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stochastic optimization of trading strategies in sequential electricity markets

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第1期308卷 400-421页

作者： Kraft, Emil Russo, Marianna Keles, Dogan Bertsch, Valentin Karlsruhe Inst Technol Chair Energy Econ Hertzstr 16 D-76187 Karlsruhe Germany NEOMA Business Sch Dept Finance Mont St Aignan France NTNU Business Sch Trondheim Norway Tech Univ Denmark Dept Technol Management & Econ Lyngby Denmark Ruhr Univ Bochum Chair Energy Syst & Energy Econ Bochum Germany

Quantity and price risks determine key uncertainties market participants face in electricity markets with increased volatility, for instance due to high shares of renewables. In the time from day-ahead until real-time, there lies a large variation in best available information, such as between forecasts and realizations of uncertain parameters like renewable feed-in and electricity prices. This uncertainty reflects on both the market outcomes and the quantity of renewable generation, making the determination of sound trading strategies across different market segments a complex task. The scope of the paper is to optimize day -ahead and intraday trading decisions jointly for a portfolio with controllable and intermittent renewable generation under consideration of risk. We include a reserve market, a day-ahead market and an intraday market in stochastic modeling and develop a multi-stage stochastic Mixed Integer Linear Program. We assess the profitability as well as the risk exposure, quantified by the conditional value at risk metric, of trading strategies following different risk preferences. We conclude that a risk-neutral trader mainly relies on the opportunity of higher expected profits in intraday trading, whereas risk can be hedged effectively by trading on the day-ahead. Finally, we show that reserve market participation implies various rationales, including the relation of expected reserve prices among each other, the relation of expected reserve prices to spot market prices, as well as the relation of the spot market prices among each other. (c) 2022 Elsevier B.V. All rights reserved.

关键词： OR in energy Electricity markets multi-stage stochastic programming Uncertainty modeling Risk modeling

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Viable healthcare supply chain network design for a pandemic

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ANNALS OF OPERATIONS RESEARCH 2023年第1期328卷 35-73页

作者： Alizadeh, Mehdi Pishvaee, Mir Saman Jahani, Hamed Paydar, Mohammad Mahdi Makui, Ahmad Iran Univ Sci & Technol Sch Ind Engn Tehran Iran RMIT Univ Sch Accounting Business Informat & Supply Chain Melbourne Vic Australia Babol Noshirvani Univ Technol Dept Ind Engn Babol Iran

The recent COVID-19 pandemic revealed that healthcare networks must have a flexible and effective structure. In this study, we develop a viable healthcare network design for a pandemic using a multi-stage stochastic approach. We propose a multi-level network that includes health centers, computed tomography scan centers, hospitals, and clinics. Patients have conditions to returning to normal life or quarantining at home. Three objectives are defined: maximizing the probability of patient recovery, minimizing the costs of all centers in the network, and minimizing the Coronavirus death rate. We investigate a real case study in Iran to demonstrate the model's applicability. Finally, we compare the healthcare supply chain network design in a pandemic with a normal situation to advise how the network can continue to remain viable.

关键词： Viable healthcare multi-stage stochastic programming COVID-19 Supply chain network design

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Large-scale financial planning via a partially observable stochastic dual dynamic programming framework

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QUANTITATIVE FINANCE 2023年第9期23卷 1341-1360页

作者： Lee, Jinkyu Kwon, Do-Gyun Lee, Yongjae Kim, Jang Ho Kim, Woo Chang Korea Adv Inst Sci & Technol KAIST Dept Ind & Syst Engn 291 Daehak Ro Daejeon 34141 South Korea Ulsan Natl Inst Sci & Technol UNIST Dept Ind Engn 50 UNIST Gil Ulsan 44919 South Korea Kyung Hee Univ Dept Ind & Management Syst Engn Dept Big Data Analyt Coll EngnGrad Sch 1732 Deogyeong Daero Yongin 17104 Gyeonggi Do South Korea

The multi-stage stochastic programming (MSP) approach is widely used to solve financial planning problems owing to its flexibility. However, the size of an MSP problem grows exponentially with the number of stages, and such problem can easily become computationally intractable. Financial planning problems often consider planning horizons of several decades, and thus, the curse of dimensionality can become a critical issue. stochastic dual dynamic programming (SDDP), a sampling-based decomposition algorithm, has emerged to resolve this issue. While SDDP has been successfully implemented in the energy domain, few applications of SDDP are found in the finance domain. In this study, we identify the major obstacle in using SDDP to solve financial planning problems to be the stagewise independence assumption and propose a partially observable SDDP (PO-SDDP) framework to overcome such limitations. We argue that the PO-SDDP framework, which models uncertainties using discrete-valued partially observable Markov states and introduces feasibility cuts, can properly address large-scale financial planning problems.

