Simulation model for anisotropic fibrous materials

Paper and paper–based materials such as cardboard are used in a wide variety of applications and in the development of new applications such as boxes an accurate simulation model is of major importance. Industrially made paper material typically has an orthotropic fibrous structure, due to the manufacturing process, where the fibers tend to align in the direction of motion in the machine. In this work a plasticity–based material model allowing for finite strains is developed. The model is suitable for materials with an anisotropic fibrous structure such as paper. The general framework is based on separate mappings describing the deformations of the continuum and the substructure and a multiplicative split of these mappings into elastic and plastic parts. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A fuzzy approach to using expert knowledge for tuning paper machines

Paper machines are very complex production systems, but their scope is simple: they consume materials and resources, called factors, to produce paper, which in turn can be described by its characteristics. In this paper, a decision support system is developed in cooperation with an industrial partner to help them with operational decision making when tuning a paper machine. The decision support system was developed in two phases. Firstly, the knowledge of experts is collected and stored in the form of a fuzzy ontology. Secondly, this knowledge is made usable so that a user of the decision support system can specify what characteristics of the produced paper to increase or to decrease and be returned with a recommendation on what factors to change. In this paper, we will work out the optimization problems on which the system is based. Additionally to a basic goal programming model, two extensions are explored, accounting for uncertainty and non-linearity, respectively.     

A recursive linear programming analysis of the future of the pulp and paper industry in the United States: Changes in supplies and demands,and the effects of recycling

The impacts of increased paper recycling on the U.S. pulp and paper sector are investigated, using the North American Pulp And Paper (NAPAP) model. This dynamic spatial equilibrium model forecasts the amount of pulp, paper and paperboard exchanged in a multi-region market, and the corresponding prices. The core of the model is a recursive price-endogenous linear programming system that simulates the behavior of a competitive industry. The model has been used to make forecasts of key variables describing the sector from 1986 to 2012, demand for paper would have the greatest impact on the amount of wood used. But the minimum recycled content policies envisaged currently would have no more effect than what will come about due to unregulated market forces.     

Machine reliability and preventive maintenance planning for cellular manufacturing systems

The paper proposes a preventive maintenance (PM) planning model for the performance improvement of cellular manufacturing systems (CMS) in terms of machine reliability, and resource utilization. In a CMS, parts are processed by a group of interdependent machines, where machine reliability plays an important role in the performance improvement of the cell. Assuming that machine failure times follow a Weibull distribution, the proposed model determines a PM interval and a schedule for performing PM actions on each machine in the cell by minimizing the total maintenance cost and the overall probability of machine failures. The model uses a combined cost and reliability based approach, and optimizes maintenance costs by administering a group maintenance policy subject to a desirable machine reliability threshold. The study also proposes a CMS design model that integrates the above PM concepts into the design process. Illustrative examples are presented to demonstrate the applicability of the proposed approach.     

A note on “A mixed integer programming model for advanced planning and scheduling (APS)”

In a recent paper, Chen and Ji [Chen, K., Ji, P., 2007. A mixed integer programming model for advanced planning and scheduling (APS). European Journal of Operational Research 181, 515–522] develop a mixed integer programming model for advanced planning and scheduling problem that considers capacity constraints and precedence relations between the operations. The orders require processing of several operations on eligible machines. The model presented in the above paper works for the case where each operation can be processed on only one machine. However, machine eligibility means that only a subset of machines are capable of processing a job and this subset may include more than one machine. We provide a general model for advanced planning and scheduling problems with machine eligibility. Our model can be used for problems where there are alternative machines that an operation can be assigned to.     

Selfish load balancing for jobs with favorite machines

This paper studies the load balancing game for the favorite machine model, where each job has a certain set of favorite machines with the shortest processing time for the job. We obtain tight bounds on the Strong Price of Anarchy (strong PoA) for the general favorite machine model and a special case of the model. Our results generalize the well-known bounds on the strong PoA for the unrelated machine and identical machine models.     

基于遗传算法和熵的缩减记忆式LS-SVM财务困境预测模型研究

为了提高财务困境预测的正确率,减少模型的训练样本数和训练时间,在传统支持向量机(SVM)预测模型的基础上,将遗传算法、信息熵和缩减记忆算法应用于最小二乘支持向量机(LS-SVM),提出了一种基于遗传算法和信息熵的缩减记忆式最小二乘支持向量机预测模型。并独立推导出了适合财务困境预测这一离散序列的熵以及支持向量机核函数的表达式,同时,给出了这一改进模型的实现步骤。实验结果表明,该模型无论是预测正确率,还是训练样本的数量和训练时间,都显著优于最小二乘支持向量机以及传统支持向量机模型。     

