基于统计过程控制技术的项目管理可视化控制系统

概述IMCS-SPC模型的总体框架、组成部分以及两大核心技术(多变量统计过程控制技术和虚拟现实可视化技术);详细讨论项目管理者构建IMCS-SPC模型的基本步骤。作为一种采用多元统计分析进行过程控制的可视化决策支持系统,IMCS-SPC为项目管理者提供了一种十分清晰、直观的过程控制方法和工具,可有效提高项目管理效率,以确保项目过程的正常运行。     

两指标双F统计过程控制图

当产品质量指标服从二元正态分时,可用T2控制图与Λ控制图联合判断产品生产的过程是否处于受控状态。本文利用T2统计量与F统计量、Λ统计量与F统计量之间的关系,得到了两指标情形下两类基于F分布统计量的统计过程控制图,简称双F统计过程控制图,并给出了控制图应用实例。     

考虑突发事件状态转移的政府应急物资采购定价模型

突发事件发生后,应急物资需求量呈现爆发式增长,政府首先将常规物资调拨至受灾区,并根据阶段性救灾成果作出应急物资采购决策。突发事件状态总是不断发生转移,使得政府已采购的物资在状态好转时容易造成浪费,在状态持续恶化时又不足以满足突发需求,因此考虑突发事件状态转移情形的应急物资采购定价策略对提升政府应急物资保障能力尤为关键。为此,本研究引入数量柔性契约到政府与应急物资供应商组成的两级应急物资采购供应链,构建了基于突发事件状态转移的应急物资采购定价模型,推导得出政企达成合作的条件与双方最优决策策略,并对比分析了契约合作与分散非合作下的供应商利润与政府成本。进一步采用数值计算与敏感性分析验证该模型的有效性,讨论了若干外生变量对政企最优决策与双方成本收益的影响,提出重要的管理启示。研究表明,考虑突发事件状态转移的基于数量柔性契约的政府应急物资采购定价模型既可以有效提高应急物资储备水平,又能保障供应商的合理收益以及控制政府成本,实现了政企双赢。     

大庆油田物资采购及库存优化机制设计与模型研究

本文结合大庆油田物资采购中的实际问题, 考虑物资市场、需求、库存三方之间的不确定性和复杂性,分别讨论了物资采购价格时变、物资需求时变、以及不同仓储容量限制下的库存优化模型的研究进展。进一步,设计了针对大庆油田物资的采购及库存优化机制,并选取大庆油田实际采购中的4种A类物资,基于时间序列方法和0-1混合整数规划,分别对机制中的价格预测部分和策略优化部分进行了数值试算,结果表明,基于准确度较高的预测价格,运用混合0-1整数规划模型制定的多品种物资的最优联合采购策略,可以实现采购成本的节省,相比于4种物资2009年的实际采购成本,节约比率高达7.66%,同时价格预测的精度也得到了用户的认可。该机制为油田物资采购和库存优化管理项目中的辅助决策支持系统原型设计提供了参照。但考虑到大庆油田实际采购中的各种复杂因素的影响,还需进一步完善该优化机制,并对相关模型进行改进。     

三种物资同时段需求的EOQ模型

本文研究了三种物资同时段需求的EOQ模型,从理论以及实例均说明了该模型相对单一物资的EOQ模型,能够缩小这三种物资对仓库的占用空间,该模型在实际问题中要求需求和采购价格均随着时间的变化而变化,通过算法计算得到了满足三种物资同时段需求的最佳采购次数,最后得出了所要建立满足需求的最小仓库容量．     

一类半参数GARCH-M模型的统计检验

半参数GARCH-M模型能够对资本市场的相对风险厌恶进行度量,本文利用经验似然方法对风险厌恶是否受到外生变量的影响进行检验。论文首先讨论了模型的参数估计及其渐近性质,其次利用估计方程构造检验统计量并证明其渐近服从卡方分布,最后进行了数值模拟。结果表明估计精度较高,检验统计量对备择假设比较敏感。     

整批间隔进货的存储量控制模型与随机局部弹性

利用作者于文[1，2]引入的随机弹性理论，研究了整批间隔进货并不允许缺货的随机存储模型中，一个采购周期内的总费用对最高存储量的弹性．给出了总费用弹性概率密度函数的一般表达式．进一步给出了存在多种需求物资的随机存储模型中总费用对单个物资采购量的弹性分布函数和概率密度函数，讨论了随着采购量的随机变化，总费用的弹性变化规律和变化范围．通过实例研究了当最高库存量的分布特性已知且采购量服从某一分布时，总费用的弹性变化范围及在该变化范围的可信度．     

