Joint replenishment and manufacturing activities control in a two stage unreliable supply chain

This paper deals with dynamic stochastic situations faced by supply chains. In this context, various interactions, disparate decisions and random phenomena must be considered. These issues are considered in this paper through a two stage supply chain control problem. The supplier and the transformation stage are both subject to random events such as periods of unavailability due to internal difficulties or market constraints. Our objective is to find information sharing control policies for the supply and production activities that minimises the expected discounted cost of ordering, inventories/backlog and transformation over an infinite horizon. This is an optimal control problem with state constraints and hybrid dynamics of the production and replenishment activities. It is shown that, from a mathematical point of view, the considered problem is difficult to tackle and it calls upon optimal and impulsive control theory notions. A dynamic stochastic model is thus proposed. The existence of an optimal control policy and Hamilton-Jacobi-Bellman optimality condition in terms of the value function of the problem are derived and discussed. A numerical schema is then proposed to solve the obtained optimality conditions equations. A complete control policy is finally developed. The confirmation of such a policy structure is illustrated through sensitivity analysis. Some particular cases are also presented and discussed.     

基于随机平均的非线性随机最优控制

首先建立非线性随机系统的最优控制问题,并介绍通过随机平均法导出平均系统、再由随机动态规划原理确定控制律的平均系统的非线性随机最优控制方法。然后,对于非线性随机系统的动态规划方程,提出应用随机平均法简化该方程、从而得到最优平均控制律的方法,并证明该最优平均控制律等价于平均系统的最优控制律。最后用一个例子说明方法及等价性,并指出在一定条件下,最优平均控制律将是动态规划方程的精确解。     

一类非线性脉冲发展方程的最优反馈控制

本文考虑受控系统为Banach空间中一类G0紧半群的非线性脉冲积微分方程的最优反馈控制问题。首先证明容许控制对的存在性,进一步对Lagrange问题给出了一个新的存在性定理。     

A physical approach to structural stochastic optimal controls

The generalized density evolution equation proposed in recent years profoundly reveals the intrinsic connection between deterministic systems and stochastic systems by introducing physical relationships into stochastic systems. On this basis, a physical stochastic optimal control scheme of structures is developed in this paper, which extends the classical stochastic optimal control methods, and can govern the evolution details of system performance, while the classical stochastic optimal control schemes, such as the LQG control, essentially hold the system statistics since there is still a lack of efficient methods to solve the response process of the stochastic systems with strong nonlinearities in the context of classical random mechanics. It is practically useful to general nonlinear systems driven by non-stationary and non-Gaussian stochastic processes. The celebrated Pontryagin’s maximum principles is employed to conduct the physical solutions of the state vector and the control force vector of stochastic optimal controls of closed-loop systems by synthesizing deterministic optimal control solutions of a collection of representative excitation driven systems using the generalized density evolution equation. Further, the selection strategy of weighting matrices of stochastic optimal controls is discussed to construct optimal control policies based on a control criterion of system second-order statistics assessment. The stochastic optimal control of an active tension control system is investigated, subjected to the random ground motion represented by a physical stochastic earthquake model. The investigation reveals that the structural seismic performance is significantly improved when the optimal control strategy is applied. A comparative study, meanwhile, between the advocated method and the LQG control is carried out, indicating that the LQG control using nominal Gaussian white noise as the external excitation cannot be used to design a reasonable control system for civil engineering structures, while the advocated method can reach the desirable objective performance. The optimal control strategy is then further employed in the investigation of the stochastic optimal control of an eight-storey shear frame. Numerical examples elucidate the validity and applicability of the developed physical stochastic optimal control methodology.     

Minimax optimal control of uncertain quasi-integrable Hamiltonian systems with time-delayed bounded feedback

A minimax optimal control strategy for uncertain quasi-integrable Hamiltonian systems with time-delayed bounded feedback control is proposed. First, a quasi-integrable Hamiltonian system with time-delayed bounded control forces and uncertain excitation and system parameters is converted into a set of Itô stochastic differential equations without time delay. Then, the partially averaged Itô stochastic differential equations for the energy processes are derived by using the stochastic averaging method for quasi-integrable Hamiltonian systems. For these equations together with an appropriate performance index, a worst-case optimal control strategy is derived via solving a stochastic differential game problem. The worst-case disturbances and the optimal bounded controls are obtained by solving a Hamilton–Jacobi–Isaacs (HJI) equation. Finally, two examples are worked out in detail to illustrate the application and effectiveness of the proposed method.     