关键词： Financial planning Large-scale optimization multi-stage stochastic programming stochastic dual dynamic programming Partially observable Markov states >

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A rolling horizon approach for a multi-stage stochastic fixed-charge transportation problem with transshipment

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第3期301卷 912-922页

作者： Cavagnini, Rossana Bertazzi, Luca Maggioni, Francesca Deutsche Post Chair – Optimization of Distribution Networks RWTH Aachen University Kackertstr. 7B Aachen 52072 Germany Deparment of Economics and Management University of Brescia Contrada Santa Chiara n. 50 Brescia 25122 Italy Department of Management Information and Production Engineering University of Bergamo Viale Marconi 5 Dalmine (BG) 24044 Italy

We study a fixed-charge transportation problem under stochastic and dynamic demand. We propose a multi-stage mixed integer stochastic programming formulation, where the first-stage decision is the delivery from the supplier to the retailers, while transshipment is used, in addition to classical backordering as recourse decision. The objective is the minimization of the total expected cost. We prove that this problem is NP-hard and, through a worst-case analysis, that transshipment can provide significant cost savings. Extensive computational studies are carried out to evaluate the performance of a rolling horizon approach with respect to the optimal cost. Numerical results show that this heuristic provides effective solutions in short computational time. Managerial insights are finally drawn. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Logistics Fixed-charge transportation problem Transshipment multi-stage stochastic programming Rolling horizon approach

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Dynamic multi-type patient advance scheduling for a diagnostic facility considering heterogeneous waiting time targets and equity

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IISE TRANSACTIONS 2022年第6期54卷 521-536页

作者： Zhou, Liping Geng, Na Jiang, Zhibin Wang, Xiuxian Shanghai Jiao Tong Univ Sino US Global Logist Inst Shanghai Peoples R China Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai Peoples R China

This article studies a dynamic advance scheduling problem where multi-type patients arrive randomly to book the future service of a diagnostic facility in a public healthcare setting. The demand for the diagnostic facility generally arises from multiple sources such as emergency patients, inpatients, and outpatients. It is challenging for public hospital managers to dynamically allocate their limited capacities to serve the incoming multi-type patients not only to achieve their heterogeneous waiting time targets in a cost-effective manner but also to maintain equity among multi-type patients. To address this problem, a finite-horizon Markov Decision Process (MDP) model is proposed to minimize the total expected costs under the constraints of maintaining equity. Because of the complex structure of the feasible region and the high-dimensional state space, the property characterization of optimal scheduling policy and the exact solution of the MDP are intractable. To solve the MDP model with high-dimensional state and action spaces, we reformulate the MDP as a multi-stage stochastic programming model and propose a modified Benders decomposition algorithm based on new dual integer cuts to solve the model. Based on real data from our collaborating hospital, we perform extensive numerical experiments to demonstrate that our proposed approach yields good performance.

关键词： Patient advance scheduling waiting time target and equity Markov decision process multi-stage stochastic programming Benders decomposition

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Improving the performance of the stochastic dual dynamic programming algorithm using Chebyshev centers

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OPTIMIZATION AND ENGINEERING 2022年第1期23卷 147-168页

作者： Beltran, Felipe Finardi, Erlon C. Fredo, Guilherme M. de Oliveira, Welington Univ Fed Santa Catarina LabPlan Florianopolis SC Brazil INESC P&D Santos SP Brazil PSL Res Univ MINES ParisTech CMA Ctr Math Appl Sophia Antipolis France

In hydro predominant systems, the long-term hydrothermal scheduling problem (LTHS) is formulated as a multistage stochastic programming model. A classical optimization technique to obtain an operational policy is the stochastic dual dynamic programming (SDDP), which employs a forward step, for generating trial state variables, and a backward step to construct Benders-like cuts. To assess the quality of the obtained policy (the cuts obtained over the iterations), a confidence interval is computed on the optimality gap. As the SDDP is a cutting-plane based method, it exhibits slow convergence in large-scale problems. To improve computational efficiency, we explore different regions in which the cuts are usually constructed by the classical algorithm. For that, the cuts in the forward step are translated using ideas related to the definition of Chebyshev centers of certain polyhedrons. Essentially, the cuts are lifted by a parameter that vanishes along with iterations without harming the convergence analysis. The proposed technique is assessed on an instance of the Brazilian LTHS problem with individualized monthly decisions per plant, indicating a higher policy quality in comparison with the classical approach, since it computes lower optimality gaps throughout the iterative process.

关键词： Hydrothermal scheduling problem multi-stage stochastic programming stochastic dynamic dual programming Chebyshev centers

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Integrated forest biorefinery network design under demand uncertainty: a case study on canadian pulp & paper industry

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第16期60卷 4954-4972页

作者： Elaradi, Mohammad Belgasem Zanjani, Masoumeh Kazemi Nourelfath, Mustapha Concordia Univ Dept Mech Ind & Aerosp Engn Montreal PQ Canada Interuniv Res Ctr Enterprise Network Logist & Transportat CIRRELT Montreal PQ Canada Univ Laval Dept Mech Engn Laval PQ Canada

