Group decision making with incomplete information: a dominance and quasi‐optimality volume‐based approach using Monte‐Carlo simulation

In this paper, we present a comprehensive framework for multiattribute group decision making considering that neither information about individual preferences nor the importance of individual decision makers for the group is available in exact form. We study several different forms of incomplete preference information, including a ranking of attribute weights, ranking of values of alternatives in each attribute, and ranking of value differences. Our approach is based on relative volumes in parameter space and allows for probabilistic statements about different results, including optimality or quasi‐optimality of alternatives, or relations between alternatives.     

Infinite horizon linear quadratic optimal control for discrete‐time stochastic systems

This paper is concerned with the infinite horizon linear quadratic optimal control for discrete‐time stochastic systems with both state and control‐dependent noise. Under assumptions of stabilization and exact observability, it is shown that the optimal control law and optimal value exist, and the properties of the associated discrete generalized algebraic Riccati equation (GARE) are also discussed. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

A simulation‐based algorithm for optimal pricing policy under demand uncertainty

We propose a simulation‐based algorithm for computing the optimal pricing policy for a product under uncertain demand dynamics. We consider a parameterized stochastic differential equation (SDE) model for the uncertain demand dynamics of the product over the planning horizon. In particular, we consider a dynamic model that is an extension of the Bass model. The performance of our algorithm is compared to that of a myopic pricing policy and is shown to give better results. Two significant advantages with our algorithm are as follows: (a) it does not require information on the system model parameters if the SDE system state is known via either a simulation device or real data, and (b) as it works efficiently even for high‐dimensional parameters, it uses the efficient smoothed functional gradient estimator.     

Necessary and sufficient conditions of risk‐sensitive optimal control and differential games for stochastic differential delayed equations

In this paper, we consider risk‐sensitive optimal control and differential games for stochastic differential delayed equations driven by Brownian motion. The problems are related to robust stochastic optimization with delay due to the inherent feature of the risk‐sensitive objective functional. For both problems, by using the logarithmic transformation of the associated risk‐neutral problem, the necessary and sufficient conditions for the risk‐sensitive maximum principle are obtained. We show that these conditions are characterized in terms of the variational inequality and the coupled anticipated backward stochastic differential equations (ABSDEs). The coupled ABSDEs consist of the first‐order adjoint equation and an additional scalar ABSDE, where the latter is induced due to the nonsmooth nonlinear transformation of the adjoint process of the associated risk‐neutral problem. For applications, we consider the risk‐sensitive linear‐quadratic control and game problems with delay, and the optimal consumption and production game, for which we obtain explicit optimal solutions.     

A multiple-criteria decision problem for optimal management of farm resources under uncertainty - a case study

An attempt is made to minimize the total operational cost and specific energy for rice cultivation, when the constraints relating to the availability of power sources and crop yield are supposed to be stochastic in nature. The Sequential Linear Goal Programming algorithm has been used to solve the resulting multiple objective optimization problem.     

A mixed numerical–analytical stable pseudo‐inversion method aimed at attaining an almost exact tracking

This paper considers the problem of computing the input u(t) of an internally asymptotically stable, possibly non‐minimum phase, linear, continuous time system Σ yielding a very accurate tracking of a pre‐specified desired output trajectory . The main purpose of the new approach proposed here is to alleviate some limitations that inherent the classical methods developed in the framework of the preview‐based stable inversion, which represents an important reference context for this class of control problems. In particular, the new method allows one to deal with arbitrary and possibly uncertain initial conditions and does not require a pre‐actuation. The desired output to be exactly tracked in steady state is here assumed to belong to the set of polynomials, exponential, and sinusoidal time functions. The desired transient response is specified to obtain a fast and smooth transition toward the steady‐state trajectory , without under and/or overshoot in the case of a set point reset. The transient control input u_t(t) is a priori assumed to be given by a piecewise polynomial function. Once has been specified, this allows the computation of the unknown u_t(t) as the approximate least squares solution of the Fredholm's integral equation corresponding to the explicit formula of the output forced response. The steady‐state input u_s(t) is analytically computed exploiting the steady‐state output response expressions for inputs belonging to the same set of . Copyright © 2013 John Wiley & Sons, Ltd.     

A priori error estimates of a meshless method for optimal control problems of stochastic elliptic PDEs

In this paper, we study the optimal control problems of stochastic elliptic equations with random field in its coefficients. The main contributions of this work are two aspects. Firstly, a meshless method coupled with the stochastic Galerkin method is investigated to approximate the control problems, which is competitive for high-dimensional random inputs. Secondly, a priori error estimates are derived for the solutions to the control problems. Some numerical tests are carried out to confirm the theoretical results and to demonstrate the efficiency of the proposed method.     