地震激励邻接高层建筑的随机最优控制

采用控制设备联接相邻的高层建筑以降低其地震响应是一个切实有效的方法。基于随机动态规划原理与随机平均法,提出耦合相邻高层建筑的随机最优控制方法。先建立任意层数并在任意层高处控制联接的耦合结构的缩聚模型,再运用随机平均法导出关于模态能量的oIt随机微分方程,应用随机动态规划原理建立动态规划方程,由此可确定最优控制律。将结构的响应控制化为模态能量控制,缩减控制系统的维数。用高斯随机过程模拟地震激励,可计及其功率谱特性。数值结果表明该耦合结构随机最优控制方法的有效性。     

带漂移因子及停时的最优脉冲随机控制问题

通过把漂移参数引入到受控于Poisson过程的状态结构中,本文建立了一非对称型最优脉冲随机控制模型。在此模型的目标函数中,首次引进了停时因素。利用随机积分及脉冲控制理论,我们不但给出了最优回报函数应满足的充分性条件,而且在一定条件下得出了其显解及相应的最优控制策略。     

Optimal search of distributed arrival time control

Distributed arrival time control (DATC) is a heuristic feedback control algorithm for scheduling which has been developed for a real-time distributed scheduling of heterarchical systems. It has been renowned not only for its fast solution searching algorithm but also for its flexibility to changing environment. However, the optimality of this heuristic method has not been analytically explained until recently because it has been designed to discover a near optimal solution instead of the true optimum. In this paper, we provide a novel optimal search method for the DATC scheduling problem by introducing a scalar cost function over the vector space of time and show the existence and location of true optima for the DATC scheduling problem through geometric approach. This geometrical interpretation enables us to find the true optimal by direct projection without iterations like previous DATC approaches. Based on the true optimum found, we evaluate the optimality of DATC algorithms by examining their dependency on initial conditions and explain their intrinsic causality mechanism for the discrepancy with true optimum. The implication of this study is on the new viewpoint over the vector space of DATC, which not only solves the optimality issue of DATC but also provides a new direction of direct search approach like projection method for the true optimum.     

球对称压电壳的随机最优控制

分析研究球对称压电壳在边界随机激励下的最优控制问题。给出压电壳的机电动力学方程、应力和电位移表达式,建立其随机最优控制问题方程;通过电势积分转化为机械振动控制方程。通过位移变换和Galerkin法,导出关于模态位移的多自由度振动最优控制方程。根据随机动态规划原理,建立HJB方程,得到压电壳的最优控制电势;并给出受控壳系统的频响函数、响应谱密度和相关函数等表达式,以计算其随机响应。最后给出数值结果,显示压电壳的随机最优控制效果。     

Optimal stocking strategies for inventory mechanism with a stochastic short-term price discount and partial backordering

Price discount is an important research topic in the field of inventory management. The existing research on this topic mainly considers fixed price discount, but ignores the situation in which stochastic short-term price discount may be involved. In this paper, we study an inventory problem considering stochastic short-term price discount and partial backordering. To address this problem, we propose an optimal replenishment and stocking model to maximise the retailers' profit. After that, a cost–benefit analysis-based heuristic method for solving the developed model is presented by considering two scenarios depending on whether a replenishment point belongs to a discount period or not. Furthermore, an algorithm is provided to elicit an optimal ordering policy from multiple solutions derived from the given heuristic solution method. Finally, a real case is offered to demonstrate the application of the proposed model, followed by a sensitivity analysis. The results indicate that a retailer can identify the optimal replenishment policy with the aim of achieving maximal profit in situations where stochastic short-term price discount and partial backordering are considered for certain inventory problems at hand. In addition, sensitivity analysis illustrates a fact that different values of the introduced parameters may influence the optimal replenishment policy.     