A discrete time optimal control model with uncertainty for dynamic machine allocation problem and its application to manufacturing and construction industries

This paper proposed a discrete time optimal control model in which machine failure time is modeled assuming a Weibull distribution and machine productivity is regarded as a fuzzy variable for dealing with a dynamic machine allocation problem (DMAP) in manufacturing and construction industries. The aim is to maximize total production or construction throughput when uncertainties such as machine breakdowns are taken into account. A failure probability-work time equation is presented to describe the relationship between machine failure probability and mean time to work. To transform the uncertain optimal control model into a deterministic one, the expected value model (EVM) was introduced for forming an equivalent crisp model. The fuzzy variables in the model are also defuzzified by using an expected value operator with an optimistic–pessimistic index. Then a number of lemmas and theorems are presented and proved to formulate the theoretical algorithm so that the crisp model of the DMAP can be solved. Three actual construction and production projects are used as practical application examples. The theoretical algorithm results for the three project examples are compared with a particle swarm optimization approach and a genetic algorithm method, which demonstrates the practicality and efficiency of our optimization method.     

数控机床现场故障分布模型的统计研究

本文对２４台可换刀计算机数控机床的现场故障数据进行一年多的跟踪采集和数理统计分析，提出了整机故障分布的数学模型，为制订评估机床可靠性的特征量提供了理论依据。     

A new approach towards integrated cell formation and inventory lot sizing in an unreliable cellular manufacturing system

This paper presents a comprehensive mathematical model for integrated cell formation and inventory lot sizing problem. The proposed model seeks to minimize cell formation costs as well as the costs associated with production, while dynamic conditions, alternative routings, machine capacity limitation, operations sequences, cell size constraints, process deterioration, and machine breakdowns are also taken into account. The total cost consists of machine procurement, cell reconfiguration, preventive and corrective repairs, material handling (intra-cell and inter-cell), machine operation, part subcontracting, finished and unfinished parts inventory cost, and defective parts replacement costs. With respect to the multiple products, multiple process plans for each product and multiple routing alternatives for each process plan which are assumed in the proposed model, the model is combinatorial. Moreover, unreliability conditions are considered, because moving from “in-control” state to “out-of-control” state (process deterioration) and machine breakdowns make the model more practical and applicable. To conquer the breakdowns, preventive and corrective actions are adopted. Finally, a Particle Swarm Optimization (PSO)-based meta-heuristic is developed to overcome NP-completeness of the proposed model.     

Using ARMA models to forecast workpiece roundness error in a turning operation

The paper describes the methodology for developing autoregressive moving average (ARMA) models to represent the workpiece roundness error in the machine taper turning process. The method employs a two stage approach in the determination of the AR and MA parameters of the ARMA model. It first calculates the parameters of the equivalent autoregressive model of the process, and then derives the AR and MA parameters of the ARMA model. Akaike's Information Criterion (AIC) is used to find the appropriate orders m and n of the AR and MA polynomials respectively. Recursive algorithms are developed for the on-line implementation on a laboratory turning machine. Evaluation of the effectiveness of using ARMA models in error forecasting is made using three time series obtained from the experimental machine. Analysis shows that ARMA(3,2) with forgetting factor of 0.95 gives acceptable results for this lathe turning machine.     

Dynamic routing to heterogeneous collections of unreliable servers

We argue the importance of problems concerning the dynamic routing of tasks for service in environments where the servers have diverse characteristics and are subject to breakdown. We propose a general model in which both service times and repair times at each machine are i.i.d.with some general distribution. Routing decisions take account of queue lengths, machine states (up or down), the elapsed processing times of jobs in service and the times to date of any machine repairs in progress. We develop an approach to machine calibration which yields a machine index which is a function of all of the preceding information. The heuristic which routes all tasks to the machine of current smallest index performs outstandingly well. The approach of the paper is flexible and is capable of yielding strongly performing routing policies for a range of variants of the basic model. These include cases where job processing is lost at each breakdown and where the machine state may be only partially observed. AMS Subject Classifications 49L20, 90B22     

Nonlinear modeling and analysis of a rolling element bearing with a clearance

Rolling element bearings are the key components in many rotating machinery. For efficient performance of the machine it is necessary to accurately predict the effect of various parameters and operating conditions on the machine’s behavior. This paper deals with the development of a nonlinear model of the rotor-bearing system on rolling element bearings with clearance. Clearance is an important nonlinearity which can cause bifurcations and chaos as has been shown in this paper. In this paper a detailed model for clearance is developed. In this model the inner race center and the outer race center are not assumed to be collinear when relations for deflections in the rolling element are developed. The model is non-dimensionalized and then analyzed to reveal rich nonlinear phenomena. Further, for better performance of any machine it is necessary to identify and stay out of chaotic regimes of operation. Hence, Lyapunov exponents and Poincaré mappings are used to analyze the system and determine the regions of chaotic response.     

