A collaborative scheduling GA for products-packages service within extended selling chains environment

The theory of network coordination provides theoretical foundations to explain how companies can overcome organizational boundaries and constraints to jointly manage business processes across their selling chains. In particular, this work focuses on Collaborative Scheduling, a collaboration process whereby selling chain trading partners activate either on-line or off-line inter-firm coordination mechanisms to jointly plan production activities in order to deliver the final products to end customers each one of them, being the delivery date as close to the date desired as possible. The problem of collaborative scheduling is formally defined by means of a mathematical model. In the model, the defined objective function has the goal to minimize the total weighted tardiness of the package of products acquired by the clients to be delivered in a specific date. The delivery date of each Product-Package is conditioned by the latest date established by each supplier for each product that forms part of the same one. Besides, having different process times for each product and different penalties for each Product-Package, each supplier can offer a different mix of additional products with different due date. Due to the complexity of the problem a Genetic Algorithm has been the approach taken for its resolution. The GA elements and procedures are defined and the parameters are tuned. Although the major contribution of this work focuses on the algorithmic development of a proposal in the context of operations research that could help to solve the problem also is discussed the environment in which this occurs and that justifies our interest. In order to validate the proposed solutions diverse configurations are presented and the results obtained by means of the GA and some heuristics rules are compared.     

Stochastic Petri net modeling of wave sequences in cardiac arrhythmias

We describe a methodology for modeling heart rhythms observed in electrocardiograms. In particular, we present a procedure to derive simple dynamic models that capture the cardiac mechanisms which control the particular timing sequences of P and R waves characteristic of different arrhythmias. By treating the cardiac electrophysiology at an aggregate level, simple network models of the wave generating system under a variety of diseased conditions can be developed. These network models are then systematically converted to stochastic Petri nets which offer a compact mathematical framework to express the dynamics and statistical variability of the wave generating mechanisms. Models of several arrhythmias are included in order to illustrate the methodology.     

Optimal design of neural networks for control in robotic arc welding

Robotic gas metal arc (GMA) welding is a manufacturing process which is used to produce high quality joints and has to a capability to be utilized in automation systems to enhance productivity. Despite its widespread use in the various manufacturing industries, the full automation of the robotic GMA welding has not yet been achieved partly because mathematical models for the process parameters for a given welding tasks are not fully understood and quantified. In this research, an attempt has been made to develop a neural network model to predict the weld bead width as a function of key process parameters in robotic GMA welding. The neural network model is developed using two different training algorithms; the error back-propagation algorithm and the Levenberg–Marquardt approximation algorithm. The accuracy of the neural network models developed in this study has been tested by comparing the simulated data obtained from the neural network model with that obtained from the actual robotic welding experiments. The result shows that the Levenberg–Marquardt approximation algorithm is the preferred method, as this algorithm reduces the root of the mean sum of squared (RMS) error to a significantly small value.     

Comparison of packet-level and fluid models of IP networks

In this paper, data flows from different nodes in the packet network of data transmission are simulated as immiscible continuum flows. Conceptual and mathematical models are proposed. Computer simulation has been performed and compared with the ns-2 system of packet-level simulation. The developed model demonstrated fairly good accuracy, requiring at the same time much lower consumption of computing resources.     

Energy efficiency analysis of a chain-based scheme via intra-grid for wireless sensor networks

This study proposes the ideal energy mathematical model for the ideal PEGASIS architecture. Since the distance between nodes is the same, this ideal energy mathematical model can obtain a longer network lifetime than that of the PEGASIS architecture in a WSN. To achieve this objective, the intra-grid PEGASIS architecture, which is architecture that is based on the PEGASIS architecture, is proposed. In the proposed architecture, the sensor area is divided into several network grids, and the nodes of each network grid are deployed at random locations, and the nodes in the network grid are connected. Finally, all of the network grids are connected. The results of a simulation reveal that the energy consumption in each round in the ideal PEGASIS architecture almost equals that in the intra-grid PEGASIS architecture, but the PEGASIS architecture consumes the most energy in each round. Additionally, only a tiny difference is found between the network lifetime of the ideal PEGASIS and that of the intra-grid PEGASIS architecture, and the PEGASIS architecture has the shortest network lifetime.     

