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11th Asia Pacific Transportation Development Conference / 29th ICTPA Annual Conference

作者： Lin, Y. -Z. Chung Yuan Christian Univ Dept Civil Engn 200 Chung Pei Rd Taoyuan 32023 Taiwan

ISBN: (数字)9780784479810

ISBN: (纸本)9780784479810

From the point of view of revenue management, a bi-level optimization model is proposed to determine the seat allocation and discriminatory pricing for high speed rail. The relation between ticket prices and quantities can be represented using demand functions. stochastic passenger demands and demand functions of high speed rail are integrated into this model. The objective is to maximize the expected total revenue. Discriminatory pricing principles and seat capacity constraints are considered simultaneously. For different market segments, discriminative ticket prices are determined in accordance with the given seat amount. The upper-level model is formulated as a nonlinear mathematical program. The lower-level model is formulated as a two-stage stochastic programming model.

关键词： high-speed railway seat hierarchical models Pricing stochastic programming stochastic models

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PENALTY METHODS WITH stochastic APPROXIMATION FOR stochastic NONLINEAR programming

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MATHEMATICS OF COMPUTATION 2017年第306期86卷 1793-1820页

作者： Wang, Xiao Ma, Shiqian Yuan, Ya-Xiang Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Chinese Univ Hong Kong Dept Syst Engn & Engn Management Shatin Hong Kong Peoples R China Chinese Acad Sci Acad Math & Syst Sci State Key Lab Sci & Engn Comp Beijing Peoples R China

In this paper, we propose a class of penalty methods with stochastic approximation for solving stochastic nonlinear programming problems. We assume that only noisy gradients or function values of the objective function are available via calls to a stochastic first-order or zeroth-order oracle. In each iteration of the proposed methods, we minimize an exact penalty function which is nonsmooth and nonconvex with only stochastic first-order or zeroth-order information available. stochastic approximation algorithms are presented for solving this particular subproblem. The worst-case complexity of calls to the stochastic first-order (or zeroth-order) oracle for the proposed penalty methods for obtaining an epsilon-stochastic critical point is analyzed.

关键词： stochastic programming nonlinear programming stochastic approximation penalty method global complexity bound

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A Scenario Tree based stochastic programming Approach for Multi-Stage Weapon Equipment Mix Production Planning in Defense Manufacturing

A Scenario Tree based Stochastic Programming Approach for Mu...

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International Conference on Mechanical, Manufacturing, Modeling and Mechatronics (IC4M)

作者： Li, Xuan Zhou, Yu Liao, Tianjun Hue, Yajun Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha 410073 Hunan Peoples R China Air Force Engn Univ Sch Mat Management & Safety Engn Xian 710051 Peoples R China Beijing Inst Syst Engn State Key Lab Complex Syst Simulat Beijing 100101 Peoples R China

The evolving military capability requirements (CRs) must be meted continuously by the multi-stage weapon equipment mix production planning (MWEMPP). Meanwhile, the CRs possess complex uncertainties with the variant military tasks in the whole planning horizon. The mean-value deterministic programming technique is difficult to deal with the multi-period and multi-level uncertain decision-making problem in MWEMPP. Therefore, a multi-stage stochastic programming approach is proposed to solve this problem. This approach first uses the scenario tree to quantitatively describe the bi-level uncertainty of the time and quantity of the ('Rs, and then build the whole off-line planning alternatives assembles for each possible scenario, at last the optimal planning alternative is selected on-line to flexibly encounter the real scenario in each period. A case is studied to validate the proposed approach. The results confirm that the proposed approach can better hedge against each scenario of the CRs than the traditional mean-value deterministic technique.

关键词： stochastic programming

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Two-stage stochastic programming Based Model Predictive Control Strategy for Microgrid Energy Management under Uncertainties

Two-stage Stochastic Programming Based Model Predictive Cont...

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International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Li, Zhongwen Zang, Chuanzhi Zeng, Peng Yu, Haibin Li, Hepeng Chinese Acad Sci Shenyang Inst Automat Lab Networked Control Syst Shenyang Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781509019700

Microgrids (MGs) are presented as a cornerstone of smart grid, which can integrate intermittent renewable energy sources (RES), storage system, and local loads environmentally and reliably. Due to the randomness in RES and load, a great challenge lies in the optimal operation of MGs. Two-stage stochastic programming (SP) can involve the forecast uncertainties of load demand, photovoltaic (PV) and wind production in the optimization model. Thus, through two-stage SP, a more robust scheduling plan is derived, which minimizes the risk from the impact of uncertainties. The model predictive control (MPC) can effectively avoid short sighting and further compensate the uncertainty within the MG through a feedback mechanism. In this paper, a two-stage SP based MPC stratey is proposed for microgrid energy management under uncertainties, which combines the advantages of both two stage SP and MPC. The results of numerical experiments explicitly demonstrate the benefits of the proposed strategy.

