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A stochastic programming approach for multi-period portfolio optimization

COMPUTATIONAL MANAGEMENT SCIENCE 2009年第2期6卷 187-208页

作者： Geyer, Alois Hanke, Michael Weissensteiner, Alex WU Wirtschaftsuniv Wien A-1190 Vienna Austria Vienna Grad Sch Finance A-1190 Vienna Austria Univ Innsbruck Dept Banking & Finance Univ Str 15 A-6020 Innsbruck Austria

This paper extends previous work on the use of stochastic linear programming to solve life-cycle investment problems. We combine the feature of asset return predictability with practically relevant constraints arising in a life-cycle investment context. The objective is to maximize the expected utility of consumption over the lifetime and of bequest at the time of death of the investor. Asset returns and state variables follow a first-order vector auto-regression and the associated uncertainty is described by discrete scenario trees. To deal with the long time intervals involved in life-cycle problems we consider a few short-term decisions (to exploit any short-term return predictability), and incorporate a closed-form solution for the long, subsequent steady-state period to account for end effects.

关键词： Life-cycle asset allocation stochastic linear programming Scenario trees VAR(1) process

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A new equivalent transformation for interval inequality constraints of interval linear programming

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FUZZY OPTIMIZATION AND DECISION MAKING 2016年第2期15卷 155-175页

作者： Chen, Mingzhi Wang, Sheng-Guo Wang, Paul P. Ye, Xiaoxiang Fuzhou Univ Coll Math & Comp Sci Fuzhou 350108 Peoples R China Univ N Carolina Dept Engn Technol Charlotte NC 28223 USA Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

In this paper, we have introduced a new approach to solve a class of interval linear programming (ILP) problems. Firstly, the novel concept of an interval ordering relation is further developed to make desired solution feasible. Secondly, according to the 3 law of normal distribution, a new equivalent transformation for constraints with the interval-valued coefficients of ILP is justified. Accordingly, the uncertainty stemmed from interval number could be replaced by the uncertainty of random variables. Consequently, the classical methodology of stochastic linear programming, a chance constrained programming model based on normal distribution is designed to work out the equivalent form of the original problem. This is because it allows us to carry out the optimization operation with a certain calibrated probability. A typical numerical example is given to illustrate how to apply equivalent transformation in order to realize ILP. Finally, we conclude this paper by elaborated comparisons among our method and selected existing solutions to advance our confidence of our research results as to their correctness and effectiveness.

关键词： Interval linear programming (ILP) stochastic linear programming Ordering relation of intervals Modeling the interval uncertainty Variate transformation Chance-constrained programming

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The influence of nuclear generation on CO₂ emissions and on the cost of the Spanish system in long-term generation planning

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2011年第3期33卷 673-683页

作者： Delgado, F. Ortiz, A. Renedo, C. J. Perez, S. Manana, M. Zobaa, Ahmed F. Univ Cantabria ETSII Dept Elect & Energy Engn E-39005 Santander Spain Brunel Univ Sch Engn & Design Uxbridge UB8 3PH Middx England

This paper presents the influence of a nuclear generation option on CO2 emissions and on the cost of the Spanish long-term generation system by means of the development of a stochastic linear model, based on the software GAMS, where multiple aspects have been contemplated: the uncertainty regards fossil fuel and CO2 emission allowance prices by analysing different scenarios, the stability and supply security of the system by considering different restrictions, the maximum grade of penetration of the different technologies by means of commissioning plans, etc. Only two of the conclusions drawn are focused on here. First, it is possible to get a clean system without nuclear power generation but the cost would be higher than in the case where the existing nuclear power plants continue to operate. Second, the development of clean coal technologies could be affected negatively by the development of nuclear generation. (c) 2011 Elsevier Ltd. All rights reserved.

