Maximal covering location problem with price decision for revenue maximization in a competitive environment

In this article, we extend the classical maximal covering model in a competitive environment by including a price decision. We formulate a revenue maximization model and propose two procedures to solve it. By a careful examination of the relationships between the maximal covering problems for different prices, we reveal interesting properties of the deduced revenue maximization model, leading to a full enumeration solution approach. With the help of two more properties we develop a second, more intelligent solution procedure. Computational experiments show promising results for a small, medium and large case study.     

建筑产品计价过程中应该注意的问题及对策

指出当计价的前提条件完全相同时，工程量清单计价编制的标底价与定额计价确定的工程造价应大体相等，强调了两种计价方法中各费用项目组成内容的一致性，及各项费用的计费基础和费率规定的统一性，以解决建筑产品计价中存在的问题。     

KNX 剑指一统 俯视群雄

分析了KNX系统与其他系统的区别，指出标准化和互联化给市场带来的便捷和竞争力，并简要介绍了KNX的网络、配置、通讯介质的架构。     

Confidence-based classifier design

In this paper, a new classifier design methodology, confidence-based classifier design, is proposed to design classifiers with controlled confidence. This methodology is under the guidance of two optimal classification theories, a new classification theory for designing optimal classifiers with controlled error rates and the C.K. Chow's optimal classification theory for designing optimal classifiers with controlled conditional error. The new methodology also takes advantage of the current well-developed classifier's probability preserving and ordering properties. It calibrates the output scores of current classifiers to the conditional error or error rates. Thus, it can either classify input samples or reject them according to the output scores of classifiers. It can achieve some reasonable performance even though it is not an optimal solution. An example is presented to implement the new methodology using support vector machines (SVMs). The empirical cumulative density function method is used to estimate error rates from the output scores of a trained SVM. Furthermore, a new dynamic bin width allocation method is proposed to estimate sample conditional error and this method adapts to the underlying probabilities. The experimental results clearly demonstrate the efficacy of the suggested classifier design methodology.     

Intelligent agents in electronic markets for information goods: customization, preference revelation and pricing

Electronic commerce has enabled the use of intelligent agent technologies that can evaluate buyers, customize products, and price in real-time. Our model of an electronic market with customizable products analyzes the pricing, profitability and welfare implications of agent-based technologies that price dynamically based on product preference information revealed by consumers. We find that in making the trade-off between better prices and better customization, consumers invariably choose less-than-ideal products. Furthermore, this trade-off has a higher impact on buyers on the higher end of the market and causes a transfer of consumer surplus towards buyers with a lower willingness to pay. As buyers adjust their product choices in response to better demand agent technologies, seller revenues decrease since the gains from better buyer information are dominated by the lowering of the total value created from the transactions. We study the strategic and welfare implications of these findings, and discuss managerial and technology development guidelines.     

石化企业高硫石油焦利用途径探讨

介绍了高硫石油焦作锅炉燃料、气化原料和水泥窑燃料等几种利用方法,并进行了技术经济比较。提出当石化企业新增用气和用电负荷较大时,建设循环流化床热电站燃用高硫石油焦应是首选方案,否则高硫焦以外销为好。石油焦气化技术如果能结合供汽、供电和化工物料联产进行设计,可增大气化规模,充分利用规模效益,这样做尽管投资较大,还是有竞争力的,而且该方案的环保效果较好。石化企业在经济条件许可的情况下,可考虑选用高硫石油焦气化方案,如企业自产的高硫石油焦不够,可将几家企业所产的高硫石油焦集中使用,或高硫石油焦与煤混用。     

高硫原油加工组合工艺探讨

详细阐述了高硫原油加工的几种工艺特点以及应采取的组合工艺形式。对延迟焦化－加氢精制－催化裂化以及渣油加氢脱硫－重油催化裂化两种加工高硫原油的组合工艺进行了经济对比。最后介绍了３种高硫焦利用方案的优缺点,推荐大型石化基地采用部分氧化法气化方案。此方案不但将石油焦转化成多种装置的原料气,而且可满足基地的用电需要,还可解决环境污染问题。     

Using Distributed Computers to Deterministically Approximate Higher Dimensional Convection Diffusion Equations

A new approach to solving D> 3 spatial dimensional convection-diffusion equation on clusters of workstations is derived by exploiting the stability and scalability of the combination of a generalized D dimensional high-order compact (HOC) implicit finite difference scheme and parallelized GMRES(m). We then consider its application to multifactor Option pricing using the Black–Scholes equation and further show that an isotropic fourth order compact difference scheme is numerically stable and determine conditions under which its coefficient matrix is positive definite. The performance of GMRES(m) on distributed computers is limited by the inter-processor communication required by the matrix-vector multiplication. It is shown that the compact scheme requires approximately half the number of communications as a non-compact difference scheme of the same order of truncation error. As the dimensionality is increased, the ratio of computation that can be overlapped with communication also increases. CPU times and parallel efficiency graphs for single time step approximation of up to a 7D HOC scheme on 16 processors confirm the numerical stability constraint and demonstrate improved parallel scalability over non-compact difference schemes.     

Optimal lower bounds on the contribution margin in the case of stochastic order arrival

In customer order driven production, decisions on the acceptance of customer orders usually have to be based on variable costs and contribution margins (abbreviated CM), since in the short term only these quantities can be influenced. If we assume that customer orders arrive according to a stochastic process and that the decisions on order acceptance have to be made on each order separately, a customer order usually should be accepted only if its contribution margin exceeds a positive lower bound. This paper shows by means of a stochastic model that, under certain assumptions, this lower bound on the contribution margin can be determined using full costing, provided that the available capacity (constant over time) and the arrival process are balanced. This insight justifies, to a certain extent, the use of full costs to support decisions on the short-term production volume, which is a behaviour that can be observed in practice rather frequently. We also demonstrate the extension of the modelling approach to state-dependent lower bounds on the contribution margin.     

Computational aspects of prospect theory with asset pricing applications

We develop an algorithm to compute asset allocations for Kahneman and Tversky’s (Econometrica, 47(2), 263–291, 1979) prospect theory. An application to benchmark data as in Fama and French (Journal of Financial Economics, 47(2), 427–465, 1992) shows that the equity premium puzzle is resolved for parameter values similar to those found in the laboratory experiments of Kahneman and Tversky (Econometrica, 47(2), 263–291, 1979). While previous studies like Benartzi and Thaler (The Quarterly Journal of Economics, 110(1), 73–92, 1995), Barberis, Huang and Santos (The Quarterly Journal of Economics, 116(1), 1–53, 2001), and Grüne and Semmler (Asset prices and loss aversion, Germany, Mimeo Bielefeld University, 2005) focussed on dynamic aspects of asset pricing but only used loss aversion to explain the equity premium puzzle our paper explains the unconditional moments of asset pricing by a static two-period optimization problem. However, we incorporate asymmetric risk aversion. Our approach allows reducing the degree of loss aversion from 2.353 to 2.25, which is the value found by Tversky and Kahneman (Journal of Risk and Uncertainty, 5, 297–323, 1992) while increasing the risk aversion from 1 to 0.894, which is a slightly higher value than the 0.88 found by Tversky and Kahneman (Journal of Risk and Uncertainty, 5, 297–323, 1992). The equivalence of these parameter settings is robust to incorporating the size and the value portfolios of Fama and French (Journal of Finance, 47(2), 427–465, 1992). However, the optimal prospect theory portfolios found on this larger set of assets differ drastically from the optimal mean-variance portfolio.