Dynamic design and re-design of multi-echelon,multi-product logistics networks with outsourcing opportunities: A computational study

We address the problem of designing/redesigning a multi-echelon logistics network over multiple periods. Strategic decisions comprise opening new facilities and selecting their capacities from a set of available discrete sizes. Capacity expansion may occur more than once over the time horizon both at new locations and at existing facilities. In addition, logistics decisions involving supplier selection, procurement, production, and distribution of multiple products are to be made. The latter also involve the choice of transportation modes with limited capacities. Finally, a strategic choice between in-house manufacturing and a mixed approach with product outsourcing is to be taken. We propose a mixed-integer linear programming model and develop additional inequalities to enhance the original formulation. To gain insight into the complexity of the problem at hand, an extensive computational study is performed with randomly generated instances that are solved with standard mathematical optimization software. Useful managerial insights are derived from varying several parameters and analyzing the impact of different business strategies on various segments of the logistics network.     

A multidisciplinary approach for Model Predictive Control Education: A Lego Mindstorms NXT-based framework

This work introduces an educational framework based on the Lego Mindstorms NXT robotic platform used to outline both the theoretical and practical aspects of the Model Predictive Control (MPC) theory. The framework has been developed in the widely used MatLab/Simulink environment. A two-wheeled inverted pendulum is considered as hands-on experimental scenario. For such a system, starting from its mathematical modeling, an established design methodology is presented aiming to outline step-by-step the predictive controller implementation on a low power architecture. This methodology stress the design of a non-linear MPC controller on a low power embedded system, pruning the designer to deal with hard real time constraints without impacting the overall design requirements. The effectiveness of this multidisciplinary approach is shown through this presentation and demonstrated with experimental results.     

A key management protocol with robust continuity for sensor networks

Robust continuity should be paid much more attention in sensor network key management protocols, especially in privacy-vital environment. However, to our best of knowledge, few papers on key management have addressed the robust continuity in sensor networks. Therefore, in this paper, we present a new key management protocol with robust continuity for sensor networks. The purpose is to minimize the key management redesign effort as well as to make the node flexible and adaptable to many different applications. Compared to existing key management protocols, our proposed protocol has the following merits: First, our protocol addresses the continuity between the pairwise key and group key, it can establish the pairwise key and group key with the same pre-distributed secrets. Second, our protocol can deal with the continuity of the pairwise key and the group key themselves; it can update both of them to support the changes in the size of the network.     

A robust and flexible digital rights management system for home networks

A robust and flexible Digital Rights Management system for home networks is presented. In the proposed system, the central authority delegates its authorization right to the local manager in a home network by issuing a proxy certificate, and the local manager flexibly controls the access rights of home devices on digital contents with its proxy certificate. Furthermore, the proposed system provides a temporary accessing facility for external devices and achieves strong privacy for home devices. For the validation of delegated rights and the revocation of compromised local managers, a hybrid mechanism combining OCSP validation and periodic renewal of proxy certificates is also presented.     

A Survey of Model Predictive Control Methods for Traffic Signal Control

Enhancing traffic efficiency and alleviating (even circumventing) traffic congestion with advanced traffic signal control (TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control (MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations, and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.      

预测控制中的稳态优化策略

预测柠制算法为了在自由度不足时按优先级优先保证基本控制目标，在自由度多余时充分利用自由度提高效益，在算法中引了优化 控制的策略，本文首先介绍和分析了以前对预测控制的改进工作，最后给出了基于目标规划思想的优化策略。     

A synthesis framework for robust gain-scheduling controllers

We present a general framework for the systematic synthesis of robust gain-scheduling controllers by convex optimization techniques and for uncertain dynamical systems described by standard linear fractional representations. We distinguish between linear time-varying parameters, which are assumed to be available online as scheduling parameters for the controller, and genuine uncertainties, not necessarily time-varying, parametric or linear, that are not available online. Under the rough hypothesis that the control channel is not affected by the unmeasurable uncertainties and that the properties of the uncertainties and scheduling variables are captured by suitable families of integral quadratic constraints, this paper reveals how controller synthesis can be turned into a genuine semi-definite program. The design framework is shown to encompass a rich class of concrete scenarios.     

