大规模定制环境下多项目计划过程模型及其应用研究

根据大规模定制的分类,对大规模定制环境下的多项目进行了界定,提出了大规模定制环境下横向与纵向多项目集成的概念。同时,在分析其他学者关于生产计划方法研究的基础上,提出了纯定制与特制标准化大规模定制环境下多项目计划安排的过程模型,并对其内容进行了分析。通过阐述其在实际企业中的应用步骤与关键点,并在企业的实际应用发现,这种将大规模定制环境下的订单按阶段进行分解,并根据阶段工作内容的区别进行计划安排的方法,更有利于企业对整个订单完成过程的控制,提高企业大规模定制能力。

Application of a fuzzy analytical hierarchy process to the prediction of vibration during rock sawing

A new predictive model for evaluating the vibration of a sawing machine was developed using a new rock classification system. The predictors are machine parameters and a rock sawability index. The new rock classification system includes four major parameters of the rock： uniaxial compressive strength, abrasiv- ity index, mean MoWs hardness, and Young＇s modulus. The FAHP approach was used when determining the weights of these parameters by six decision makers. Two groups of carbonate rocks were sawn using a fully-instrumented laboratory sawing rig at different feed rates and depths of cut. During the sawing trials system vibration was monitored as a measure of saw performance. Then, a new statistical model was obtained by multiple regression on the machining parameters and the rock sawability index. The model is very useful for the evaluation of the system vibration, and for selecting suitable machining parameters, from a limited set of mechanical properties.

Drilling Processes Using Drag Bits and Its Application

This paper proposes an analytical model to describe rock drilling processes using drag bits and rotary drills, and to induce relations among rock properties, bit shapes, and drilling parameters （rotary speed, thrust, torque, and stroke）. In this model, a drilling process is divided into successive cycles. Each cycle includes two motions： feed and cutting. According to this model, drilling torque includes four components generated from cutting, friction, feed, and idle running respectively, the first three items are all proportional to the UCS （uniaxial compressive strength） when the penetration rate is constant. Laboratory tests verified the correctness and effectiveness of the proposed model qualitatively. Especially, the influence of friction on the flank face and the idle running was confirmed. Field experiments were performed. The results showed good correlation between the torque, penetration rate, and UCS. The proposed model and equations engender the possibility of eliminating useless components of cutting forces when investigating the relation between mechanical data and physical properties of rocks.

A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling

A selective moving window partial least squares(SMW-PLS) soft sensor was proposed in this paper and applied to a hydro-isomerization process for on-line estimation of para-xylene(PX) content. Aiming at the high frequency of model updating in previous recursive PLS methods, a selective updating strategy was developed. The model adaptation is activated once the prediction error is larger than a preset threshold, or the model is kept unchanged.As a result, the frequency of model updating is reduced greatly, while the change of prediction accuracy is minor.The performance of the proposed model is better as compared with that of other PLS-based model. The compromise between prediction accuracy and real-time performance can be obtained by regulating the threshold. The guidelines to determine the model parameters are illustrated. In summary, the proposed SMW-PLS method can deal with the slow time-varying processes effectively.

A Double Shot Noise Process and Its Application in Insurance

The authors consider a compound Cox model of insurance risk with the additional economic assumption of a positive interest rate. As the authors note a duality result relating a compound Cox model of insurance risk with a positive interest rate and a double shot noise process, the authors analyze a double shot noise process systematically for its theoretical distributional properties, based on the piecewise deterministic Markov process theory, and the martingale methodology. The authors also obtain the moments of aggregate accumulated/discounted claims where the claim arrival process follows a Cox process with shot noise intensity. Removing the parameters in a double shot noise process gradually, the authors show that it becomes a compound Cox process with shot noise intensity, a single shot noise process and a compound Poisson process. Numerical comparisons are shown between the moments （i.e. means and variances） of a compound Poisson model and their counterparts of a compound Cox model with/without considering a positive interest rate. For that purpose, the authors assume that claim sizes and primary event sizes follow an exponential distribution, respectively.

A Practice Guide of Software Aging Prediction in a Web Server Based on Machine Learning

In the past two decades, software aging has been studied by both academic and industry communities. Many scholars focused on analytical methods or time series to model software aging process. While machine learning has been shown as a very promising technique in application to forecast software state: normal or aging. In this paper, we proposed a method which can give practice guide to forecast software aging using machine learning algorithm. Firstly, we collected data from a running commercial web server and preprocessed these data. Secondly, feature selection algorithm was applied to find a subset of model parameters set. Thirdly, time series model was used to predict values of selected parameters in advance. Fourthly, some machine learning algorithms were used to model software aging process and to predict software aging. Fifthly, we used sensitivity analysis to analyze how heavily outcomes changed following input variables change. In the last, we applied our method to an IIS web server. Through analysis of the experiment results, we find that our proposed method can predict software aging in the early stage of system development life cycle.