关键词： energy management microgrid model predictive control stochastic programming uncertainty

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Optimal Home Energy Management Integrating Random PV and Appliances Based on stochastic programming 28

Optimal Home Energy Management Integrating Random PV and App...

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28th Chinese Control and Decision Conference

作者： Li, Hepeng Zang, Chuanzhi Zeng, Peng Yu, Haibin Li, Zhongwen Ni Fenglian Chinese Acad Sci Shenyang Inst Automat Lab Networked Control Syst Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Huaihai Inst Technol Lianyungang 222005 Peoples R China

ISBN: (纸本)9781467397148

This paper presents an detailed study about the development of an integrative DR policy for the optimal home energy management system under stochastic environment. In this study, home appliances are classified into three categories and detailed modeling of all kinds of home appliances is given. Then, the optimal HEMS problem is formulated as a stochastic programming model considering the uncertainties of PV production and critical loads to minimize a customer's electricity cost. Monte Carlo simulation method is used to decompose the problem into a mixed integer linear programming problem. Finally, the proposed stochastic programming model is verified through numerical simulation. The simulation results show that the proposed stochastic DR model can reduce the effect of the uncertainties in residential environment on the electricity cost and obtain a better DR policy than the conventional deterministic model.

关键词： Demand Response Home Energy Management System stochastic programming Uncertainties

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Application of stochastic programming and Probabilistic Analyses as Key Parameters for Real Decision Making regarding Implementing or Not Energy Rationing - A Case Study for the Brazilian Hydrothermal Interconnected System 19

Application of Stochastic Programming and Probabilistic Anal...

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19th Power Systems Computation Conference (PSCC)

作者： Maceira, M. E. P. Melo, A. C. G. Zimmermann, M. P. UERJ State Univ Rio de Janeiro CEPEL Elect Energy Res Ctr Rio De Janeiro Brazil ELETROSUL Eletrosul Cent Eletr SA Florianopolis SC Brazil

ISBN: (纸本)9788894105124

During 2014 and 2015, the Brazilian hydrothermal interconnected system faced critical hydrological conditions, such as extremely low multivariate inflow values on February 2014 and January 2015. Therefore, an issue that arose in early 2014 was whether the Brazilian government would have to implement or not an energy rationing. In this sense, this paper summarizes a proposed approach to technically support this decision, based on dual stochastic dynamic programming, multivariate inflows scenarios generation and probabilistic analyses, and that utilized the chain of optimization models developed by CEPEL and real configurations of the Brazilian large scale interconnected hydrothermal system. These studies, inserted in a very comprehensive and detailed technical analyses carried out by the Brazilian Monitoring Committee of the Electrical Sector, led to the decision of not implementing an energy rationing in 2014, and to continue to closely monitoring the electric power system performance.

关键词： Hydrothermal Systems Probabilistic Analysis stochastic programming Decision Making under Uncertainty Large Scale Systems

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E-MRO Service Planning with Uncertain Constraints Based on stochastic programming 20

E-MRO Service Planning with Uncertain Constraints Based on S...

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20th IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Li, Ling Liu, Min Shen, Weiming Cheng, Guoqing Zhang, Feng Tongji Univ Sch Elect & Informat Engn Shanghai Peoples R China Jingdezhen Ceram Inst Sch Informat Engn Jingdezhen Peoples R China

ISBN: (纸本)9781509019151

E-business based maintenance, repair and overhaul (E-MRO) is a new MRO service mode. Although in real world there are a number of E-MRO prototype systems, few comprehensive studies have been conducted on this topic. Motivated by the challenges of making optimal E-MRO service planning, simultaneously considering the capacity constraints of MRO service providers and the maintenance constraints of equipment users, this paper proposes a stochastic programming model involving multi-choice parameters, where uncertain factors in E-MRO are quantified. To solve the model, the properties of expectation of a random variable, and the Lagrange interpolating polynomial approach are used to derive the deterministic model equivalent to the stochastic programming model. The objective of the model is to seek optimal service planning, including determining whether to configure the corresponding service from the corresponding provider to the corresponding user at the corresponding period, and determining the time of the corresponding service. The optimal service planning can be referred by practitioners for a more reasonable decision. A numerical example validated the feasibility of proposed model.

