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Non-anticipative risk-averse analysis with effective scenarios applied to long-term hydrothermal scheduling

COMPUTATIONAL & APPLIED MATHEMATICS 2023年第3期42卷 1-12页

作者： Arsie, Karla Cristiane Gonzaga, Clovis Caesar Karas, Elizabeth Wegner Pericaro, Gislaine Aparecida Pontificia Univ Catolica Parana Curitiba Parana Brazil Inst Lactec Curitiba Parana Brazil Univ Fed Parana Curitiba Parana Brazil Univ Estadual Parana Campo Mourao Brazil

We deal with long-term operation planning problems of hydrothermal power systems by considering scenario analysis and risk aversion. This is a stochastic sequential decision problem whose solution must be non-anticipative, in the sense that a decision at a stage cannot use knowledge of the future. We propose strategies to reduce the number of scenarios in such way that the decision obtained by solving the non-anticipative risk-averse problem considering the subset of effective scenarios is as reliable as the decision from the whole set of scenarios. Numerical experiments are presented for validation of the strategies proposed by solving the problem for two test systems with real data extracted of the Brazilian interconnected system.

关键词： Non-anticipative scenario analysis stochastic programming Nonlinear optimization Hydrothermal power systems

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Optimized Scenario Reduction: Solving Large-Scale stochastic Programs with Quality Guarantees

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INFORMS JOURNAL ON COMPUTING 2023年第4期35卷 886-908页

作者： Zhang, Wei Wang, Kai Jacquillat, Alexandre Wang, Shuaian Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Hong Kong Peoples R China MIT Sloan Sch Management Cambridge MA 02142 USA

stochastic programming involves large-scale optimization with exponentially many scenarios. This paper proposes an optimization-based scenario reduction approach to generate high-quality solutions and tight lower bounds by only solving small-scale instances, with a limited number of scenarios. First, we formulate a scenario subset selection model that optimizes the recourse approximation over a pool of solutions. We provide a theoretical justification of our formulation, and a tailored heuristic to solve it. Second, we propose a scenario assortment optimization approach to compute a lower bound-hence, an optimality gap-by relaxing nonanticipativity constraints across scenario "bundles." To solve it, we design a new column-evaluation-and-generation algorithm, which provides a generalizable method for optimization problems featuring many decision variables and hard-to-estimate objective parameters. We test our approach on stochastic programs with continuous and mixed-integer recourse. Results show that (i) our scenario reduction method dominates scenario reduction benchmarks, (ii) our scenario assortment optimization, combined with column-evaluation-and-generation, yields tight lower bounds, and (iii) our overall approach results in stronger solutions, tighter lower bounds, and faster computational times than state-of-the-art stochastic programming algorithms.

关键词： Massachusetts 02142 stochastic programming scenario reduction column evaluation and generation

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Dynamic optimization with side information

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期304卷 634-651页

作者： Bertsimas, Dimitris McCord, Christopher Sturt, Bradley MIT Operat Res Ctr Cambridge MA USA Univ Illinois Dept Informat & Decis Sci Chicago IL 60607 USA

We develop a tractable and flexible data-driven approach for incorporating side information into multi-stage stochastic programming. The proposed framework uses predictive machine learning methods (such as k-nearest neighbors, kernel regression, and random forests) to weight the relative importance of vari-ous data-driven uncertainty sets in a robust optimization formulation. Through a novel measure concen-tration result for a class of supervised machine learning methods, we prove that the proposed approach is asymptotically optimal for multi-period stochastic programming with side information. We also describe a general-purpose approximation for these optimization problems, based on overlapping linear decision rules, which is computationally tractable and produces high-quality solutions for dynamic problems with many stages. Across a variety of multi-stage and single-stage examples in inventory management, finance, and shipment planning, our method achieves improvements of up to 15% over alternatives and requires less than one minute of computation time on problems with twelve stages.(c) 2022 Elsevier B.V. All rights reserved.

关键词： stochastic programming Distributionally robust optimization Machine learning Dynamic optimization

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Drones for relief logistics under uncertainty after an earthquake

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第1期310卷 117-132页

作者： Dukkanci, Okan Koberstein, Achim Kara, Bahar Y. European Univ Viadrina Fac Business Adm & Econ D-15230 Frankfurt oder Germany Bilkent Univ Dept Ind Engn TR-06800 Ankara Turkiye

This study presents a post-disaster delivery problem called the relief distribution problem using drones under uncertainty, in which critical relief items are distributed to disaster victims gathered at assembly points after a disaster, particularly an earthquake. Because roads may be obstructed by debris after an earthquake, drones can be used as the primary transportation mode. As the impact of an earthquake cannot be easily predicted, the demand and road network uncertainties are considered. Additionally, the objective is to minimize the total unsatisfied demand subject to a time-bound constraint on the deliv-eries, as well as the range and capacity limitations of drones. A two-stage stochastic programming and its deterministic equivalent problem formulations are presented. The scenario decomposition algorithm is implemented as an exact solution approach. To apply this study to real-life applications, a case study is conducted based on the western (European) side of Istanbul, Turkey. The computational results are used to evaluate the performance of the scenario decomposition algorithm and analyze the value of stochas-ticity and the expected value of perfect information under different parametric settings. We additionally conduct sensitivity analyses by varying the key parameters of the problem, such as the time-bound and capacities of the drones. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Humanitarian logistics Relief distribution Drone delivery Uncertainty stochastic programming