A QFT framework for nonlinear robust stability

The problem of deriving conditions for a stabilizing linear compensator in a uncertain nonlinear control system is addressed, for some types of memoryless nonlinearities like the saturation or the dead zone. The approach is to incorporate to QFT conditions given by the application of harmonic balance and multiplier techniques, providing the designer with a very transparent tool for synthesizing nonlinear stabilizing compensators, balancing between different possible alternatives. Copyright © 2002 John Wiley & Sons, Ltd.     

A system dynamics modeling approach for a multi-level,multi-product,multi-region supply chain under demand uncertainty

It is highly crucial to design a supply chain in a way that determines to what extent each level should direct orders to the next level and, in cases where several next levels exist, which next level should be selected for such transmission of orders. The main significance of the proposed model is to choose an appropriate region for order placement and also investigate when a level in a specific region is disrupted what happens to the other levels in other regions. Also the main impact of this article is pertaining to managerial decisions that if a level in supply chain is shut down and disrupted what they should do Therefore, in the present study, system dynamics approach is used to design a five-level supply chain consisting of retailer, final product distributor, manufacturer, material distributor, and supplier in four different regions by specifying the amount of orders placed by each level at each region as well as the next level at each region that should receive these orders. The developed model is kind of multi-product model in which initial customer demand for any product is a random variable. There is only one entity in each region at every level, except for the number of material distributor(s) and supplier(s) which equals the number of raw material item(s). Finally, a numerical example is presented to discuss the result for normal conditions, oscillating demands, variations in price, changes in costs, and a combination of these variations which may vary depending on changes in the how orders are placed.     

无偏模型预测控制综述

无偏(静差)模型预测控制(Model predictive control, MPC)的设计目标是使被控变量渐近地跟踪设定值, 这类控制方法直接关系到闭环系统的跟踪性能和抗扰性能.由于可以有效处理不可测扰动、模型失配等, 无偏MPC具有很强的工程应用价值, 但是在理论方面并没有得到充分重视.近30年来, 围绕无偏MPC的原理、分析和设计展开了一系列的研究工作, 并取得了系统性的研究成果.当前的一些研究结果大多分散在不同的参考文献中, 缺少全面的梳理和呈现.本文的主要工作包括回顾常见无偏控制方法, 综述当前无偏MPC的研究进展, 并探讨一些潜在的研究方向.     

A robust and high-performance queue management controller for large round trip time networks

Congestion management for transmission control protocol is of utmost importance to prevent packet loss within a network. This necessitates strategies for active queue management. The most applied active queue management strategies have their inherent disadvantages which lead to suboptimal performance and even instability in the case of large round trip time and/or external disturbance. This paper presents an internal model control robust queue management scheme with two degrees of freedom in order to restrict the undesired effects of large and small round trip time and parameter variations in the queue management. Conventional approaches such as proportional integral and random early detection procedures lead to unstable behaviour due to large delay. Moreover, internal model control–Smith scheme suffers from large oscillations due to the large round trip time. On the other hand, other schemes such as internal model control–proportional integral and derivative show excessive sluggish performance for small round trip time values. To overcome these shortcomings, we introduce a system entailing two individual controllers for queue management and disturbance rejection, simultaneously. Simulation results based on Matlab/Simulink and also Network Simulator 2 (NS2) demonstrate the effectiveness of the procedure and verify the analytical approach.     

城市污水处理过程模型预测控制研究综述

城市污水处理过程具有生化反应复杂、非线性、不确定性等特点,在受进水流量、进水组分、天气变化等强干扰作用时,很难保证出水水质维持在规定范围内。模型预测控制以其可应用于非线性系统、显式处理约束问题等优点,近几年在城市污水处理过程中得到了广泛应用。本文从模型预测控制方法在城市污水处理过程中所依托的模型角度出发,介绍了其在基于机理模型和数据驱动的城市污水处理过程中的研究现状,阐述了在不同模型和控制变量下的控制效果。最后提出了城市污水处理过程中仍需解决的问题,并对模型预测控制方法在城市污水处理过程控制中的未来研究方向做出了展望。     

饱和约束系统的鲁棒模型预测控制

针对饱和约束系统提出了一种鲁棒模型预测控制算法,分别考虑了多面体不确定性和结构反馈不确定性.考虑无穷时域的最坏二次性能指标,通过采用带有饱和特性的反馈控制结构,将控制律的求解转化为一个在线的线性矩阵不等式优化问题.初始时刻优化问题的可行性保证了闭环控制系统的鲁棒稳定性.最后的仿真结果说明了算法的优越性.     

