A method for stochastic multiple criteria decision making based on dominance degrees

This paper proposes a method for solving the stochastic multiple criteria decision making (SMCDM) problem, where consequences of alternatives with respect to criteria are represented by random variables with probability distributions. Firstly, definitions and related analysis of dominance degree of one probability distribution over another are given. Then, by calculating the dominance degrees, the dominance degree matrix of alternative pairwise comparisons with respect to each criterion is built. Further, using PROMETHEE II method, an overall dominance degree matrix of alternative pairwise comparisons is constructed, and a net flow of each alternative is calculated. Based on the obtained net flows, a ranking of alternatives is determined. Finally, numerical examples for the three cases are given to illustrate the use of the proposed method.     

Advanced resource planning as a decision support module for ERP

The planning and decision support capabilities of the manufacturing planning and control system, which provides the core of any enterprise resource planning package, can be enhanced substantively by the inclusion of a decision support module as an add-on at the midterm planning level. This module, called advanced resource planning (ARP), provides a parameter-setting process, with the ultimate goal of yielding realistic information about production lead times for scheduling purposes, sales and marketing, strategic and operational decision making, and suppliers and customers. This article illustrates the ARP approach with reports from several real-life implementations by large industrial companies.     

Sliding mode control of switched hybrid systems with stochastic perturbation

This paper is concerned with the sliding mode control (SMC) of a continuous-time switched stochastic system. A sufficient condition for the existence of reduced-order sliding mode dynamics is derived and an explicit parametrization of the desired sliding surface is also given. Then, a sliding mode controller is then synthesized for reaching motion. Moreover, the observer-based SMC problem is also investigated. Some sufficient conditions are established for the existence and the solvability of the desired observer and the observer-based sliding mode controller is synthesized. Finally, numerical examples are provided to illustrate the effectiveness of the proposed theory.     

Performance analysis of hierarchical group key management integrated with adaptive intrusion detection in mobile ad hoc networks

We develop a mathematical model to quantitatively analyze a scalable region-based hierarchical group key management protocol integrated with intrusion detection to deal with both outsider and insider security attacks for group communication systems (GCSs) in mobile ad hoc networks (MANETs). Our proposed adaptive intrusion detection technique is based on majority voting by nodes in a geographical region to cope with collusion of compromised nodes, with each node preloaded with anomaly-based or misuse-based intrusion detection techniques to diagnose compromised nodes in the same region. When given a set of parameter values characterizing operational and environmental conditions, we identify the optimal intrusion detection rate and the optimal regional area size under which the mean time to security failure of the system is maximized and/or the total communication cost is minimized for GCSs in MANET environments. The tradeoff analysis in performance versus security is useful in identifying and dynamically applying optimal settings to maximize the system lifetime for scalable mobile group applications while satisfying application-specific performance requirements.     

Model reduction of multi-scale chemical Langevin equations

This paper addresses the model reduction problem for a class of stiff chemical Langevin equations that arise as models of biomolecular networks with fast and slow reactions and can be described as continuous Markov processes. Initially, a coordinate transformation is sought that allows the decoupling of fast and slow variables in the model equations. Necessary and sufficient conditions are derived for such a linear transformation to exist, along with an explicit change of variables which achieves the desired decoupling. For the systems for which this step is applicable, the method of adiabatic elimination is applied to determine a representation of the slow dynamics. Theoretical concepts and results are illustrated with simple examples.     

Dynamic demultiplexing in network calculus—Theory and application

During the last two decades, starting with the seminal work by Cruz, network calculus has evolved as a new theory for the performance analysis of networked systems. In contrast to classical queueing theory, it deals with performance bounds instead of average values and thus has been the theoretical basis of quality of service proposals such as the IETF’s Integrated and Differentiated Services architectures. Besides these it has, however, recently seen many other application scenarios as, for example, wireless sensor networks, switched Ethernets, avionic networks, Systems-on-Chip, or even to speed-up simulations, to name a few.In this article, we extend network calculus by adding a new versatile modeling element: a demultiplexer. Conventionally, demultiplexing has been either neglected or assumed to be static, i.e., fixed at the setup time of a network. This is restrictive for many potential applications of network calculus. For example, a load balancing based on current link loads in a network could not be modeled with conventional network calculus means. Our demultiplexing element is based on stochastic scaling. Stochastic scaling allows one to put probabilistic bounds on how a flow is split inside the network. Fundamental results on network calculus with stochastic scaling are therefore derived in this work. We illustrate the benefits of the demultiplexer in a sample application of uncertain load balancing.     

Existence of optimal controls for systems driven by FBSDEs

We prove the existence of optimal relaxed controls as well as strict optimal controls for systems governed by non linear forward-backward stochastic differential equations (FBSDEs). Our approach is based on weak convergence techniques for the associated FBSDEs in the Jakubowski S-topology and a suitable Skorokhod representation theorem.     

Stochastic volatility models with leverage and heavy-tailed distributions: A Bayesian approach using scale mixtures

This paper studies a heavy-tailed stochastic volatility (SV) model with leverage effect, where a bivariate Student-t distribution is used to model the error innovations of the return and volatility equations. Choy et al. (2008) studied this model by expressing the bivariate Student-t distribution as a scale mixture of bivariate normal distributions. We propose an alternative formulation by first deriving a conditional Student-t distribution for the return and a marginal Student-t distribution for the log-volatility and then express these two Student-t distributions as a scale mixture of normal (SMN) distributions. Our approach separates the sources of outliers and allows for distinguishing between outliers generated by the return process or by the volatility process, and hence is an improvement over the approach of Choy et al. (2008). In addition, it allows an efficient model implementation using the WinBUGS software. A simulation study is conducted to assess the performance of the proposed approach and its comparison with the approach by Choy et al. (2008). In the empirical study, daily exchange rate returns of the Australian dollar to various currencies and daily stock market index returns of various international stock markets are analysed. Model comparison relies on the Deviance Information Criterion and convergence diagnostic is monitored by Geweke’s convergence test.     

模糊控制在电弧冶炼炉中应用

本文主要针对电弧冶炼在生产实际过程中是一个非线性且存在严重随机干扰的系统,运用常规控制难以取得令人满意的控制效果原情况下,将模糊控制理论运用到当中去,从而使系统得到较好的控制效果和较强的鲁棒性。