采购方主导的基于能力期权契约的应急物资采购模型

设计了一个基于能力期权契约的应急物资采购模型,政府作为唯一的采购方期初向一个合约供应商采购实物储备量,并向供应商购买一定量的生产能力期权,一旦在期权执行期内有应急物资需求发生,可立即将供应商储备的生产能力转化为实物产品.通过推导和算例模拟证明,这样的采购机制在大量减少政府应急物资实物储备量的同时,降低了政府的缺货风险.同时,也合理补偿了灾后供应商被安排突击生产应急物资发生的成本,应急物资供应链实现了协调,供和需双方实现了双赢.     

基于信息化技术的物资采购创新研究

物资采购是任何组织发展的中心环节,有效及时的物资采购直接影响着组织内部各种活动的正常开展。现代社会信息技术在物资采购领域的应用缩短了采购时间,提高了采购效率,推动了物资采购管理的创新。本文通过分析信息技术对物资采购的影响,为如何有效利用信息技术提出建议。     

基于供应方生产能力的应急物资生产模型

利用期权契约所建立的政企合作储备应急物资模式能够有效解决政府单独储备模式所造成的物资储备量过少或过多而引起的困境。然而由于应急物资的需求特性,若应急物资供应企业采用按单生产方式安排生产储备计划,势必会造成库存水平升高,引发资金周转困难等问题,对政企之间的长期合作造成不利影响。基于此,本文设计了基于供应方生产能力的应急物资生产模型。该模型在政府利用批发价格契约与期权契约采购应急物资的基础上,研究了供应方根据自身生产能力进行柔性生产时的生产与储备问题。通过推导政企双方最优决策后,重点分析了期权权利金,执行价格,加急生产成本等参数对供应方生产决策的影响,并证明与按单生产模式相比,柔性生产模式可有效降低供应方的库存量与生产成本,提高其利润,继而提高整体供应链的利润水平,有助于促进政企之间长期稳定的合作。     

Optimal feedback control of bilinear systems

Feedback synthesis of optimal constrained controls for single-input bilinear systems is considered. Quadratic cost functionals (with and without quadratic control penalization) are modified by the inclusion of additional nonnegative state penalizing functions in the respective cost integrands. The latter functions are chosen so as to regularize the problems, in the sense that feedback solutions of particularly simple form are obtained. Finite and infinite time horizon problem formulations are treated, and associated aspects of feedback stabilization of bilinear systems are discussed.     

Computational Method for Time-Optimal Switching Control

An efficient algorithm, called the time-optimal switching (TOS) algorithm, is proposed for the time-optimal switching control of nonlinear systems with a single control input. The problem is formulated in the arc times space, arc times being the durations of the arcs. A feasible switching control, or as a special case bang-bang control, is found using the STC method previously developed by the authors to get from an initial point to a target point with a given number of switchings. Then, by means of constrained optimization techniques, the cost being considered as the summation of the arc times, a minimum-time switching control solution is obtained. Example applications of the TOS algorithm involving second-order and third-order systems are presented. Comparisons are made with a well-known general optimal control software package to demonstrate the efficiency of the algorithm.     

Bang-bang control of a plant with one positive real pole and one negative real pole

In this paper, a relay-controlled plant, possessing one positive real pole and one negative real pole, is studied. First, a regulating system is considered; we investigate the design of the controller for simultaneous reduction of error and error derivative to zero. It is seen that this can be achieved provided the initial values of error and error derivative fall inside an admissible (or controllable) region. Attention is then focused on the tracking system. It is shown that, provided the input to the system is of a form similar to the impulse response of the plant, and provided the initial values of error and error derivative of the system fall inside the same admissible region, then the controller used for regulating the system can also be used for tracking the system so as to force the output of the plant to follow the input perfectly; in both cases with at mostone switching reversal of the relay.     