Transforming Pulp and Paper (P&P) mills into Integrated Forest Biorefineries (IFBR) is a prominent solution to save Canadian P&P industry that has been facing decline of conventional paper demand. We propose a comprehensive decision model for the design of IFBR value chains by taking the uncertain demand of bioproducts into consideration. In particular, we propose a multi-stage stochastic programming model to obtain the optimal investment plan over a long-term planning horizon in the presence of various market trends. We also develop a Monte-Carlo simulation platform to validate the proposed model and to compare its performance with alternative decision models. The model is applied to a realistic case study inspired from P&P companies in Canada, where the value of incorporating the dynamic nature of uncertain demand has been estimated. Further, we elaborate on the value of considering flexibility in terms of adjusting the investment plan in response to changes in the demand. Our results indicate that the demand for bioproducts has a substantial impact on the profitability of the IFBR. We also demonstrated the significant value of explicitly incorporating the uncertainty in IFBR network design as well as adapting the investment plan to the changes in the demand.

关键词： Integrated forest biorefineries supply chain design multi-stage stochastic programming pulp and paper Monte-Carlo simulation

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A flexible system design approach for multi-facility capacity expansion problems with risk aversion

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IISE TRANSACTIONS 2022年第2期55卷 187-200页

作者： Zhao, Sixiang Haskell, William B. Cardin, Michel-Alexandre Shanghai Jiao Tong Univ Sino US Global Logist Inst Shanghai Peoples R China Purdue Univ Krannert Sch Management W Lafayette IN 47907 USA Imperial Coll London Dyson Sch Design Engn London England

This article studies a model for risk aversion when designing a flexible capacity expansion plan for a multi-facility system. In this setting, the decision maker can dynamically expand the capacity of each facility given observations of uncertain demand. We model this situation as a multi-stage stochastic programming problem, and we express risk aversion through the Conditional Value-at-Risk (CVaR) and a mean-CVaR objective. We optimize the multi-stage problem over a tractable family of if-then decision rules using a decomposition algorithm. This algorithm decomposes the stochastic program over scenarios and updates the solutions via the subgradients of the function of cumulative future costs. To illustrate the practical effectiveness of this method, we present a numerical study of a decentralized waste-to-energy system in Singapore. The simulation results show that the risk-averse model can improve the tail risk of investment losses by adjusting the weight factors of the mean-CVaR objective. The simulations also demonstrate that the proposed algorithm can converge to high-performance policies within a reasonable time, and that it is also more scalable than existing flexible design approaches.

关键词： Capacity expansion problem system design real options risk aversion multi-stage stochastic programming decision rules

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Data-Driven Rolling Horizon Approach for Dynamic Design of Supply Chain Distribution Networks under Disruption and Demand Uncertainty

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DECISION SCIENCES 2022年第1期53卷 150-180页

作者： Fattahi, Mohammad Govindan, Kannan Shahrood Univ Technol Dept Ind Engn & Management Shahrood Iran Shanghai Maritime Univ China Inst FTZ Supply Chain Shanghai 201306 Peoples R China Univ Southern Denmark Ctr Sustainable Supply Chain Engn Danish Inst Adv Study Dept Technol & Innovat DK-5230 Odense M Denmark

We address the dynamic design of supply chain networks in which the moments of demand distribution function are uncertain and facilities' availability is stochastic because of possible disruptions. To incorporate the existing stochasticity in our dynamic problem, we develop a multi-stage stochastic program to specify the optimal location, capacity, inventory, and allocation decisions. Further, a data-driven rolling horizon approach is developed to use observations of the random parameters in the stochastic optimization problem. In contrast to traditional stochastic programming approaches that are valid only for a limited number of scenarios, the rolling horizon approach makes the determined decisions by the stochastic program implementable in practice and evaluates them. The stochastic program is presented as a quadratic conic optimization, and to generate an efficient scenario tree, a forward scenario tree construction technique is employed. An extensive numerical study is carried out to investigate the applicability of the presented model and rolling horizon procedure, the efficiency of risk-measurement policies, and the performance of the scenario tree construction technique. Several key practical and managerial insights related to the dynamic supply chain network design under uncertainty are gained based on the computational results.

关键词： Conic optimization Data-driven rolling horizon approach Dynamic supply chain network design multi-stage stochastic programming Risk management

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Glide paths for a retirement plan with deferred annuities

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JOURNAL OF PENSION ECONOMICS & FINANCE 2022年第4期21卷 565-581页

作者： Jang, Chul Clare, Andrew Owadally, Iqbal City Univ London London England Hanyang Univ Ansan South Korea

We construct investment glide paths for a retirement plan using both traditional asset classes and deferred annuities (DAs). The glide paths are approximated by averaging the asset proportions of stochastic optimal investment solutions. The objective function consists of power utility in terms of secured retirement income from purchased DAs, as well as a bequest that can be withdrawn before retirement. Compared with conventional glide paths and investment strategies, our DA-enhanced glide paths provide the investor with higher welfare gains, more efficient investment portfolios and more responsive retirement income patterns and bequest levels to different fee structures and personal preferences.

关键词： Deferred annuity glide-path strategy multi-stage stochastic programming retirement planning

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