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multi-objective stock market portfolio selection using multi-stage stochastic programming with a harmony search algorithm

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NEURAL COMPUTING & APPLICATIONS 2022年第24期34卷 22257-22274页

作者： Asgari, H. Behnamian, J. Bu Ali Sina Univ Fac Engn Dept Ind Engn Hamadan Hamadan Iran

The problem of stock portfolio selection is one of the most critical problems in financial markets. The portfolio selection problem is to find an optimal solution to allocate a fixed amount of capital to a set of available stocks with the objective function of having the maximum expected rate of return and, at the same time, the least possible risk. Among the shortcomings that most stock portfolio models have is not paying attention to future changes and focusing too much on past information. This study aims to provide a framework that addresses some shortcomings and provides a practical tool. In this regard, this study suggests a multi-objective and multi-stage stochastic model for portfolio selection in the financial market. The stage refers to the periods in which the stock portfolio will be reviewed. By combining the scenario generation model with multi-stage stochastic programming, the investor is expected to achieve a suitable solution based on past information and various future scenarios. To solve the proposed model, a meta-heuristic algorithm whose main idea is derived from the harmony search algorithm is proposed. Finally, numerical instances were utilized by the use of real stock information from the Iranian stocks market. The algorithm proposed in this research was compared with a genetic algorithm in terms of the quality of the generated solutions and the runtime of the algorithms, and the superiority of the proposed algorithm has been proven.

关键词： Portfolio selection multi-stage stochastic programming multi-objective optimization Harmony search algorithm

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A multi-stage stochastic programming model of lot-sizing and scheduling problems with machine eligibilities and sequence-dependent setups

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ANNALS OF OPERATIONS RESEARCH 2022年第1期311卷 35-50页

作者： Chen, Sheng-, I Su, Delvinia Natl Chiao Tung Univ Dept Ind Engn & Management 1001 Univ Rd Hsinchu Taiwan

We focus on the lot-sizing and scheduling problem with the additional considerations of machine eligibility, sequence-dependent setups, and uncertain demands. multi-stage stochastic programming is proposed. We analyze the problem structure and suggest ways for modeling and solving large-scale stochastic integer programs. The analysis compares deterministic and stochastic model solutions to assess demand variance effects under the circumstances of increasing, fluctuating, and decreasing demands. The result shows that the expected cost performance of the stochastic programming model outperforms that of the deterministic model, in particular, when the demand is highly uncertain in the circumstance of an upward market trend. Our study can apply to the wafer fab manufacturing and other industries that heavily restricted by machine eligibility and demand uncertainties.

关键词： Lot-sizing and scheduling Sequence-dependent setups Machine eligibility multi-stage stochastic programming

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A multi-stage stochastic programming-Based Offloading Policy for Fog Enabled IoT-eHealth

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2021年第2期39卷 411-425页

作者： Zhang, Long Cao, Bin Li, Yun Peng, Mugen Feng, Gang Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China

To meet low latency and real-time monitoring demands of IoT-eHealth, fog computing is envisioned as a key technology to offer elastic computing resource at the edge of networks. In this context, eHealth devices can offload collected healthcare data or computational expensive tasks to a nearby fog server. However, the mobility of the eHealth devices may make the connection between them to fog servers uncertain, resulting in possible migration between fog servers. In order to evaluate the impact of this uncertainty on decision-making for offloading and resource allocation, we formulate the task offloading problem as a multi-stage stochastic programming (MSSP), with aim of minimizing the total latency of offloading to determine whether to offload or not, how much workload to offload, how much computing resource to allocate, as well as whether to migrate or not. Different from the previous MSSP based work focusing on the workload assignment only, the proposed MSSP examines joint decisions of offloading, resource allocation, and migration, advancing the understanding of the interactions among these decisions. Furthermore, to reduce the computational complexity of MSSP, we design an efficient sub-optimal offloading policy based on Sample Average Approximation, called SAA-MSSP. We conduct extensive simulation experiments to validate the effectiveness of SAA-MSSP. The results show that SAA-MSSP can converge to a near-optimal solution quickly.

