新产品设计前端的筛选与评价系统研究

本着重于新产品设计前端的筛选与评价系统研究。设计了若干准则，有社会环境准则、经济成本准则、技术准则、时间准则、价值准则等，对新产品构思进行筛选。运用模糊理论，确定隶属度函数，得到评价矩阵，最终得到综合评价；运用加权评分法，确定评判的各影响因素及其各自所占的权重，给每种构思进行评分，根据最后总得分的多少来评定构思的优劣。最后，阐述了评价系统在设计过程中的重要作用，评价系统贯穿于整个设计过程中。     

A modelling framework for comparative testing of energy forecasts

A project is described in which a methodology was developed for the comparative testing of medium-term national energy forecasts. The basis of the approach is a computer modelling system which can be used flexibly to investigate the dynamics of alternative assumptions about energy futures. The application of the methodology to the forecasts produced by a particular research group is discussed and the educative value of a modelling approach for the users in this case is explained. The study is intended as a contribution to the U.K. national debate on energy policy.     

The Assessment of Diurnal Storage Requirements for Gas Supply Systems

The British Gas Corporation is preparing for substantial capital expenditure to provide additional gas storage capacity, which is required to balance out the within-day variations of demand, Conversion to natural gas, with its higher calorific value, resulted in a substantial increase in the effective volume of gas storage available. This surplus has been eroded by the rapid growth in demand over the decade since conversion, and new investment in storage will be required if current forecasts of growth are correct. The assessment of the storage requirements has therefore become more important, and more sophisticated methods are being developed. In this paper the factors affecting the need for storage are examined, the methods adopted in incorporating these factors into a simulation model are discussed, and the results from the models are presented. Consideration is given to the national policy aspects of the problem and to the interactions between O.R. and other staff within B.G.C. during the development of the project.     

STOCHASTIC ENERGY DEMAND AND THE STABILIZATION VALUE OF ENERGY STORAGE

The economic value of energy storage to meet peak electricity demand is analyzed with an emphasis on the role of demand uncertainty. The concept of the stabilization value, which measures that part of the benefit of the storage project which is due solely to the stochastic demand components, is defined. The magnitude of the stabilization value, relative to the overall value of energy storage, is evaluated in terms of a simple model that accounts for the relevant characteristics of the electric power utility's production mix. It is found that neglecting the demand uncertainty can seriously bias the benefit assessment of the storage project as well as the determination of the optimal storage capacity.     

A simulation and evaluation of earned value metrics to forecast the project duration

In this paper, we extensively review and evaluate earned value (EV)-based methods to forecast the total project duration. EV systems have been set up to deal with the complex task of controlling and adjusting the baseline project schedule during execution, taking into account project scope, timed delivery and total project budget. Although EV systems have been proven to provide reliable estimates for the follow-up of cost performance within our project assumptions, they often fail to predict the total duration of the project. We present an extensive simulation study where we carefully control the level of uncertainty in the project, the influence of the project network structure on the accuracy of the forecasts and the time horizon where the EV-based measures provide accurate and reliable results. We assume a project setting where project activities and precedence relations are known in advance and do not consider fundamentally unforeseeable events and/or unknown interactions among various actions that might cause entirely unexpected effects in different project parts. This is the first study that investigates the potential of a recently developed method, the earned schedule method, which improves the connection between EV metrics and the project duration forecasts.     

Choosing realistic values of indifference,preference and veto thresholds for use with environmental criteria within ELECTRE

The ELECTRE III outranking model is particularly suited to aiding the choice between project alternatives on the basis of mainly environmental criteria. The model requires values of three criterion thresholds, the indifference threshold (q), the preference threshold (p) and the veto threshold (v). These allow the uncertainties inherent in the criteria valuations to be incorporated into the decision process. There is, at present, a high degree of subjectivity involved in determining these thresholds, which are expressed in terms of the error/uncertainty associated with the valuations of each of the criteria under scrutiny. If, however, the ELECTRE III outranking model is to be used within a formal environmental appraisal system, the thresholds which govern the outranking relationship of one project option over another must take account of the effect on human beings of the difference between any two criterion scores. The authors suggest a new method for applying the standard ELECTRE III model to decision-aid problems within the formal mechanism of environmental impact assessment. This involves a new, more comprehensive approach for specifying realistic limits for p, q and v, within the context of an environmental appraisal, where both criterion error/uncertainty and human sensitivity to differing levels of the criterion are taken into account. Threshold valuations for noise impacts from a highway project are used to illustrate the proposed method.     

A case study of a batch manufacturing plant simulation

This paper shows how a successful simulation model of a batch manufacturing plant was developed by an OR team working in conjunction with their clients. A decision was needed on the design of a multi-product, multi-stage, multi-item plant that would meet sales forecasts for the next five years. The background to the Plant and the project are discussed and the development of the simulation model is shown. The results from the model are displayed, together with other implementation issues. Major learning points from the study are highlighted.     