Fuzzy幂群的基数定理

文（１）提出了幂群的概念，给出了幂群中各元素是等势的基数定理，文（２）提出了Ｆｕｚｚｙ幂群的概念，但没研究其中各元素的基数问题，本文深入研究这一问题，得到了由Ｄ．Ｄｕｂｏｉｓ等在文（３）中提出的和由李洪兴等在文（４）中提出的两种Ｆｕｚｚｙ集基数形式下的Ｆｕｚｚｙ幂群的基数定理，并给出了Ｆｕｚｚｙ幂群中与基数有关的若干结果。     

兼职辅助工作的机器维修模型

本文研究了单修理工有效利用闲期兼职辅助工作的机器维修模型。这个模型被视为带休假策略的有限源排队问题，利用拟生灭过程的方法，给出了系统在稳态下机器故障数的分布和机器等待维修时间的分布，并将结果与经典的机器维修模型作了比较。     

Fuzzy extensions to Integer Programming models of cell-formation problems in machine scheduling

Cell formation has received much attention from academicians and practitioners because of its strategic importance to modern manufacturing practices. Existing research on cell formation problems using integer programming (IP) has achieved the target of solving problems that simultaneously optimise: (a) cell formation, (b) machine-cell allocation, and (c) part-machine allocation. This paper will present extensions of the IP model where part-machine assignment and cell formation are addressed simultaneously, and also a significant number of constraints together with an enhanced objective function are considered. The main study examines the integration of inter-cell movements of parts and machine set-up costs within the objective function, and also the combination of machine set-up costs associated with parts revisiting a cell when part machine operation sequence is taken into account. The latter feature incorporates a key set of constraints which identify the number of times a part travels back to a cell for a later machine operation. Due to two main drawbacks of IP modelling for cell formation, i.e. (a) only one objective function can be involved and (b) the decision maker is required to specify precisely goals and constraints, fuzzy elements like fuzzy constraints and fuzzy goals will be considered in the proposed model. Overall the paper will not only include an extended and enhanced integer programming model for assessing the performance of cell formation, but also perform a rigorous study of fuzzy integer programming and demonstrate the feasibility of achieving better and faster clustering results using fuzzy theory.     

Queueing network model for a single-operator machine interference problem with external operations

Machine interference is a significant problem in many manufacturing systems. Prior research shows that machine interference may be as high as 10% of machine time. This paper proposes a queueing network model for a single-operator, machine interference problem with external operations, i.e., those tasks that can be completed while the machine is running. The interactions for part/machine and machine/operator are modeled as an open and a closed queueing network, respectively. In the open network, part inter-arrival time follows an exponential distribution. In both networks, service times follow a general distribution that is characterized by their first two moments. An iterative procedure is developed to solve the proposed model. Solution quality is justified by an industry-based case study. Data were collected from the integrated circuit (IC) ink-marking machines of a leading IC packaging company in Taiwan that allowed the construction of an experimental design for computational tests that encompassed a wide range of production scenarios. Empirical results show promise for the proposed methodology in helping to solve industrial problems. Model limitations as well as future research opportunities are discussed.     

关于“几类具5次强非线性项的发展方程的显式精确孤波解”的注记

关于非线性发展方程的孤波解问题一直是人们感兴趣的一个研究课题 .本文指出文 [2 ]中关于Kundu方程和导数 Schr dinga方程的结论事实上是可以用积分法得到 ,而且文 [1 ,2 ]中涉及到的其它非线性发展方程的相应结论 ,用积分法也能得到     

An application of support vector machines to sales forecasting under promotions

This paper deals with sales forecasting of a given commodity in a retail store of large distribution. For many years statistical methods such as ARIMA and Exponential Smoothing have been used to this aim. However the statistical methods could fail if high irregularity of sales are present, as happens for instance in case of promotions, because they are not well suited to model the nonlinear behaviors of the sales process. In recent years new methods based on machine learning are being employed for forecasting applications. A preliminary investigation indicates that methods based on the support vector machine (SVM) are more promising than other machine learning methods for the case considered. The paper assesses the application of SVM to sales forecasting under promotion impacts, compares SVM with other statistical methods, and tackles two real case studies.     

基于支持向量机的神经元形态分类

针对神经元的空间几何形态特征分类问题以及神经元的生长预测问题进行了探讨.结合神经元的形态数据,分别建立了基于支持向量机的神经元形态分类模型、基于主成分分析和支持向量机的神经元分类模型以及基于遗传算法和RBF网络的神经元生长预测模型,在较合理的假设下,对各个模型进行求解,得到了较理想的结果.