关键词： Generation expansion planning Nuclear energy CO2 emissions stochastic linear programming Generation costs Clean coal technologies

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A high performance neural network model for solving chance constrained optimization problems

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NEUROCOMPUTING 2013年 121卷 540-550页

作者： Nazemi, Alireza Tahmasbi, Narges Shahrood Univ Sch Math Sci Dept Math Shahrood Iran

This paper presents a neural network model to solve chance constrained optimization (CCO) problems. The main idea is to convert the chance constrained problem into an equivalent convex second order cone programming (CSOCP) problem. A neural network model is then constructed for solving the obtained CSOCP problem. By employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the original problem. The simulation results also show that the proposed neural network is feasible and efficient. (C) 2013 Elsevier B.V. All rights reserved.

关键词： stochastic linear programming Chance constrained optimization Neural network models Stability Convergence

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Planning logistics operations in the oil industry

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2000年第11期51卷 1271-1288页

作者： Dempster, MAH Pedrón, NH Medova, EA Scott, JE Sembos, A Univ Cambridge Judge Inst Management Cambridge CB2 1AG England

In this paper we apply stochastic programming modelling and solution techniques to planning problems for a consortium of oil companies. A multiperiod supply, transformation and distribution scheduling problem-the Depot and Refinery Optimization Problem (DROP)-is formulated for strategic or tactical level planning of the consortium's activities. This deterministic model is used as a basis for implementing a stochastic programming formulation with uncertainty in the product demands and spot supply costs (DROPS), whose solution process utilizes the deterministic equivalent linear programming problem. We employ our STOCHGEN general purpose stochastic problem generator to 'recreate' the decision (scenario) tree for the unfolding future as this deterministic equivalent. To project random demands for oil products at different spatial locations into the future and to generate random fluctuations in their future prices/costs a stochastic input data simulator is developed and calibrated to historical industry data. The models are written in the modelling language XPRESS-MP and solved by the XPRESS suite of linear programming solvers. From the viewpoint of implementation of large-scale stochastic programming models this study involves decisions in both space and time and careful revision of the original deterministic formulation. The first part of the paper treats the specification, generation and solution of the deterministic DROP model. The stochastic version of the model (DROPS) and its implementation are studied in detail in the second part and a number of related research questions and implications discussed.

关键词： energy logistics planning supply chain management stochastic linear programming

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A Dynamic stochastic Optimization for Recharging Plug-In Electric Vehicles

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IEEE TRANSACTIONS ON SMART GRID 2018年第5期9卷 4154-4161页

作者： Liu, Siyan Etemadi, Atnir H. George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA

This paper presents a recharging scheme for plug-in (hybrid) electric vehicles. Despite their many advantages such as reducing carbon footprint, lower fuel cots, and high performance, uncoordinated recharging of electric vehicles in a high-penetration system can increase system peak load and create new peaks in the demand profile, hence, reducing system reliability and operational integrity. To optimize electric vehicle recharging costs and prevent such reliability problems, a dynamic stochastic optimization method is proposed that formulates a stochastic linear programming approach taking into account load, electricity pricing, and renewable energy generation uncertainties, and solves the day-ahead problem in an offline fashion. A second online stage is also proposed that uses offline solutions, collects real-time system data, and adjusts recharging schedules to obtain a better recharging scheme once system uncertainties are revealed. The proposed method is robust to variations in different stochastic parameters, has a low communication requirement, and benefits both users and the power utility. Recharging system structure, data models, and mathematical formulation of the proposed method are presented. Results demonstrate that unlike other recharging schemes, the proposed method does not increase system peak, does not create new peaks, and fills the valleys of demand profile to optimize power system operations.

关键词： Electric vehicle recharging schemes stochastic linear programming

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A Two-Stage stochastic Dynamic Economic Dispatch Model Considering Wind Uncertainty

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2016年第2期7卷 819-829页

作者： Liu, Yang Nair, Nirmal-Kumar C. Univ Auckland Dept Elect & Comp Engn Auckland 1142 New Zealand

This paper proposes a two-stage stochastic dynamic economic dispatch model aiming at better managing system variability and uncertainty influenced by wind generation. stochastic decomposition algorithm is proposed to solve the model in order to facilitate real-time application. The proposed method is tested on PJM-5 and RTS-24 systems and the results verify that the model inherits advantages from conventional dynamic economic dispatch, which is able to make out of merit order dispatch instructions for future benefits. Furthermore, the proposed model can correct false generator pre-ramp instructions due to forecast error and evaluate potential nodal wise reserve requirement therefore improving reserve deliverability. For computational efficiency assessment, stochastic decomposition is compared with sample average approximation. A modified IEEE-118 bus case study is shown.