News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions

The macroeconomic climate influences operations with regard to, e.g., raw material prices, financing, supply chain utilization and demand quotas. In order to adapt to the economic environment, decision-makers across the public and private sectors require accurate forecasts of the economic outlook. Existing predictive frameworks base their forecasts primarily on time series analysis, as well as the judgments of experts. As a consequence, current approaches are often biased and prone to error. In order to reduce forecast errors, this paper presents an innovative methodology that extends lag variables with unstructured data in the form of financial news: (1) we apply a variety of models from machine learning to word counts as a high-dimensional input. However, this approach suffers from low interpretability and overfitting, motivating the following remedies. (2) We follow the intuition that the economic climate is driven by general sentiments and suggest a projection of words onto latent semantic structures as a means of feature engineering. (3) We propose a semantic path model, together with estimation technique based on regularization, in order to yield full interpretability of the forecasts. We demonstrate the predictive performance of our approach by utilizing 80,813 ad hoc announcements in order to make long-term forecasts of up to 24 months ahead regarding key macroeconomic indicators. Back-testing reveals a considerable reduction in forecast errors.     

Emergy analysis of a farm biogas project in China: A biophysical perspective of agricultural ecological engineering

This paper aims to present a biophysical understanding of the agricultural ecological engineering by emergy analysis for a farm biogas project in China as a representative case. Accounting for the resource inputs into and accumulation within the project, as well as the outputs to the social system, emergy analysis provides an empirical study in the biophysical dimension of the agricultural ecological engineering. Economic benefits and ecological economic benefits of the farm biogas project indicated by market value and emergy monetary value are discussed, respectively. Relative emergy-based indices such as renewability (R%), emergy yield ratio (EYR), environmental load ratio (ELR) and environmental sustainability index (ESI) are calculated to evaluate the environmental load and local sustainability of the concerned biogas project. The results show that the farm biogas project has more reliance on the local renewable resources input, less environmental pressure and higher sustainability compared with other typical agricultural systems. In addition, holistic evaluation and its policy implications for better operation and management of the biogas project are presented.     

On market value margins and cost of capital

The valuation of insurance liabilities plays a central role in the design of any solvency framework. We investigate the notion of “fair value of liabilities” at a conceptional level and compare several implementations which are currently discussed in the Solvency II project. Our focus is on the cost of capital approach based on market information. In particular, we discuss the applicability of arguments borrowed from financial mathematics.     

A Multi-attribute Value Model for the Study of UK Energy Policy

A multi-attribute value model has been developed to study UK energy-policy options. The model consists of four main stages: attribute selection, in which users identify up to 15 key attributes of an energy policy; attribute rating, in which a quantitative assessment is made of the relative importance of each of the attributes selected; option evaluation, in which users are asked to score five energy-policy options for the UK (drawn from a range of published sources) on each of the attributes; and experimentation, in which users are encouraged to explore and modify a tabular and graphical representation of the results. The model has been used in a series of workshops at which representatives from different organizations have been invited to use the model to explore each other's viewpoints. User reactions to the model are discussed, and the potential of such models for addressing controversial social issues is considered.     

Forecasting corporate failure using ensemble of self-organizing neural networks

For more than a decade, the number of research works that deal with ensemble methods applied to bankruptcy prediction has been increasing. Ensemble techniques present some characteristics that, in most situations, allow them to achieve better forecasts than those estimated with single models. However, the difference between the performance of an ensemble and that of its base classifier but also between that of ensembles themselves, is often low. This is the reason why we studied a way to design an ensemble method that might achieve better forecasts than those calculated with traditional ensembles. It relies on a quantification process of data that characterize the financial situation of a sample of companies using a set of self-organizing neural networks, where each network has two main characteristics: its size is randomly chosen and the variables used to estimate its weights are selected based on a criterion that ensures the fit between the structure of the network and the data used over the learning process. The results of our study show that this technique makes it possible to significantly reduce both the type I and type II errors that can be obtained with conventional methods.     

Optimal adjustment of manpower-requirement forecasts: A case study of Iran

The manpower forecasting models are frequently used in development planning. The usual approach in these models is to correlate the manpower requirement with the level of economic activities and to declare the forecasted figures as the educational targets of the development plan. In this paper we show that because of uncertainties involved in these forecasts, and due to the lack of a cost consideration mechanism in the above models, the implementation of such an educational plan causes an inefficiency in the society's allocation of resources. We then derive an adjustment rule which modifies the manpower-requirement forecasts based on balancing the trade-offs between the cost of educational and the degree of target realization.For demonstration purposes, we apply the above rule to the case of Iran and, through this application, we introduce a methodology for analyzing the sensitivity of results to different types of errors contained in the manpower-requirement forecasts.     