关键词： eHealth fog computing offloading multi-stage stochastic programming sample average approximation

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multi-Period Active Distribution Network Planning Using multi-stage stochastic programming and Nested Decomposition by SDDIP

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IEEE TRANSACTIONS ON POWER SYSTEMS 2021年第3期36卷 2281-2292页

作者： Ding, Tao Qu, Ming Huang, Can Wang, Zekai Du, Pengwei Shahidehpour, Mohammad Xi An Jiao Tong Univ Dept Elect Engn Xian 710049 Peoples R China Lawrence Livermore Natl Lab Livermore CA 94550 USA Elect Reliabil Council Texas Austin TX 76574 USA IIT ECE Dept Chicago IL 60616 USA

This paper presents a multi-period active distribution network planning (ADNP) with distributed generation (DG). The objective of the proposed ADNP is to minimize the total planning cost, subject to both investment and operation constraints. The paper proposes a multi-stage stochastic optimization model to address DG uncertainties over several periods, in which the decisions are made sequentially by only using the present-stage information. A nested decomposition method is proposed which applies the stochastic dual dynamic integer programming (SDDIP) method to address computational intractabilities of the proposed ADNP approach. The presented numerical results and discussions on a 33-bus distribution system and a large-scale 906-bus system verify the effectiveness of the proposed ADNP method and its solution method.

关键词： Planning Uncertainty stochastic processes Load modeling Substations programming Investment Distribution network planning uncertainty distributed energy resources multi-stage stochastic programming

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Rolling-window multi-stage stochastic programming for a Virtual Power Plant in the Real-time Market

Rolling-window Multi-stage Stochastic Programming for a Virt...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Luo, Zhe Guo, Ye Sun, Hongbin Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Tsinghua Berkeley Shenzhen Inst TB SI Shenzhen 518055 Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China

ISBN: (纸本)9781665405072

Considering the uncertainty of real-time market prices, a rolling-window multi-stage stochastic programming scheme is proposed to solve the dispatch problem of a virtual power plant (VPP), aiming to minimize its operating cost. On this basis, the decision-making process of the VPP is modeled as a continuous sequence procedure and the influence of uncertainty information that would be revealed in the future can be taken into account at the current time interval. Meanwhile, to better reflect the reality, the nonanticipativity constraints are introduced to guarantee that the decisions at the current time slot should be made only relies on the information already observed without the hindsight of future realizations of uncertainties. Experimental results show the proposed scheme could simulate the influence of future uncertain information on scheduling policy by determining operation strategies under different scenarios, and also specify the unique dispatch strategy of the VPP in a rolling continuously way based on nonanticipativity constraints.

关键词： Virtual power plant real-time market price uncertainty rolling-window multi-stage stochastic programming

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multi-stage stochastic programming for demand response optimization

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COMPUTERS & OPERATIONS RESEARCH 2020年 118卷

作者： Sahin, Munise Kubra Cavus, Ozlem Yaman, Hande Katholieke Univ Leuven Fac Econ & Business ORSTAT Leuven 3000 Belgium Bilkent Univ Dept Ind Engn Ankara 06800 Turkey

The increase in the energy consumption puts pressure on natural resources and environment and results in a rise in the price of energy. This motivates residents to schedule their energy consumption through demand response mechanism. We propose a multi-stage stochastic programming model to schedule different kinds of electrical appliances under uncertain weather conditions and availability of renewable energy. We incorporate appliances with chargeable and dischargeable batteries to better utilize the renewable energy sources. Our aim is to minimize the electricity cost and the residents' dissatisfaction. We use a scenario groupwise decomposition (group subproblem) approach to compute lower and upper bounds for instances with a large number of scenarios. The results of our computational experiments show that the approach is very effective in finding high quality solutions in small computation times. We provide insights about how optimization and renewable energy combined with batteries for storage result in peak demand reduction, savings in electricity cost and more pleasant schedules for residents with different levels of price sensitivity. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Smart grid Demand response multi-stage stochastic programming Scenario groupwise decomposition

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Energy management and optimal storage sizing for a shared community: A multi-stage stochastic programming approach

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APPLIED ENERGY 2019年 236卷 42-54页

作者： Hafiz, Faeza de Queiroz, Anderson Rodrigo Fajri, Poria Husain, Iqbal North Carolina State Univ Elect & Comp Engn Dept Raleigh NC 27695 USA North Carolina Cent Univ Sch Business Decis Sci Dept Durham NC USA North Carolina State Univ Civil Construct & Environm Engn Dept Raleigh NC 27695 USA Univ Nevada Elect & Biomed Engn Dept Reno NV 89557 USA

The aim of this paper is to propose a new energy management framework and storage sizing for a community composed of multiple houses and distributed solar generation. Uncertainties associated with solar generation and electricity demand are included to make the mathematical models more realistic, and as a result, provide more accurate control strategies to manage storage devices utilization. To evaluate that, a multi-stage stochastic program model designed to minimize community electricity purchase cost per day is used to support decision-making by creating control policies for energy management. Two different strategies are created to represent the interest of a single household (the individual energy management - IEM) and households that share their assets with the community (shared energy management - SEM). Our strategies consider time-of-use rates (ToU), load and resource variation during different seasons, with their distinct days of the year, to calculate net present value (NPV) associated with the energy savings. IEM and SEM are then used in a framework designed to establish the requirement of optimal energy storage size for each house of the community based on NPV values. The results of this study for an analysis considering a community with five houses show that the proposed SEM strategy reduces the overall electricity purchase costs for a summer day up to 11% and 3% compared with heuristic and IEM control respectively. Moreover, our results suggest that the application of the methodology increases peak energy savings up to 17%, scales up solar generation usage up to 23%, and the optimal storage size obtained in the shared community case reduces up to 50%.