关键词： Dynamic economic dispatch sample average approximation stochastic linear programming stochastic decomposition reserve procurement

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A Quantitative Decision Model Towards Maximizing Organizational Sustainability

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ENGINEERING MANAGEMENT JOURNAL 2013年第1期25卷 3-18页

作者： Turan, Fikret Korhan Needy, Kim LaScola Istanbul Kemerburgaz Univ Dept Ind Engn Istanbul Turkey Univ Arkansas Fayetteville AR 72701 USA

This article introduces a decision model as a multi-stage, stochastic linear program, integrating both financial and non-financial performance measures into the process of investment planning via the triple bottom line framework. The model developed enables decision makers to create an optimal balanced investment portfolio considering stakeholder priorities together with financial performance of investment projects. As a stochastic program, the model considers uncertainty explicitly, which is not an aspect of previous models. The article provides an application of the model to a local government in the city of Pittsburgh as a case study discussing the implications for managers and policy makers.

关键词： Uncertainty stochastic linear programming Corporate Sustainability Business Sustainability Capital Budgeting Analytic Network Process

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Service-oriented container slot allocation policy under stochastic demand

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2023年第1期176卷

作者： Liang, Jinpeng Li, Liming Zheng, Jianfeng Tan, Zhijia Dalian Maritime Univ Sch Transportat Engn Dalian Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan Peoples R China

The liner shipping industry plays a pivotal role in global cargo transportation, catering to both contract and spot shippers. Proper capacity allocation between these shippers is vital for maintaining service quality and improving revenue. This research investigates the service oriented container slot allocation problem under stochastic demand, aiming to maximize total freight revenue while providing adequate service levels to contract shippers for sustaining their market loyalty. We use the fill rate (i.e., the proportion of satisfied demand) as a metric for service level and formulate the research problem as a stochastic linear programming model. To solve this model, we convert it into a multi-objective attainability problem by setting a target for total revenue, and apply Blackwell's Approachability Theorem to theoretically determine the feasibility of any given revenue and service level requirements. Leveraging these insights, we devise near-optimal policies to guide the slot allocation decision under each demand scenario. Numerical experiments demonstrate that our approach outperforms the benchmark policies in the literature. Furthermore, it can also achieve near-optimal performance closely resembling the sampling average approximation (SAA) solution while significantly reducing the computational time.

关键词： Liner shipping Container slot allocation stochastic demand stochastic linear programming Service level

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Expected gain-loss pricing and hedging of contingent claims in incomplete markets by linear programming

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2010年第3期201卷 770-785页

作者： Pinar, Mustafa C. Salih, Aslihan Camci, Ahmet Bilkent Univ Dept Ind Engn TR-06800 Ankara Turkey Bilkent Univ Dept Management TR-06800 Ankara Turkey

We analyze the problem of pricing and hedging contingent claims in the multi-period, discrete time, discrete state case using the concept of a "lambda gain-loss ratio opportunity". Pricing results somewhat different from, but reminiscent of, the arbitrage pricing theorems of mathematical finance are obtained. Our analysis provides tighter price bounds on the contingent claim in an incomplete market, which may converge to a unique price for a specific value of a gain-loss preference parameter imposed by the market while the hedging policies may be different for different sides of the same trade. The results are obtained in the simpler framework of stochastic linear programming in a multi-period setting. and have the appealing feature of being very simple to derive and to articulate even for the non-specialist. They also extend to markets with transaction costs. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Contingent claim Pricing Hedging Martingales stochastic linear programming Transaction costs

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