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The integrated lot-sizing and cutting stock problem under demand uncertainty

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第20期61卷 6691-6717页

作者： Curcio, Eduardo de Lima, Vinicius L. Miyazawa, Flavio K. Silva, Elsa Amorim, Pedro INESC TEC Campus FEUP Porto Portugal Univ Estadual Campinas Inst Comp Campinas SP Brazil Univ Porto Fac Engn Porto Portugal INESC TEC Campus FEUP Rua Dr Roberto FriasS-N P-4200465 Porto Portugal

Interest in integrating lot-sizing and cutting stock problems has been increasing over the years. This integrated problem has been applied in many industries, such as paper, textile and furniture. Yet, there are only a few studies that acknowledge the importance of uncertainty to optimise these integrated decisions. This work aims to address this gap by incorporating demand uncertainty through stochastic programming and robust optimisation approaches. Both robust and stochastic models were specifically conceived to be solved by a column generation method. In addition, both models are embedded in a rolling-horizon procedure in order to incorporate dynamic reaction to demand realisation and adapt the models to a multistage stochastic setting. Computational experiments are proposed to test the efficiency of the column generation method and include a Monte Carlo simulation to assess both stochastic programming and robust optimisation for the integrated problem. Results suggest that acknowledging uncertainty can cut costs by up to 39.7%, while maintaining or reducing variability at the same time.

关键词： Lot-sizing cutting stock stochastic programming robust optimisation column generation rolling-horizon

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An optimization scheme for designing power rationing schedules in a long-term power shortage

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ELECTRIC POWER SYSTEMS RESEARCH 2023年 225卷

作者： Mou, Yuting Wang, Beibei Shen, Zhan Southeast Univ Sch Elect Engn Nanjing 210018 Jiangsu Peoples R China

Power rationing is the last resort to prevent large-scale blackouts after demand response resources are exhausted during power shortages. However, the traditional rolling blackout method has been criticized for causing significant losses. To address this issue, this paper proposes a novel optimization scheme for designing power rationing schedules in a long-term power shortage, which considers different types of consumers at multiple time scales. The proposed scheme takes into account economic losses due to limited power supply, disruptions in industrial chains, and the social costs caused by excessive activation of the same consumers. First, consumers are categorized as maintenance consumers, work-shift consumers, and fast-response consumers based on their consumption characteristics. Then, a two-stage stochastic programming model is presented to account for long-term uncertainties in power shortage, which yields the maintenance and work-shifting schedules. Given these predetermined schedules, once the demand-supply gap is better revealed in real-time, a dispatch model for fast-response consumers is solved to generate the activation schedules. The case study demonstrates that the proposed scheme can effectively reduce costs when compared to the rolling blackout approach, as well as respecting industrial chain coupling and fairness.

关键词： Power rationing Orderly power utilization stochastic programming Demand side management Multiple time scales Maintenance tasks scheduling Industrial chain Fairness

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Collaborative stochastic expansion planning of cyber-physical system considering extreme scenarios

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IET GENERATION TRANSMISSION & DISTRIBUTION 2023年第10期17卷 2419-2434页

作者： Zhang, Yiwei Li, Chengze Wan, Haiyang Shi, Qingxin Liu, Wenxia Xu, Yanhui North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China

In this paper, a collaborative stochastic expansion planning model of cyber-physical system (CPS) with resilience constraints is proposed. The model can reduce the coupling risk and enhance the resilience under extreme scenarios, from the perspective of the structural/functional coupling between the physical and cyber systems. The model is to collaboratively optimize expansion transmission line siting, expansion communication fibre siting, and service routing distribution to minimize total investment cost. Constraints are divided into conventional constraints and resilience constraints. In the resilience constraints, the impact of cyber failures on the dispatch strategy is analyzed, and the load shedding in stochastic scenarios is limited to guarantee the resilience of the planning scheme. Particularly, to reach a stable solution for the stochastic planning, a mixed scenario set is proposed based on the probabilistic typhoon model and the complex network theory. Finally, the model is solved by the progressive hedging (PH) algorithm. The case studies of the IEEE RTS-79 test system demonstrate that, compared with the traditional independent planning model, the model proposed in this paper performs better in limiting load loss and enhancing resilience under extreme scenarios.