Time-delay stochastic optimal control and stabilization of quasi-integrable Hamiltonian systems

Innovative procedures for the time-delay stochastic optimal control and stabilization of quasi-integrable Hamiltonian systems subject to Gaussian white noise excitations are proposed. First, the problem of time-delay stochastic optimal control of quasi-integrable Hamiltonian systems is formulated and converted into the problem of stochastic optimal control without time delay. Then the converted control problem is solved by applying the stochastic averaging method for quasi-integrable Hamiltonian systems and the stochastic dynamical programming principle. The time-delay feedback stabilization of quasi-integrable Hamiltonian systems is formulated as an ergodic control problem with an un-determined cost function which is determined later by minimizing the largest Lyapunov exponent of the controlled system. As an example, a two-degree-of-freedom quasi-integrable Hamiltonian system with time-delay feedback control forces is investigated in detail to illustrate the procedures and their effectiveness.     

Optimal minimum variance‐entropy control of tumour growth processes based on the Fokker–Planck equation

The authors demonstrated an optimal stochastic control algorithm to obtain desirable cancer treatment based on the Gompertz model. Two external forces as two time‐dependent functions are presented to manipulate the growth and death rates in the drift term of the Gompertz model. These input signals represent the effect of external treatment agents to decrease tumour growth rate and increase tumour death rate, respectively. Entropy and variance of cancerous cells are simultaneously controlled based on the Gompertz model. They have introduced a constrained optimisation problem whose cost function is the variance of a cancerous cells population. The defined entropy is based on the probability density function of affected cells was used as a constraint for the cost function. Analysing growth and death rates of cancerous cells, it is found that the logarithmic control signal reduces the growth rate, while the hyperbolic tangent–like control function increases the death rate of tumour growth. The two optimal control signals were calculated by converting the constrained optimisation problem into an unconstrained optimisation problem and by using the real–coded genetic algorithm. Mathematical justifications are implemented to elucidate the existence and uniqueness of the solution for the optimal control problem.Inspec keywords: optimal control, genetic algorithms, cancer, Fokker‐Planck equation, cellular biophysics, stochastic systems, probability, tumours, entropy, medical control systemsOther keywords: cancer treatment, Gompertz model, time‐dependent functions, process input signals, external treatment agents, tumour growth rate, constrained optimisation problem, cost function, cancerous cells population, probability density function, logarithmic control signal, Fokker‐Planck equation, tumour growth process, optimal control signals, optimal control problem, optimal minimum variance‐entropy control, optimal stochastic control algorithm, tumour death rates, hyperbolic tangent‐like control function, unconstrained optimisation problem, real‐coded genetic algorithm     

Robust optimal Robin boundary control for the transient heat equation with random input data

The problem of robust optimal Robin boundary control for a parabolic partial differential equation with uncertain input data is considered. As a measure of robustness, the variance of the random system response is included in two different cost functionals. Uncertainties in both the underlying state equation and the control variable are quantified through random fields. The paper is mainly concerned with the numerical resolution of the problem. To this end, a gradient‐based method is proposed considering different functional costs to achieve the robustness of the system. An adaptive anisotropic sparse grid stochastic collocation method is used for the numerical resolution of the associated state and adjoint state equations. The different functional costs are analysed in terms of computational efficiency and its capability to provide robust solutions. Two numerical experiments illustrate the performance of the algorithm. Copyright © 2016 John Wiley & Sons, Ltd.     

邻接高耸结构的随机最优控制

文基于随机动态规划原理与随机平均法，提出耦合相邻高耸结构的随机最优控制方法。先建立任意层数并在任意层高处控制联接的耦合结构的缩聚模型，再运用随机平均法导出关于模态能量的It6随机微分方程，应用随机动态规划原理建立动态规划方程，由此可确定最优控制律。将结构的响应控制化为模态能量控制，缩减控制系统的维数。用高斯随机过程模拟地震激励，可计及其功率谱特性。数值结果表明该耦合结构控制方法的有效性。     

