We propose a new method for certain multistage stochastic programs with linear or nonlinear objective function, combining a primal interior point approach with a linear-quadratic control problem over the scenario tree...
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In this paper, a robust scheduling method is suggested in the optimization of batch plant with uncertainties considering not only expected value but also variance. Many papers treating scenario-based stochastic progra...
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In this paper, a robust scheduling method is suggested in the optimization of batch plant with uncertainties considering not only expected value but also variance. Many papers treating scenario-based stochastic programming took expected values as objective functions. However, the meaning of expected value itself is sum of the probability of each scenario times each objective value. It implies nothing but currently calculated biggest value. It doesn't work when unexpected event happens. Therefore it is required to consider an additional criterion. I will choose variance and standard deviation of objective function.
In this article, we present a stochastic simulation-based genetic algorithm for solving chance constraint programming problems, where the random variables involved in the parameters follow any continuous distribution....
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In this article, we present a stochastic simulation-based genetic algorithm for solving chance constraint programming problems, where the random variables involved in the parameters follow any continuous distribution. Generally, deriving the deterministic equivalent of a chance constraint is very difficult due to complicated multivariate integration and is only possible if the random variables involved in the chance constraint follow some specific distribution such as normal, uniform, exponential and lognormal distribution. In the proposed method, the stochastic model is directly used. The feasibility of the chance constraints are checked using stochastic simulation, and the genetic algorithm is used to obtain the optimal solution. A numerical example is presented to prove the efficiency of the proposed method.
This errata points out several errors in various optimization models and an inconsistent numerical result in Chian-Son Yu, Han-Lin Li [International Journal of Production Economics 64 (2000) 385–397].
This errata points out several errors in various optimization models and an inconsistent numerical result in Chian-Son Yu, Han-Lin Li [International Journal of Production Economics 64 (2000) 385–397].
This work shows a control policy based on MPC and applied to project risk management. MPC has been applied due the properties that presents such as the easy constraint treatment or the extension to multivariable case....
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This work shows a control policy based on MPC and applied to project risk management. MPC has been applied due the properties that presents such as the easy constraint treatment or the extension to multivariable case. The control actions are the mitigation actions to execute in order to reduce the risk exposure. stochastic variables have been introduced to model the uncertainties of risk impacts. Integer variables are involved in the optimization problem modelling the mitigation actions.
Portfolio optimization has been one of the important research fields in modern finance. The most important character within this optimization problem is the uncertainty of the future returns and we usually use arithme...
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Portfolio optimization has been one of the important research fields in modern finance. The most important character within this optimization problem is the uncertainty of the future returns and we usually use arithmetic means and variance-covariances to represent the returns. In this study, by representing the returns as random variables in the optimization problem, we model a portfolio selection problem with transaction costs as a two-stage stochastic programming problem. We evaluate our stochastic programming model using the historical data obtained from the Taiwan Stock Exchange and the results show that our method outperforms the market and some other familiar models.
In this paper, the minimum weight edge covering problem with stochastic weights is studied. We propose the concepts of expected minimum weight edge cover, α-minimum weight edge cover and the most minimum weight edge ...
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In this paper, the minimum weight edge covering problem with stochastic weights is studied. We propose the concepts of expected minimum weight edge cover, α-minimum weight edge cover and the most minimum weight edge cover originally. According to different decision criteria, three types of models: expected value model, chance-constrained programming and dependent-chance programming are formulated. We integrate stochastic simulation and genetic algorithm to produce a hybrid intelligent algorithm in order to solve the models. Finally, a numerical example is given to illustrate the effectiveness of the algorithm.
In the optimal control of industrial, field, or service robots, the standard procedure is to determine first off-line a feedforward control and a reference trajectory, based on some selected nominal values of the unkn...
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In the optimal control of industrial, field, or service robots, the standard procedure is to determine first off-line a feedforward control and a reference trajectory, based on some selected nominal values of the unknown stochastic model parameters, and to correct then the inevitable and increasing deviation of the state or performance of the robot from the prescribed state or performance of the system by on-line measurement and control actions. Due to the stochastic variations of the model parameters, increasing measurement and correction actions are needed during the process. By optimal stochastic trajectory planning (OSTP), based on stochastic optimization methods, the available a priori and sample information about the robot and its working environment is incorporated into the control process. Consequently, more robust reference trajectories and feedforward controls are obtained which cause much less on-line control actions. In order to maintain a high quality of the guiding functions, the reference trajectory and the feedforward control can be updated at some later time points such that additional information about the control process is available. After the presentation of the Adaptive Optimal stochastic Trajectory Planning (AOSTP) procedure based on stochastic optimization methods, several numerical techniques for the computation of robust reference trajectories and feedforward controls under real-time conditions are presented.
A stochastic simulation based genetic algorithm (GA) is presented, in this paper, for solving chance constraint programming problems in which the random variables follow some discrete distributions. The feasibility of...
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A stochastic simulation based genetic algorithm (GA) is presented, in this paper, for solving chance constraint programming problems in which the random variables follow some discrete distributions. The feasibility of the chance constraints is checked by stochastic simulation. In general, the feasible region associate with such problems is non-convex. Therefore, GA is used to obtain the optimal solution. In the proposed method, the stochastic model is directly used without finding the deterministic equivalent of it. A numerical example is presented to prove the efficiency of the proposed method. * E-mail: rabin@*** [ABSTRACT FROM AUTHOR]
This paper introduces a hybrid optimization approach, an inexact two-stage mixed integer linear programming (ITMILP) model, for the planning of regional solid waste management systems under uncertainty. The model impr...
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This paper introduces a hybrid optimization approach, an inexact two-stage mixed integer linear programming (ITMILP) model, for the planning of regional solid waste management systems under uncertainty. The model improves upon the existing mixed integer, two-stage stochastic and interval-parameter programming approaches by allowing uncertainties presented as random distributions and discrete intervals, as well as policies expressed as allowable waste-loading targets to be effectively incorporated within a general optimization framework. In the modeling formulation, penalties are imposed when the policies are violated. In its solutions algorithm, the ITMILP model is transformed into two deterministic submodels, which were solved sequentially. Application of the developed methodology to the planning of a waste management system indicates that reasonable solutions for the binary and continuous decision variables can be generated through this approach. Considerable information was generated regarding decisions of facility expansion within a multi-period, multi-scale and multi-waste-level context;and optimal waste flow allocation patterns were achieved within the waste management system. The ITMILP model was then employed to generate a number of decision alternatives under various policy conditions, allowing for more in-depth analyses of tradeoffs between environmental and economic objectives.
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