In this paper, we consider a stochastic programming approach to multistage post-tax portfolio optimization. Asset performance information is specified as a scenario tree generated by two alternative methods based on s...
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In this paper, we consider a stochastic programming approach to multistage post-tax portfolio optimization. Asset performance information is specified as a scenario tree generated by two alternative methods based on simulation and optimization. We assume three tax wrappers involving the same instruments for an efficient investment strategy and determine optimal allocations to different instruments and wrappers. The tax rules are integrated with the linear and mixed integer stochastic models to yield air overall tax and return-efficient multistage portfolio. The computational performance of these models is tested using a case study with different scenario trees. Our experiments show that optimal portfolios obtained by both linear programming and mixed integer stochastic models diversify over wrappers and the original capital is distributed among assets within each wrapper. (C) 2003 Elsevier B.V. All rights reserved.
This paper studies the optimal allocation of transmit power in a wireless communication network. First, a stochastic programming formulation is introduced, based on penalizing violations of quality-of-service constrai...
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This paper studies the optimal allocation of transmit power in a wireless communication network. First, a stochastic programming formulation is introduced, based on penalizing violations of quality-of-service constraints. The maximization of the certainty equivalent signal-to-interference ratio under Rayleigh fading corresponds to a penalty model where (max-min) fairness is explicitly taken into consideration. Second, optimum dynamic power allocation is discussed. Efficient dynamic resource allocation under both linear and logarithmic utility functions is addressed. The dynamic model studies the optimal trade-off between instantaneous quality-of-service and a delay-penalized reliable quality-of-service. Related work on optimal stochastic power control is summarized.
A sequential simulation procedure (which requires shorter com puter running time than a nonsequential one) is an effective method for solving stochastic personnel scheduling models. The time needed for a particular em...
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A sequential simulation procedure (which requires shorter com puter running time than a nonsequential one) is an effective method for solving stochastic personnel scheduling models. The time needed for a particular employee to perform a given activ ity is assumed to be a random number with a given distribution.
作者：
Xu, BinBoyce, Scott E.Zhang, YuLiu, QiangGuo, LeZhong, Ping-AnHohai Univ
State Key Lab Hydrol Water Resources & Hydraul En Coll Hydrol & Water Resources 1 Xikang Rd Nanjing 210098 Jiangsu Peoples R China US Geol Survey
Calif Water Sci Ctr 4165 Spruance RdSuite 200 San Diego CA 92101 USA Hohai Univ
Coll Hydrol & Water Resources 1 Xikang Rd Nanjing 210098 Jiangsu Peoples R China China Yangtze Power Co Ltd
19 Jinrong St Beijing 100032 Peoples R China Hohai Univ
Natl Engn Res Ctr Water Resources Efficient Utili Coll Hydrol & Water Resources 1 Xikang Rd Nanjing 210098 Jiangsu Peoples R China
Reservoir refill operation modeling attempts to maximize a set of benefits while minimizing risks. The benefits and risks can be in opposition to each other, such as having enough water for hydropower generation while...
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Reservoir refill operation modeling attempts to maximize a set of benefits while minimizing risks. The benefits and risks can be in opposition to each other, such as having enough water for hydropower generation while leaving enough room for flood protection. In addition to multiple objects, the uncertainty of streamflow can make decision making difficult. This paper develops a stochastic optimization model for reservoir refill operation with the objective of maximizing the expected synthesized energy production for a cascade system of hydropower stations while considering flood risk. Streamflow uncertainty is addressed by discretized streamflow scenarios and flood risk is controlled by a joint chance constraint restricting the occurrence probability. With the variability of flood risk level, two advancing refill scenarios for exploring operation benefit are presented. Scenario I loosens the current stagewise storage bounds conditions and allows advancing reservoir refills but keeps the flood risk level the same as the refill policies obtained under the current storage bounds. Scenario II keeps the current storage bounds unchanged but allows increases in flood risk level. The proposed methodology is applied to the Xiluodu cascade system of reservoirs in China and investigates the optimal refill policies obtained by both scenarios. Compared with the benchmark obtained under the current storage bounds and lowest flood risk level, the results show (1) the synthesized energy production can be improved by 2.13% without changing the flood risk level under Scenario I, and (2) the synthesized energy production can also be increased by 0.21% at the expense of increasing the flood risk level by 4.4% when Scenario II is employed. As Scenario I produces higher benefit and lower risk than Scenario II, it is recommended to loosen the current stagewise storage bounds but to keep the flood risk level unchanged during refill operations. (C) 2016 American Society of Civil Engineers.