Complex economic dynamics: Chaotic saddle, crisis and intermittency

Complex economic dynamics is studied by a forced oscillator model of business cycles. The technique of numerical modeling is applied to characterize the fundamental properties of complex economic systems which exhibit multiscale and multistability behaviors, as well as coexistence of order and chaos. In particular, we focus on the dynamics and structure of unstable periodic orbits and chaotic saddles within a periodic window of the bifurcation diagram, at the onset of a saddle-node bifurcation and of an attractor merging crisis, and in the chaotic regions associated with type-I intermittency and crisis-induced intermittency, in non-linear economic cycles. Inside a periodic window, chaotic saddles are responsible for the transient motion preceding convergence to a periodic or a chaotic attractor. The links between chaotic saddles, crisis and intermittency in complex economic dynamics are discussed. We show that a chaotic attractor is composed of chaotic saddles and unstable periodic orbits located in the gap regions of chaotic saddles. Non-linear modeling of economic chaotic saddle, crisis and intermittency can improve our understanding of the dynamics of financial intermittency observed in stock market and foreign exchange market. Characterization of the complex dynamics of economic systems is a powerful tool for pattern recognition and forecasting of business and financial cycles, as well as for optimization of management strategy and decision technology.     

Purpose, function and types of mathematics assessment in China

This paper takes a historical perspective to review the development of assessment practice, in China, which is outlined with a focus on the purpose and function of different assessment practices. Specifically, civil examination in imperial China served the purpose of selection and had the functions of directing and propelling educational activities. Various examinations used in modern China have kept the similar purpose as civil examination but changed in other ways. Several other assessment methods developed in modern China have shown more promise for improving mathematics education than examinations, but further efforts are needed for their development. The value of reviewing Chinese assessment practice historically is then addressed in an international context. The accomplishment of this review itself suggests that focusing on the purpose and function of an assessment is a feasible approach for reviewing assessment practice in other educational systems.     

Quadrature Methods for Bayesian Optimal Design of Experiments With Nonnormal Prior Distributions

Many optimal experimental designs depend on one or more unknown model parameters. In such cases, it is common to use Bayesian optimal design procedures to seek designs that perform well over an entire prior distribution of the unknown model parameter(s). Generally, Bayesian optimal design procedures are viewed as computationally intensive. This is because they require numerical integration techniques to approximate the Bayesian optimality criterion at hand. The most common numerical integration technique involves pseudo Monte Carlo draws from the prior distribution(s). For a good approximation of the Bayesian optimality criterion, a large number of pseudo Monte Carlo draws is required. This results in long computation times. As an alternative to the pseudo Monte Carlo approach, we propose using computationally efficient Gaussian quadrature techniques. Since, for normal prior distributions, suitable quadrature techniques have already been used in the context of optimal experimental design, we focus on quadrature techniques for nonnormal prior distributions. Such prior distributions are appropriate for variance components, correlation coefficients, and any other parameters that are strictly positive or have upper and lower bounds. In this article, we demonstrate the added value of the quadrature techniques we advocate by means of the Bayesian D-optimality criterion in the context of split-plot experiments, but we want to stress that the techniques can be applied to other optimality criteria and other types of experimental designs as well. Supplementary materials for this article are available online.     

Optimization models and a GA-based algorithm for stochastic time-cost trade-off problem

In real-life projects, both the trade-off between the project cost and the project completion time, and the uncertainty of the environment are considerable aspects for decision-makers. However, the research on the time-cost trade-off problem seldom concerns stochastic environments. Besides, optimizing the expected value of the objective is the exclusive decision-making criterion in the existing models for the stochastic time-cost trade-off problem. In this paper, two newly developed alternative stochastic time-cost trade-off models are proposed, in which the philosophies of chance-constrained programming and dependent-chance programming are adopted for decision-making. In addition, a hybrid intelligent algorithm integrating stochastic simulations and genetic algorithm is designed to search the quasi-optimal schedules under different decision-making criteria. The goal of the paper is to reveal how to obtain the optimal balance of the project completion time and the project cost in stochastic environments.     

Comparing clusterings—an information based distance

This paper proposes an information theoretic criterion for comparing two partitions, or clusterings, of the same data set. The criterion, called variation of information (VI), measures the amount of information lost and gained in changing from clustering C to clustering C^′. The basic properties of VI are presented and discussed. We focus on two kinds of properties: (1) those that help one build intuition about the new criterion (in particular, it is shown the VI is a true metric on the space of clusterings), and (2) those that pertain to the comparability of VI values over different experimental conditions. As the latter properties have rarely been discussed explicitly before, other existing comparison criteria are also examined in their light. Finally we present the VI from an axiomatic point of view, showing that it is the only “sensible” criterion for comparing partitions that is both aligned to the lattice and convexely additive. As a consequence, we prove an impossibility result for comparing partitions: there is no criterion for comparing partitions that simultaneously satisfies the above two desirable properties and is bounded.     

Combining Economic Forecasts

It is well known that no particular forecasting agency dominates when the accuracy of economic forecasts of the UK is investigated. There are good reasons for believing that if forecasts differ, some combination of them will be an improvement over the individual forecasts. The problem is to determine what weights to attach to each forecast. Various methods have been suggested in the literature, including equal weights (averaging), optimal weights (linear regression), varying weights based on past performance, and the Bayesian approach. We review these methods and examine their performance for important macro-economic variables.