关键词： e-business based maintenance repair and overhaul (E-MRO) service planning stochastic programming uncertainty

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Convergence of Discrete Approximations of stochastic programming Problems with Probabilistic Criteria 9th

Convergence of Discrete Approximations of Stochastic Program...

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9th International Conference on Discrete Optimization and Operations Research (DOOR)

作者： Kibzun, Andrey I. Ivanov, Sergey V. Moscow Inst Aviat Technol Volokolamskoe Shosse 4A-80GSP 3 Moscow 125993 Russia Sobolev Inst Math Acad Koptyug Ave4 Novosibirsk 630090 Russia

ISBN: (纸本)9783319449142;9783319449135

We consider stochastic programming problems with probabilistic and quantile objective functions. The original distribution of the random variable is replaced by a discrete one. We thus consider a sequence of problems with discrete distributions. We suggest conditions, which guarantee that the sequence of optimal strategies converges to an optimal strategy of the original problem. We consider the case of a symmetrical distribution, the case of the loss function increasing in the random variable, and the case of the loss function increasing in the optimization strategy.

关键词： stochastic programming Probabilistic criterion Value-at-risk Quantile function Convergent discretization

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stochastic programming model for oversaturated intersection signal timing

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2015年第PartC期58卷 474-486页

作者： Tong, Yue Zhao, Lei Li, Li Zhang, Yi Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Jiangsu Prov Collaborat Innovat Ctr Modern Urban Nanjing Jiangsu Peoples R China

In large cities, signalized intersections often become oversaturated in rush hours due to growing traffic demand. If not controlled properly, they may collectively result in serious congestion. How to schedule traffic signals for oversaturated intersections has thus received increasing interests in recent years. Among various factors that may influence control performance, uncertainty in traffic demand remains as an important one that needs to be further studied. In some recent works, e.g., Yin (2008) and Li (2011), robust optimization models have been utilized to address uncertainty in traffic demand and to design fixed-timed signal control for oversaturated intersections. In this paper, we propose a stochastic programming (SP) model to schedule adaptive signal timing plans that minimize the expected vehicle delay. Our numerical experiments show that the proposed SP model better describes the fluctuations of traffic flows and outperforms the deterministic linear programming (LP) model in total vehicle delay, total throughput, and vehicle queue lengths. Moreover, we compare the proposed SP model with the adaptive signal control model proposed in Lin et al. (2011) to provide insights on such improvements from green time utilization and queue balancing perspectives. Furthermore, we study the feasibility of the proposed SP model in practice, with an emphasis on the required sample sizes. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Traffic signals Oversaturated intersection stochastic programming

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Partition-based Method for Two-stage stochastic Linear programming Problems with Complete Recourse

Partition-based Method for Two-stage Stochastic Linear Progr...

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作者： Carlos Andres Gamboa Roiguez PONTIFICIA UNIVERSIDADE CATOICA DO RIO DE JANEIRO

学位级别：硕士

The hardest part of modelling decision-making problems in the real world, is the uncertainty associated to realizations of futures events. The stochastic programming is responsible about this subject; the target is finding solutions that are feasible for all possible realizations of the unknown data, optimizing the expected value of some functions of decision variables and random variables. The approach most studied is based on Monte Carlo simulation and the Sample Average Approximation (SAA) method which is a kind of discretization of expected value, considering a finite set of realizations or scenarios uniformly distributed. It is possible to prove that the optimal value and the optimal solution of the SAA problem converge to their counterparts of the true problem when the number of scenarios is sufficiently big. Although that approach is useful, there exist limiting factors about the computational cost to increase the scenarios number to obtain a better solution; but the most important fact is that SAA problem is function of each sample generated, and for that reason is random, which means that the solution is also uncertain, and to measure its uncertainty it is necessary consider the replications of SAA problem to estimate the dispersion of the estimated solution, increasing even more the computational cost. The purpose of this work is presenting an alternative approach based on robust optimization techniques and applications of Jensen's inequality, to obtain bounds for the optimal solution, partitioning the support of distribution (without scenarios creation) of unknown data, and taking advantage of the convexity. At the end of this work the convergence of the bounding problem and the proposed solution algorithms are analyzed.

关键词： Robust Optimization Sample Average Approximation stochastic programming Partition-based method

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