Real-time management of transportation disruptions in forestry

In this paper, we present a mathematical programming model based on a time-space network representation for solving real-time transportation problems in forestry. We cover a wide range of unforeseen events that may disrupt the planned transportation operations (e.g., delays, changes in the demand and changes in the topology of the transportation network). Although each of these events has different impacts on the initial transportation plan, one key characteristic of the proposed model is that it remains valid for dealing with all the unforeseen events, regardless of their nature. Indeed, the impacts of such events are reflected in a time-space network and in the input parameters rather than in the model itself. The empirical evaluation of the proposed approach is based on data provided by Canadian forestry companies and tested under generated disruption scenarios. The test sets have been successfully solved to optimality in short computational times and demonstrate the potential improvement of transportation operations incurred by this approach.     

Modeling honeybee communication using network of spiking neural networks to simulate nectar reporting behavior

The paper presents the findings of the research that attempted to mathematically model the cognitive behavior that could arise due to the interaction between honeybees in a colony during forager recruitment process. The model defines a honeybee as a spiking neural network, and colony as a network of spiking neural networks. The proposed mathematical model has been evaluated by analyzing the cognitive behavior generated by the main network which represents honeybees’ interaction as interactions of component networks (i.e. spiking neural networks). Accordingly, behavior of the component network, that represents an unemployed forager in the colony, was examined under different scenarios by setting networks’ parameters to simulate ecological situations in the colony. The reporting of different level of quantity of nectar sources by scouts to the colony, an attempt made by a scout to attract more unemployed foragers for foraging, and influence by dancing foragers to attract other unemployed foragers for foraging are those ecological colony states that have been tested in this research. The results of all these cases have supported that the proposed mathematical model can sufficiently simulate the unemployed forager’s behavior during recruitment process.     

Rapid Physarum Algorithm for shortest path problem

As shortest path (SP) problem has been one of the most fundamental network optimization problems for a long time, technologies for this problem are still being studied. In this paper, a new method by integrating a path finding mathematical model, inspired by Physarum polycephalum, with extracted one heuristic rule to solve SP problem has been proposed, which is called Rapid Physarum Algorithm (RPA). Simulation experiments have been carried out on three different network topologies with varying number of nodes. It is noted that the proposed RPA can find the optimal path as the path finding model does for most networks. What is more, experimental results show that the performance of RPA surpasses the path finding model on both iterations and solution time.     

基于模糊逻辑的分层组播拥塞控制机制

分析现有分层组播拥塞控制的优缺点,提出一种基于模糊逻辑的分层组播拥塞控制机制FLMCC。接收端利用指数平滑预测模型预测期望速率,根据期望速率选择层数,从而获得不同的吞吐量。发送端根据接收端的反馈使用模糊控制器对发送速率进行调整,从而平滑发送速率。仿真结果表明,FLMCC具有良好的公平性和速率平滑性,能够适应网络异构性。     

基于拟生灭过程的多跳Ad hoc网络洪泛方式下拥塞控制及饱和条件研究

802.11Ad hoc网络中拥塞控制是保证网络稳定性的重要因素。拥塞发生时,通常的做法是控制流量,而研究网络何时出现拥塞则是目前的首先任务。针对多跳Ad hoc网络,对单个节点的数据发送过程,考虑包的最大重传次数、数据包的缓冲队列,提出一种新的基于802.11MAC接入协议DCF Basic Access机制的无限状态拟生灭过程模型。研究了在洪泛方式下为避免网络到达饱和状态,节点MAC层上数据包的到达率应满足的条件。指出在DCF协议下到达率对网络状态的影响。与现有文献的区别是,从网络层的角度进行研究,提出了一种新的更精确地描述数据发送过程的模型。给出了多跳Ad hoc网络中稳定性与数据到达率的数学关系式,为多跳Ad hoc网络的拥塞控制提供了一个重要的参考。     

网络流量预测模型研究进展

网络流量预测的关键是建立相应的数学模型.介绍了网络流量预测模型的基本概念和分类,指出了单一预测模型存在的问题.从模型构建原理出发,重点对组合预测模型进行了系统的分类,将其分为线性组合模型、优化组合模型、分解重构组合模型,介绍了各组合模型的相关研究进展,并分析了它们的特点和适用范围,指出组合预测模型能更精确、全面地预测流量.针对流量预测目前存在的问题,对未来研究趋势提出了几点思考.     