关键词： collaborative planning cyber physical system progressive hedging algorithm resilience stochastic planning cyber-physical systems power system planning stochastic programming

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An Enhanced NSGA-II for Solving Berth Allocation and Quay Crane Assignment Problem With stochastic Arrival Times

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第1期24卷 459-473页

作者： Ji, Bin Huang, Han Yu, Samson S. Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Deakin Univ Sch Engn Waurn Ponds Vic 3216 Australia

The berth allocation and quay crane assignment problem (BACAP) is an important port operation planning problem. To obtain an effective and reliable schedule of berth and quay crane (QC), this study addresses the BACAP with stochastic arrival times of vessels. An efficient method combining scenario generation is presented to simulate the stochastic arrival times. After then, a mixed integer linear programming (MILP) model is established, aiming to minimize the expectation of the vessels' total stay time in port. A multi-objective constraint-handling (MOCH) strategy is adopted to reformulate the developed model, which converts constraint violations into an objective, thus transforming the single-objective optimization model with complex constraints into a dual-objective optimization model with only easy-handling constraints. Then an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) is proposed to solve the dual-objective model, in which a neighborhood search algorithm and a search bias mechanism are incorporated to strengthen the local exploitation capability. Furthermore, a repair method (RM), penalty function (PF) and the superiority of feasible solutions (SF) strategy for constraint handling are designed respectively and incorporated with genetic algorithm to solve the original single-objective optimization model. Finally, numerical experiments on instances in the literature are conducted to validate the effectiveness of the MOCH and the proposed ENSGA-II. The results show that the average total stay time of vessels is reduced when stochastic arrival times are considered. Comparison results with another two multi-objective methods and three single-objective methods combined with different constraint-handling strategies corroborate the superiority of the proposed ENSGA-II and MOCH.

关键词： stochastic processes Seaports Optimization programming Heuristic algorithms Resource management Costs Berth allocation and quay crane assignment stochastic programming scenario simulation multi-objective constraint handling

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Robustness of stochastic programs with endogenous randomness via contamination

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第3期305卷 1259-1272页

作者： Kopa, Milos Rusy, Tomas Charles Univ Prague Fac Math & Phys DPMS Sokolovska 83 Prague 18675 Czech Republic

Investigating stability of stochastic programs with respect to changes in the underlying probability distri-butions represents an important step before deploying any model to production. Often, the uncertainty in stochastic programs is not perfectly known, thus it is approximated. The stochastic distribution's mis-specification and approximation errors can affect model solution, consequently leading to suboptimal de-cisions. It is of utmost importance to be aware of such errors and to have an estimate of their influence on the model solution. One approach, which estimates the possible impact of such errors, is the contam-ination technique. The methodology studies the effect of perturbation in the probability distribution by some contaminating distribution on the optimal value of stochastic programs. Lower and upper bounds, for the optimal values of perturbed stochastic programs, have been developed for numerous types of stochastic programs with exogenous randomness. In this paper, we first extend the current results by de-veloping a tighter lower bound applicable to wider range of problems. Thereafter, we define contamina-tion for decision-dependent randomness stochastic programs and prove various lower and upper bounds. We split the various cases into two separate sub-classes based on whether the feasibility set is fixed or decision-dependent and discuss several tractable formulations. Finally, we illustrate the contamination results on a real example of a stochastic program with endogenous randomness from a financial industry.(c) 2022 Elsevier B.V. All rights reserved.

关键词： stochastic programming Decision-dependent randomness Contamination

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Resource planning strategies for healthcare systems during a pandemic

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第1期304卷 192-206页

作者： Fattahi, Mohammad Keyvanshokooh, Esmaeil Kannan, Devika Govindan, Kannan Northumbria Univ Newcastle Business Sch Newcastle Upon Tyne England Shanghai Maritime Univ China Inst FTZ Supply Chain Shanghai 201306 Peoples R China Yonsei Univ Yonsei Frontier Lab Seoul South Korea Univ Southern Denmark Danish Inst Adv Study Ctr Sustainable Supply Chain Engn Dept Technol & Innovat Campusvej 55 Odense Denmark Texas A&M Univ Mays Business Sch Dept Informat & Operat Management College Stn TX 77845 USA

We study resource planning strategies, including the integrated healthcare resources' allocation and shar-ing as well as patients' transfer, to improve the response of health systems to massive increases in de-mand during epidemics and pandemics. Our study considers various types of patients and resources to provide access to patient care with minimum capacity extension. Adding new resources takes time that most patients don't have during pandemics. The number of patients requiring scarce healthcare resources is uncertain and dependent on the speed of the pandemic's transmission through a region. We develop a multi-stage stochastic program to optimize various strategies for planning limited and necessary health-care resources. We simulate uncertain parameters by deploying an agent-based continuous-time stochas-tic model, and then capture the uncertainty by a forward scenario tree construction approach. Finally, we propose a data-driven rolling horizon procedure to facilitate decision-making in real-time, which miti-gates some critical limitations of stochastic programming approaches and makes the resulting strategies implementable in practice. We use two different case studies related to COVID-19 to examine our opti-mization and simulation tools by extensive computational results. The results highlight these strategies can significantly improve patient access to care during pandemics;their significance will vary under dif-ferent situations. Our methodology is not limited to the presented setting and can be employed in other service industries where urgent access matters. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： OR in health services COVID-19 pandemic Resource sharing and allocation Patients? transfers Multi -stage stochastic programming Data -driven rolling horizon

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