漂浮基双臂空间机器人基于自适应遗传算法的最优非完整运动规划

讨论了漂浮基双臂空间机器人非完整运动的最优控制规划问题。以多体动力学理论为基础,导出了载体位置、姿态均不受控制情况下,双臂空间机器人耦合了系统动量守恒关系的系统动量矩守恒关系,并将其转化为控制系统状态方程。由此,将双臂空间机器人的非完整运动规划问题转化为非线性系统的控制问题。以此为基础,利用近似优化的方法,给出了双臂空间机器人基于自适应遗传算法的最优非完整运动规划控制算法。其优点在于,仅需要控制双臂空间机器人机械臂的关节运动,即可达到对载体姿态及机械臂关节位置的双重控制效果。一个平面双臂空间机器人系统的数值仿真,验证了算法的有效性。     

结构振动控制系统最优控制力及控制作用最优分布的一般算法

基于随机最优控制的分离原理,在独立的模态空间中,得出了结构振动控制系统最优控制力、控制作用最优分布、结构振动控制系统受控响应、结构振动控制系统控制溢出等问题的一般算法,阐明了这些问题对应用研究及工程实践的理论意义。     

宽带激励下非线性振动系统响应的最小化控制

将一种基于广义谐和函数的随机平均法和随机动态规划原理相结合，提出了一种非线性随机最优控制方法，可以为受宽带激励的单自由度强非线性振动系统设计最优控制规律，以使得系统的稳态响应最小化。方法中的随机平均法用来得到受控系统位移幅值的Ito随机微分方程；用随机动态规划原理为系统稳态响应最小化建立动态规划方程；在控制力为有界的条件下，从动态规划方程中可以导出最优控制规律；通过求解FPK方程得到受控系统的响应。本文用一个具体的例子阐述了这一控制方法的实施过程。     

CIR模型下保险公司最优投资再保险策略研究

本文主要研究Cox-Ingersoll-Ross（CIR）随机利率模型下保险公司的最优投资和再保险问题．假设保险公司投资于金融市场中的无风险资产、零息债券和多种股票．此外保险公司购买比例再保险合约以转移承保风险．模型中,我们用仿射过程刻画随机利率,通过扩散过程模拟保险公司盈余过程,即用连续过程近似跳过程．保险公司的目标是通过保险投资最大化终端财富的期望幂效用．由于保险公司的财富过程不是自融资过程,在求解过程中,我们先将原优化问题转化为自融资问题,通过随机最优控制方法导出相应的HJB方程,进而得到最优投资、再保险策略和幂效用函数下的最优值函数．我们发现随着风险厌恶系数的增大,公司投资于股票的比例会降低,初始利率越高,保险公司终端财富的值函数越大．最后,我们给出了保费率、利率参数和风险厌恶系数对投资策略、投资效用的敏感性分析．     

航天器太阳帆板伸展过程最优控制的粒子群优化算法

讨论航天器太阳帆板伸展过程中航天器姿态运动的最优控制问题。利用多体动力学方法导出带太阳帆板航天器姿态运动方程。在系统角动量为零的情况下,带太阳帆板航天器系统的姿态运动控制问题可转化为无漂移系统的非完整运动规划问题。在非完整运动规划中引入粒子群优化算法,通过控制太阳帆板伸展运动可同时获得航天器姿态的期望位形。数值仿真表明,该方法对太阳帆板伸展过程中航天器主体姿态控制是有效的。     

A two-phase computational scheme for solving bang-bang control problems

This paper focuses on numerical methods for solving time-optimal control problems using discrete-valued controls. A numerical Two-Phase Scheme, which combines admissible optimal control problem formulation with enhanced branch-and-bound algorithms, is introduced to efficiently solve bang-bang control problems in the field of engineering. In Phase I, the discrete restrictions are relaxed, and the resulting continuous problem is solved by an existing optimal control solver. The information on switching times obtained in Phase I is then used in Phase II wherein the discrete-valued control problem is solved using the proposed algorithm. Two numerical examples, including a third-order system and the F-8 fighter aircraft control problem, are presented to demonstrate the use of this proposed scheme. Comparing to STC and CPET methods proposed in the literature, the proposed scheme provides a novel method to find a different switching structure with a better minimum time for the F-8 fighter jet control problem.