An expert system for flow routing in a river network

Flow routing in a river network is one of the most important parts in the domain of water resources and hydrology. There are different characteristics in the different river networks. The mathematical models adopted to deal with the flow routing have their own advantages respectively. Thus a suitable model should be selected to solve the problem of flow routing on a specially appointed river network. But this selection is very difficult and requires highly specialized knowledge and expertise about the models. It is not easy and not appropriate for a civil engineer to spend much time in learning how to select a suitable model and how to manipulate it. Recent developments in mathematical model of flow routing and artificial intelligence have rendered it possible to construct an expert system to help civil engineers make this kind of decision. An expert system is addressed to handle this decision-making in this paper. The way for manipulation of the selected model and whether or not the interbasin rainfall-runoff should be considered are integrated into the system too. VP-Expert is employed as the shell of this expert system. The expert system has been tested on the Changtan watershed, north Guangdong province of China.     

网络仿真软件的应用与实践

网络仿真是一种利用数学建模和统计分析的方法模拟网络行为,从而获取特定的网络特性参数的技术。该文使用网络仿真软件进行网络性能仿真,通过仿真测试网络利用率、丢包率及阻塞情况,构造了不同处理方式的网络模型,给出了仿真结果并进行了分析。     

Ontology based approach to Bayesian student model design

Probabilistic student model based on Bayesian network enables making conclusions about the state of student’s knowledge and further learning and teaching process depends on these conclusions. To implement the Bayesian network into a student model, it is necessary to determine “a priori” probability of the root nodes, as well as, the conditional probabilities of all other nodes. In our approach, we enable non-empirical mathematical determination of conditional probabilities, while “a priory” probabilities are empirically determined based on the knowledge test results. The concepts that are believed to have been learned or not learned represent the evidence. Based on the evidence, it is concluded which concepts need to be re-learned, and which not. The study described in this paper has examined 15 ontologically based Bayesian student models. In each model, special attention has been devoted to defining “a priori” probabilities, conditional probabilities and the way the evidences are set in order to test the successfulness of student knowledge prediction. Finally, the obtained results are analyzed and the guidelines for ontology based Bayesian student model design are presented.     

An approximate optimization strategy using refined hybrid metamodel

An approximate model called metamodel or surrogate model is a mathematical model that numerically approximates response of a system during an engineering simulation process or test. The introduction of a metamodel makes it possible to express response defined in the design problem as a simple mathematical function of design variables. A metamodel can be built with response surface method (RSM), kriging, neural network, radial basis function, and so on. Each method has its advantages and disadvantages. A combined metamodel called hybrid model, ensemble model, or multiple surrogates has been developed to maximize each metamodel's strength. The hybrid model of this research includes RSM and kriging. Besides, a strategy to refine the hybrid metamodel is implemented by reducing design space. In this process, information related to Hessian is utilized for an unconstrained optimization problem, on the contrary feasibility for a constrained optimization problem. This research presents a new hybrid metamodel-based optimization strategy called refined hybrid metamodel. Five mathematical test problems, two-bar design, spring design, and propeller shaft design problems are solved with the suggested method, verifying its usefulness. Most of the optimal results with the proposed method are closer to exact solutions with smaller function evaluations than existing methods.     