Kinetic Modeling of Isothermal or Non-isothermal Adsorption in a Pellet：Application to Adsorption Heat Pumps

Understanding the interaction between a fluid and a solid phase is of fundamental importance to the design of an adsorption process.Because the heat effects associated with adsorption are comparatively large,the as-sumption of isothermal behavior is a valid approximation only when uptake rates are relatively slow.In this article,we propose to determine when it is needed to choose the isothermal or non-isothermal assumption according to two physical parametersα（ratio convection/capacity） andβ（quantity of energy/capacity） .The proposed problem is solved by a mathematical method in the Laplace domain.Whenα→∞（infinitely high heat transfer coefficient） or β→0（infinitely large heat capacity） ,the limiting case is isothermal.When the diffusion is rapid（α10） the kinetics of sorption is controlled entirely by heat transfer.If the adsorption process is to be used as a heat pump,it shall be represented by an isotherm model withαandβas high as possible.

Pole-placement self-tuning control of nonlinear Hammerstein system and its application to pH process control

By taking advantage of the separation characteristics of nonlinear gain and dynamic sector inside a Hammerstein model, a novel pole placement self tuning control scheme for nonlinear Hammerstein system was put forward based on the linear system pole placement self tuning control algorithm. And the nonlinear Hammerstein system pole placement self tuning control(NL-PP-STC) algorithm was presented in detail. The identi fication ability of its parameter estimation algorithm of NL-PP-STC was analyzed, which was always identi fiable in closed loop. Two particular problems including the selection of poles and the on-line estimation of model parameters, which may be met in applications of NL-PP-STC to real process control, were discussed. The control simulation of a strong nonlinear p H neutralization process was carried out and good control performance was achieved.

A dynamic model for railway freight overbooking

In order to apply overbooking idea in Chinese railway freight industry to improve revenue, a Markov decision process(dynamic programming) model for railway freight reservation was formulated and the overbooking limit level was proposed as a control policy. However, computing the dynamic programming treatment needs six nested loops and this will be burdensome for real-world problems. To break through the calculation limit, the properties of value function were analyzed and the overbooking protection level was proposed to reduce the calculating quantity. The simulation experiments show that the overbooking protection level for the lower-fare class is higher than that for the higher-fare class, so the overbooking strategy is nested by fare class. Besides, by analyzing the influence on the overbooking strategy of freight arrival probability and cancellation probability, the proposed approach is efficient and also has a good application prospect in reality. Also, compared with the existing reservation(FCFS), the overbooking strategy performs better in the fields of vacancy reduction and revenue improvement.

Auxiliary error and probability density function based neuro-fuzzy model and its application in batch processes

This paper focuses on resolving the identification problem of a neuro-fuzzy model(NFM) applied in batch processes. A hybrid learning algorithm is introduced to identify the proposed NFM with the idea of auxiliary error model and the identification principle based on the probability density function(PDF). The main contribution is that the NFM parameter updating approach is transformed into the shape control for the PDF of modeling error. More specifically, a virtual adaptive control system is constructed with the aid of the auxiliary error model and then the PDF shape control idea is used to tune NFM parameters so that the PDF of modeling error is controlled to follow a targeted PDF, which is in Gaussian or uniform distribution. Examples are used to validate the applicability of the proposed method and comparisons are made with the minimum mean square error based approaches.

Advantages of Multi-body Simulation within the Design Process of Heavy Drivetrains

For the further design of the particular gearbox components, the alternating cycles of the respective application mean an often insufficient knowledge of the actual loads occuring in use. Especially for the application within lifting units, such dynamic load cycles are very difficult to pre-estimate. The so-called slack rope test represents the most critical point in the load cycle and provides a special challenge for the gearbox design. Because of this missing expert knowledge, a test bench of such an application is installed and applied to practical movement cycles. Besides the test bench, a multi-body simulation model of the whole system is mapped within the MBS （multi-body simulation） environment SIMPACK. To verify this simulation model, the results are compared with the respective measurements of the test bench. These comparisons show very good agreements. Thus, one of the major advantages of using such simulation tools is the possibility to re-evaluate the internal and external loads during the whole design process. Finally, these simulations serve as a clarification of the load spectrum of the different drivetrain components. Gearbox series or different modifications of the design can now be analyzed prospectively without extensive testing.

Demand Model for Investment-oriented Life Insurance under uncertain lifetime

In this paper, we set up continuous time model with Poisson Process to analyze demand of investment-oriented life insurance. Individual life time is assumed random, and he is received fixed income, investment-oriented life insurance is an important financial asset under this model. Dynamic programming is applied to analyze this problem. The optimal explicit solutions are obtained in the case of CRRA utilities, and draw its demand curve with numerical simulation.