintentional information should be included. In this paper, we survey and analyze different ways of visualizing a small set of discrete alternatives graphically in the context of multiple criteria decision making. Some of the ways discussed are widely used and some others deserve to be brought into a wider awareness. This survey provides a starting point for all those who deal with multiple criteria decision making problems and need information of what kind of visualization techniques could be put to use in order to support the decision maker better.     

Messunsicherheit in der Werkstoffprüfung

Measurement uncertainty in testing of materials For the testing of materials the respective uncertainty has to be indicated for each measured value. From the 1995 published GUM [1] 2000 the Uncert report for different measuring methods was developed and published as Code of Practice (COP) [2–4]. These are to be represented in connection with the procedures recommended in the standards and the practical implementation.     

Material screening and choosing methods – A review

The selection of a material for a specific engineering purpose is a lengthy and expensive process. Approximately always more than one material is suitable for an engineering application, and the final selection is a compromise that brings some advantages as well as disadvantages. One of the issues that emerges from this review is that regardless of the relation of design stages and process selection with material selection, screening and ranking are two vital steps in the material selection. A variety of quantitative selection procedures have been developed to solve this issue, so that a systematic evaluation can be made. This paper seeks to address the following questions: (1) what is the contribution of the literature in the field of screening and choosing the materials? (2) What are the methodologies/systems/tools for material selection of engineering components? (3) Which approaches were prevalently applied? (4) Is there any inadequacy of the approaches? This research not only provides evidence that the multi-criteria decision making approaches has the potential to greatly improve the material selection methodology, but also aids the researchers and decision makers in applying the approaches effectively.