关键词： Energy management multi-stage stochastic programming Optimal storage sizing Sampling-based decomposition Solar energy Energy storage

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A multi-stage stochastic programming model for relief distribution considering the state of road network

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2019年 123卷 64-87页

作者： Hu, Shaolong Han, Chuanfeng Dong, Zhijie Sasha Meng, Lingpeng Tongji Univ Sch Econ & Management 1239 Siping Rd Shanghai 200092 Peoples R China Texas State Univ 601 Univ Dr San Marcos TX 78666 USA Shanghai Maritime Univ China Inst FTZ Supply Chain 1550 Haigang Ave Shanghai 201306 Peoples R China

As an important aspect in disaster operations management, relief distribution has been challenged by lots of factors, such as unpredictable occurrence time, intensity and location of secondary disasters (e.g. aftershocks and landslides, which usually occur after an earthquake), and availability of vehicles. A multi-stage stochastic programming model is developed for disaster relief distribution with consideration of multiple types of vehicles, fluctuation of rental, and the state of road network. The state of road network is characterized using uncertain and dynamic road capacity. The scenario tree is employed to represent the uncertain and dynamic road capacity, and demonstrate the decision process of relief distribution. A progressive hedging algorithm (PHA) is proposed for solving the proposed model in large-scale size. Based on a real-world case of Yaan earthquake in China, numerical experiments are presented to study the applicability of the proposed model and demonstrate the effectiveness of the proposed PHA. Useful managerial insights are provided by conducting numerical analysis. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Emergency logistics Transportation Uncertain and dynamic road capacity multi-stage stochastic programming Progressive hedging algorithm

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multi-stage stochastic programming With Nonanticipativity Constraints for Expansion of Combined Power and Natural Gas Systems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2018年第1期33卷 317-328页

作者： Ding, Tao Hu, Yuan Bie, Zhaohong Xi An Jiao Tong Univ Sch Elect Engn State Key Lab Elect Insulat & Power Equipment Xian 710049 Shaanxi Peoples R China

A novel multi-stage stochastic programming model is proposed for the expansion coplanning of gas and power networks considering the uncertainties in net load demand. Meanwhile, the nonanticipativity constraints are taken into account to guarantee the decisions should only depend on the information of realized uncertainties up to the present stage. Compared with the traditional two-stage stochastic programming model, the proposed multi-stage stochastic programming model yields sequential investment decisions with the uncertainties revealed gradually over time, such that the investment decisions are capable of keeping future options open and can shift from "never be changed" decisions to a flexible "wait and see" decisions. The test on three systems shows the effectiveness of the proposed multi-stage stochastic programming model.

关键词： multi-stage stochastic programming nonanticipacivity constraints natural gas and power network expansion co-planning scenario tree sequential decision making

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Compromise policy for multi-stage stochastic linear programming: Variance and bias reduction

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COMPUTERS & OPERATIONS RESEARCH 2023年 153卷

作者： Xu, Jiajun Sen, Suvrajeet Univ Southern Calif Ming Hsieh Dept Elect & Comp Engn Univ Pk Los Angeles CA 90089 USA Univ Southern Calif Daniel J Epstein Dept Ind & Syst Engn Univ Pk Los Angeles CA 90089 USA

This paper focuses on algorithms for multi-stage stochastic linear programming (MSLP). We propose an ensemble method named the "compromise policy", which not only reduces the variance of the function approximation but also reduces the bias of the estimated optimal value. It provides a tight lower bound estimate with a confidence interval. By exploiting parallel computing, the compromise policy provides demonstrable advantages in performance and stability with marginally extra computational time. We further propose a meta-algorithm to solve the MSLP problems based on in-sample and out-of-sample optimality tests. Our meta -algorithm is incorporated within an SDDP-type algorithm for MSLP and significantly improves the reliability of the decisions suggested by SDDP. These advantages are demonstrated via extensive computations, which illustrate the effectiveness of our approach.

关键词： multi-stage stochastic programming Ensemble model Variance reduction Online optimization Distributed computing Bias reduction

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