一种实用的磨矿多模型预测控制策略研究

房矿过程是选矿过程中的重要一环,属多输入多输出系统,并存在严重的扰动变量(诸如硬度等)和时变参数.介绍了一种磨矿控制系统中实用的多变量动态矩阵控制的多模型控制策略.该方法基于不同的矿石硬度对球磨过程建立不同的阶跃模型进行多模型预测控制.仿真证实了该方法的有效性.     

Model Predictive Control for Stochastic Resource Allocation

In this paper, we consider a class of stochastic resource allocation problems where resources assigned to a task may fail probabilistically to complete assigned tasks. Failures to complete a task are observed before new resource allocations are selected. The resulting temporal resource allocation problem is a stochastic control problem, with a discrete state space and control space that grow in cardinality exponentially with the number of tasks. We modify this optimal control problem by expanding the admissible control space, and show that the resulting control problem can be solved exactly by efficient algorithms in time that grows nearly linear with the number of tasks. The approximate control problem also provides a bound on the achievable performance for the original control problem. The approximation is used as part of a model predictive control (MPC) algorithm to generate resource allocations over time in response to information on task completion status. We show in computational experiments that, for single resource class problems, the resulting MPC algorithm achieves nearly the same performance as the optimal dynamic programming algorithm while reducing computation time by over four orders of magnitude. In multiple resource class experiments involving 1000 tasks, the model predictive control performance is within 4% of the performance bound obtained by the solution of the expanded control space problem.     

Hybrid Fuzzy Modelling for Model Predictive Control

Model predictive control (MPC) has become an important area of research and is also an approach that has been successfully used in many industrial applications. In order to implement a MPC algorithm, a model of the process we are dealing with is needed. Due to the complex hybrid and nonlinear nature of many industrial processes, obtaining a suitable model is often a difficult task. In this paper a hybrid fuzzy modelling approach with a compact formulation is introduced. The hybrid system hierarchy is explained and the Takagi–Sugeno fuzzy formulation for the hybrid fuzzy modelling purposes is presented. An efficient method for identifying the hybrid fuzzy model is also proposed. A MPC algorithm suitable for systems with discrete inputs is treated. The benefits of the MPC algorithm employing the hybrid fuzzy model are verified on a batch-reactor simulation example: a comparison between the proposed modern intelligent (fuzzy) approach and a classic (linear) approach was made. It was established that the MPC algorithm employing the proposed hybrid fuzzy model clearly outperforms the approach where a hybrid linear model is used, which justifies the usability of the hybrid fuzzy model. The hybrid fuzzy formulation introduces a powerful model that can faithfully represent hybrid and nonlinear dynamics of systems met in industrial practice, therefore, this approach demonstrates a significant advantage for MPC resulting in a better control performance.     

MIMO系统的多模型预测控制

针对非线性多变量系统提出一种多模型预测控制(MMPC)策略.首先给出一种多模型辨识方法,利用模糊满意聚类算法将复杂非线性系统划分为若干子系统,并获得多个线性模型,通过模型变换得出全局系统模型,接着对全局MIMO系统设计MMPC,并进行了系统的性能分析,最后以pH中和过程为例,通过仿真研究验证了辨识和控制算法的有效性.     

预测函数控制的内模控制理论

内模控制是一种基于过程数学模型进行控制器设计的新型控制策略,是研究预测控制重要的理论基础。预测函数控制是一种控制量计算方程简单,实时控制效果好的新型预测控制算法。本文用内模控制理论研究预测函数控制,分析了系统的鲁棒性和稳定性,最后进行了参数设计和仿真研究。     

A framework for robust parametric set membership identification

This paper proposes a new framework for studying robust parametric set membership identification. The authors derive some new results on the fundamental limitations of algorithms in this framework, given a particular model structure. The new idea is to quantify uncertainty only with respect to the (finite dimensional) parametric part of the model and not the (fixed size) unmodeled dynamics. Thus, the measure of uncertainty is different from the measures used in previous robust identification work where system norms are used to quantify uncertainty. As an example, the results are used to assess the fidelity of a certain approximate robust parametric set membership identification algorithm