Linear−quadratic optimal control and nonzero‐sum differential game of forward−backward stochastic system

An existence and uniqueness result for one kind of forward–backward stochastic differential equations with double dimensions was obtained under some monotonicity conditions. Then this result was applied to the linear‐quadratic stochastic optimal control and nonzero‐sum differential game of forward–backward stochastic system. The explicit forms of the optimal control and the Nash equilibrium point are obtained respectively. We note that our method is effective in studying the uniqueness of Nash equilibrium point. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Selection of optimal electronic toll collection system for India: A subjective-fuzzy decision making approach

Present study deals with the adoption of newer technologies for developing nations. Most of the developing countries due to lack of resources perform techno-socio-economic analyses on the already existing models of the developed ones. Such adopted technologies may not perform effectively because of unlike socio-economic factors. Hence, it becomes important to select new technologies based on appropriate and suitable criteria with respect to a particular country. In this paper, we have demonstrated selection of optimal electronic toll collection (ETC) system for India. In this context, we have considered thirteen crucial parameters for selection of appropriate ETC system. Cost is found to be the pivotal selection criterion in India. Further, fuzzy logic based MADM (multiple attribute decision making) approach is employed for selection of optimal ETC system for India. RFID-based (radio frequency identification) ETC is found to be the most suitable alternative among all considered ETC technologies. Our results are in strong agreement with the report of apex committee, appointed by “Government of India (Ministry of Road Transport & Highways)” for implementation of ETC in India.     

Near‐optimal control for a stochastic SIRS model with imprecise parameters

Parameter values are usually assumed to be precisely known in many epidemic models but they could be imprecise due to various uncertainties. In this paper, we develop a stochastic SIRS model that includes imprecise parameters and white noise, formulate and analyze the near‐optimal control problem for the stochastic model. We obtain priori estimates of the susceptible, infected and recovered populations. Sufficient and necessary conditions for the near optimality of the model are established using Ekeland's principle and a nearly maximum condition on the Hamiltonian function. Numerical simulations are also performed to demonstrate the analytical results and evaluate the influence of imprecise parameters, white noise and treatment control on the dynamics of epidemics.     

Worst‐case optimal control for an electrical drive system with time‐delay

This paper considers an optimal control developed for an electrical drive system with a DC motor. Since it is a linear control system with input time‐delay subject to unknown but bounded disturbances, we construct a worst‐case feedback control policy, which can guarantee that, for all admissible uncertain disturbances, the real system state should be in a prescribed neighborhood of a desired value at the given final time, and the cost functional takes the best guarantee value. The worst‐case feedback control policy is allowed to be corrected at a given set of correction points between the initial and the final time, which is equivalent to solving a (m‐1)‐level min‐max problem. Since the min‐max problem at each stage does not yield a simple analytical solution, construction of the optimal policy is computationally prohibitive. This is why we consider an approximate control policy which is more convenient for computation. The simulation results illustrate that this proposed approach is feasible. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

带Markov跳的离散时间随机控制系统的最大值原理

本文研究一类同时含有Markov跳过程和乘性噪声的离散时间非线性随机系统的最优控制问题, 给出并证明了相应的最大值原理. 首先, 利用条件期望的平滑性, 通过引入具有适应解的倒向随机差分方程, 给出了带有线性差分方程约束的线性泛函的表示形式, 并利用Riesz定理证明其唯一性. 其次, 对带Markov跳的非线性随机控制系统, 利用针状变分法, 对状态方程进行一阶变分, 获得其变分所满足的线性差分方程. 然后, 在引入Hamilton函数的基础上, 通过一对由倒向随机差分方程刻画的伴随方程, 给出并证明了带有Markov跳的离散时间非线性随机最优控制问题的最大值原理, 并给出该最优控制问题的一个充分条件和相应的Hamilton-Jacobi-Bellman方程. 最后, 通过 一个实际例子说明了所提理论的实用性和可行性.     

A new approach to network‐based H∞ control for stochastic systems

This paper deals with the problem of network‐based H_∞ control for a class of uncertain stochastic systems with both network‐induced delays and packet dropouts. The networked control system under consideration is represented by a stochastic model, which consists of two successive delay components in the state. The uncertainties are assumed to be time varying and norm bounded. Sufficient conditions for the existence of H_∞ controller are proposed to ensure exponentially stable in mean square of the closed‐loop system that also satisfies a prescribed performance. The conditions are expressed in the frame of linear matrix inequalities (LMIs), which can be verified easily by means of standard software. Two practical examples are provided to show the effectiveness of the proposed techniques. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimal production decision for a risk‐averse manufacturer faced with random yield and stochastic demand