Consideration was given to the problem of stochastic programming with the quantile (VaR) criterion. Conditions related with the characteristics of probabilistic distributions under which the quantile function is conve...
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Consideration was given to the problem of stochastic programming with the quantile (VaR) criterion. Conditions related with the characteristics of probabilistic distributions under which the quantile function is convex in strategy were presented. Relationship between convexity of the quantile function and convexity of the function of integral (CVaR) quantile criterion was shown.
A common feed formulation problem is the variability of nutrients in ingredients coming from different sources. Linear programming (LP) models require the variability of ingredients to be disregarded or to be estimate...
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A common feed formulation problem is the variability of nutrients in ingredients coming from different sources. Linear programming (LP) models require the variability of ingredients to be disregarded or to be estimated and linearized. However, stochastic programming (SP) can incorporate nutrient variability to provide least cost rations that meet each nutritional requirement at specified confidence levels. This study evaluated the ability of LP, LP with a margin of Safety (LPMS), and SP models to formulate poultry rations at least cost, with a given probability to meet nutrient requirements as set by the National Research Council in 1984. Rations formulated by LP were least cost, but did not take nutrient variability into account. The LPMS and SP models met various poultry nutritional requirements at confidence levels ranging from P greater-than-or-equal-to 5 to P greater-than-or-equal-to 90. The SP model consistently produced lower cost rations than LPMS.
In this paper, a new stochastic programming approach is presented to address chemical process optimization problems under uncertainty. The novel algorithm, named as delayed sampling approach, solves an equivalent dete...
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In this paper, a new stochastic programming approach is presented to address chemical process optimization problems under uncertainty. The novel algorithm, named as delayed sampling approach, solves an equivalent deterministic optimization model transformed from the stochastic optimization problem between two stochastic simulations. The sampling numbers are reduced considerably and the computational burden is then alleviated remarkably. A complex crude distillation unit is modeled and optimized using the new stochastic approach. Savings of up to 80% in CPU time has been achieved without significant loss of solution precision compared to the conventional stochastic optimization method. (C) 2000 Elsevier Science Ltd. All rights reserved.
This paper presents two contributions: A set of routines that manipulate instances of stochastic programming problems in order to make them more amenable for different solution approaches;and a development environment...
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This paper presents two contributions: A set of routines that manipulate instances of stochastic programming problems in order to make them more amenable for different solution approaches;and a development environment where these routines can be accessed and in which the modeler can examine aspects of the problem structure. The goal of the research is to reduce the amount of work, time, and cost involved in experimenting with different solution methods.
This paper proposed a novel stochastic programming which formulates the minimum energy cost routing for ad hoc wireless network. We presented the stochastic programming formulation to find the minimum energy cooperati...
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ISBN:
(纸本)9780769535579
This paper proposed a novel stochastic programming which formulates the minimum energy cost routing for ad hoc wireless network. We presented the stochastic programming formulation to find the minimum energy cooperative routing in slow fading channel and fast fading channel respectively. The analysis shows that the stochastic programming formulation can optimize the minimum energy cost cooperative routing in slow fading channel and approximate the optimum value in fast fading channel preferably.
In this paper, we develop a multi-stage stochastic programming model for dynamic international portfolio risk management with options in an integrated view. Upon scenario trees, the model can automatically compute the...
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In this paper, we develop a multi-stage stochastic programming model for dynamic international portfolio risk management with options in an integrated view. Upon scenario trees, the model can automatically compute the optimal hedging strategies, which provides rolling and dynamic decisions for how much option positions should be established and how much should be liquidated, while simultaneously allocating the corresponding underlying assets. Extensive numerical analyses strongly verify the effectiveness of the model, especially in market downturns, and support the computational feasibility and performance of the model.
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