蛋白质相互作用网络演化模型研究进展

研究蛋白质相互作用网络的演化机制及模型对于理解生物系统的进化及组织形成过程具有重要的意义。到目前为止,已经出现了多种依赖不同演化机制的蛋白质相互作用网络演化模型,这些模型有针对性地体现了真实蛋白质相互作用网络中出现的某些拓扑特征,但同时也具有一定的局限性。通过对典型蛋白质相互作用网络演化模型进行研究,从模型的构建机理、演化模型及真实蛋白质相互作用网络的拓扑特征等方面进行了分析和比较,并总结了各个模型的特点。最后,对蛋白质网络演化模型的进一步发展提出了自己的看法,为深入理解蛋白质相互作用网络演化模型提供有益参考。     

Allocating data and operations to nodes in distributed databasedesign

The allocation of data and operations to nodes in a computer communications network is a critical issue in distributed database design. An efficient distributed database design must trade off performance and cost among retrieval and update activities at the various nodes. It must consider the concurrency control mechanism used as well as capacity constraints at nodes and on links in the network. It must determine where data will be allocated, the degree of data replication, which copy of the data will be used for each retrieval activity, and where operations such as select, project, join, and union will be performed. We develop a comprehensive mathematical modeling approach for this problem. The approach first generates units of data (file fragments) to be allocated from a logical data model representation and a characterization of retrieval and update activities. Retrieval and update activities are then decomposed into relational operations on these fragments. Both fragments and operations on them are then allocated to nodes using a mathematical modeling approach. The mathematical model considers network communication, local processing, and data storage costs. A genetic algorithm is developed to solve this mathematical formulation     

基于价格的拥塞控制模型扩展

基于价格的拥塞控制模型将网络拥塞控制问题抽象为聚合效用最大化的优化问题,已经成为拥塞控制机制设计和分析的一般框架。然而简化的线性模型与现实网络环境有一定差距,不能准确有效地指导新协议的设计与分析。因此,近年来研究人员正试图结合新的技术和应用,扩展完善基于价格的模型,建立更为准确有效的模型来反映现实的网络环境。为了系统地了解该领域研究工作的进展,本文首先简要回顾了基于价格的模型,分析其不足,介绍近年来在模型扩展方面的研究工作,最后指出几个有意义的研究方向。     

Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models

Predicting student academic performance has long been an important research topic in many academic disciplines. The present study is the first study that develops and compares four types of mathematical models to predict student academic performance in engineering dynamics – a high-enrollment, high-impact, and core course that many engineering undergraduates are required to take. The four types of mathematical models include the multiple linear regression model, the multilayer perception network model, the radial basis function network model, and the support vector machine model. The inputs (i.e., predictor variables) of the models include student's cumulative GPA, grades earned in four pre-requisite courses (statics, calculus I, calculus II, and physics), and scores on three dynamics mid-term exams (i.e., the exams given to students during the semester and before the final exam). The output of the models is students' scores on the dynamics final comprehensive exam. A total of 2907 data points were collected from 323 undergraduates in four semesters. Based on the four types of mathematical models and six different combinations of predictor variables, a total of 24 predictive mathematical models were developed from the present study. The analysis reveals that the type of mathematical model has only a slight effect on the average prediction accuracy (APA, which indicates on average how well a model predicts the final exam scores of all students in the dynamics course) and on the percentage of accurate predictions (PAP, which is calculated as the number of accurate predictions divided by the total number of predictions). The combination of predictor variables has only a slight effect on the APA, but a profound effect on the PAP. In general, the support vector machine models have the highest PAP as compared to the other three types of mathematical models. The research findings from the present study imply that if the goal of the instructor is to predict the average academic performance of his/her dynamics class as a whole, the instructor should choose the simplest mathematical model, which is the multiple linear regression model, with student's cumulative GPA as the only predictor variable. Adding more predictor variables does not help improve the average prediction accuracy of any mathematical model. However, if the goal of the instructor is to predict the academic performance of individual students, the instructor should use the support vector machine model with the first six predictor variables as the inputs of the model, because this particular predictor combination increases the percentage of accurate predictions, and most importantly, allows sufficient time for the instructor to implement subsequent educational interventions to improve student learning.