Consider a risk‐averse manufacturer who produces a single product with a random yield to satisfy uncertain market demand. The manufacturer maximizes its expected profit subject to a chance constraint that requires the probability of the total profit below a target be less than a predetermined level. We show that due to the profit target constraint in the presence of random yield and stochastic demand, the manufacturer can neither produce too much nor too little irrespective of the predetermined probability level, depending solely on the profit target level. Further, the special case of uniform yield and uniform demand is examined and we obtain the manufacturer's optimal production quantity. In addition, two special cases of random yield rate and deterministic demand or deterministic yield rate and stochastic demand are considered. The opposite impacts of random yield and stochastic demand are revealed: the random yield induces a minimum production quantity that may cause the manufacturer to increase its production quantity, while the stochastic demand induces a maximum production quantity that may cause the manufacturer to decrease its production quantity. By comparing the solutions of the above‐mentioned special cases with the case of both random yield and stochastic demand, it is demonstrated that the existence of both random yield and stochastic demand results in a more constrained production requirement for the manufacturer (a larger minimum production quantity and a smaller maximum production quantity). That is, the opposite impacts of random yield and stochastic demand will not offset, but enhance each other.     

A mathematical model on EPQ for stochastic demand in an imperfect production system

In the paper, we develop an EPQ (economic production quantity) inventory model to determine the optimal buffer inventory for stochastic demand in the market during preventive maintenance or repair of a manufacturing facility with an EPQ (economic production quantity) model in an imperfect production system. Preventive maintenance, an essential element of the just-in-time structure, may cause shortage which is reduced by buffer inventory. The products are sold with the free minimal repair warranty (FRW) policy. The production system may undergo “out-of-control” state from “in-control” state, after a certain time that follows a probability density function. The defective (non-conforming) items in “in-control” or “out-of-control” state are reworked at a cost just after the regular production time. Finally, an expected cost function regarding the inventory cost, unit production cost, preventive maintenance cost and shortage cost is minimized analytically. We develop another case where the buffer inventory as well as the production rate are decision variables and the expected unit cost considering the above cost functions is optimized also. The numerical examples are provided to illustrate the behaviour and application of the model. Sensitivity analysis of the model with respect to key parameters of the system is carried out.     

A BSDE approach to a risk-based optimal investment of an insurer

We discuss a backward stochastic differential equation, (BSDE), approach to a risk-based, optimal investment problem of an insurer. A simplified continuous-time economy with two investment vehicles, namely, a fixed interest security and a share, is considered. The insurer’s risk process is modeled by a diffusion approximation to a compound Poisson risk process. The goal of the insurer is to select an optimal portfolio so as to minimize the risk described by a convex risk measure of his/her terminal wealth. The optimal investment problem is then formulated as a zero-sum stochastic differential game between the insurer and the market. The BSDE approach is used to solve the game problem. It leads to a simple and natural approach for the existence and uniqueness of an optimal strategy of the game problem without Markov assumptions. Closed-form solutions to the optimal strategies of the insurer and the market are obtained in some particular cases.     

A robust optimal sliding‐mode control approach for magnetic levitation systems

This paper presents a robust optimal sliding‐mode control approach for position tracking of a magnetic levitation system. First, a linear model that represents the nonlinear dynamics of the magnetic levitation system is derived by the feedback linearization technique. Then, the robust optimal sliding‐mode control developed from the linear model is proposed. In the proposed control scheme, the integral sliding‐mode control with robust optimal approach is developed to achieve the features of high performance in position tracking response and robustness to the matched and unmatched uncertainties. Simulation and experimental results from the computer‐controlled magnetic levitation system are illustrated to show the validity of the proposed control approach for practical applications. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

A study of the impact of information technology on firm performance: a flexible production function approach

Most previous studies of the impact of information technology (IT) on firm performance have relied on the Cobb–Douglas production function, which makes unrealistic assumptions. These include assuming that (1) the scale elasticity, i.e. returns to scale, is constant throughout the entire output range; (2) the elasticity of substitution between inputs is one, whereas it can be anywhere from zero to infinity in practice; and (3) there are no complex interrelationships between inputs, i.e. no squared or product terms. The translog production function (used in two previous studies) does not make these assumptions and is therefore more accurate. However, the two previous studies found no significant difference between the two. This research has extended previous work on the impact of IT investments by providing more accurate results in some areas and new findings in others. IT's importance is shown to be much greater than previously thought. The translog function is used to demonstrate that the productivity paradox does not hold (i.e. both IT labor and capital indeed have a significant impact on firm revenues), but with considerably different results, i.e. the impact of IT is markedly more significant than that obtained from the Cobb–Douglas. Another interesting finding is that of scale elasticity. Our results show that this is less than 1, implying decreasing returns to scale of output. Finally, the substitutability between inputs was determined, which is not possible with the Cobb–Douglas. It was found that the partial elasticities of substitution for IT labor with IT capital and non-IT labor were quite substantial.