Efficient robust optimization for robust control with constraints

This paper proposes an efficient computational technique for the optimal control of linear discrete-time systems subject to bounded disturbances with mixed linear constraints on the states and inputs. The problem of computing an optimal state feedback control policy, given the current state, is non-convex. A recent breakthrough has been the application of robust optimization techniques to reparameterize this problem as a convex program. While the reparameterized problem is theoretically tractable, the number of variables is quadratic in the number of stages or horizon length N and has no apparent exploitable structure, leading to computational time of per iteration of an interior-point method. We focus on the case when the disturbance set is ∞-norm bounded or the linear map of a hypercube, and the cost function involves the minimization of a quadratic cost. Here we make use of state variables to regain a sparse problem structure that is related to the structure of the original problem, that is, the policy optimization problem may be decomposed into a set of coupled finite horizon control problems. This decomposition can then be formulated as a highly structured quadratic program, solvable by primal-dual interior-point methods in which each iteration requires time. This cubic iteration time can be guaranteed using a Riccati-based block factorization technique, which is standard in discrete-time optimal control. Numerical results are presented, using a standard sparse primal-dual interior point solver, that illustrate the efficiency of this approach.     

Finding candidate singular optimal controls: A state of the art survey

Pontryagin's maximum principle gives no information about a singular optimal control if the problem is linear. This survey shows how candidate singular optimal controls may be found for linear and nonlinear problems. A theorem is given on the maximum order of a linear singular problem.This paper is based in part on the research undertaken by the author at the Hatfield Polytechnic, Hatfield, Hertfordshire, England, for the Ph.D. Degree.     

Model-based and model-free control of flexible-link robots: A comparison between representative methods

The paper presents a comparative study on representative methods for model-based and model-free control of flexible-link robots. Model-based techniques for the control of flexible-link robots can come up against limitations when an accurate model is unavailable, due to parameters uncertainty or truncation of high order vibration modes. On the other hand, several research papers argue that suitable model-free control methods result in satisfactory performance of flexible-link robots. In this paper two model-free approaches of flexible-link robot control are examined: (i) energy-based control, and (ii) neural adaptive control. The performance of the aforementioned methods is compared to the inverse dynamics model-based control, in a simulation case study for planar 2-DOF manipulators.     

Time-optimal control of multivariable systems near the origin

This article deals with bang-bang solutions of linear time-optimal control problems. Linear multivariable systems are considered which have one or more control components. It is shown in which way the control components act together to make the system achieve the ultimate aim (namely, the origin in the state space) as quickly as possible. The theory only applies to initial positions sufficiently near the origin. Criteria are given which give the number of switches per control component.Asymptotic dependences of the switching times and the final time on the distance of the initial position from the origin are established. The theory provides a numerical procedure to calculate the time-optimal control. These calculations are very simple. Basic to the proof of these results is a generalized implicit function theorem due to Artin (Ref. 1).     

The Kumar-Becker-Lin scheme revisited

The Kumar-Becker-Lin scheme introduces a slowly vanishing cost bias in the parameter estimation part of self-tuning control in order to improve its performance. This paper establishes the a.s. optimality of a variant of this scheme for Markov chains on a countable state space when the action space is compact metric and the parameter space is a compact subset ofR ^m .     

Applications of fuzzy logic in the control of robotic manipulators

Fuzzy logic has been utilized at several hierarchical levels of a typical robotic control system. Four broad levels of application may be identified - task design, system monitoring (including self-tuning and self-organization), information filtering and preprocessing, and in-loop direct control. Even though the need for fuzzy logic is felt mostly at upper levels of the control system, the present applications are mainly concentrated within the lowest level, perhaps driven by convenience rather than necessity. This paper surveys several applications of fuzzy logic in the control of robotic manipulators. Applications are grouped into four hierarchical categories, broadly corresponding to an existing architecture of a robotic control system. Such a classification can be beneficial in ascertaining the appropriateness of fuzzy logic for the specific control task.     

A data-driven switching control approach for braking systems with constraints

Braking control is of paramount importance in guaranteeing driving safety and comfort, but it is a well-known challenging task, due to the highly nonlinear and road condition-dependent behavior of the vehicle. Existing braking controllers typically rely on accurate models of the vehicle dynamics and the vehicle–road interaction, which are quite difficult to be retrieved in practice. In the wake of the data-driven control paradigm, we propose a model-free and fully data-based braking control method. The architecture of our scheme is two-layered, featuring: an inner switching controller, directly designed from data to match a given closed-loop behavior, and an outer predictive reference governor, exploited to enforce constraints and possibly improve the overall braking performance. The effectiveness of the approach is shown in a simulation environment, by providing a sensitivity analysis to the main tuning knobs of the method.