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On conditional cuts for stochastic dual dynamic programming

EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION 2020年第2期8卷 173-199页

作者： van Ackooij, W. Warin, X. EDF R&D OSIRIS 7 Blvd Gaspard Monge F-91120 Palaiseau France

Multistage stochastic programs arise in many applications from engineering whenever a set of inventories or stocks has to be valued. Such is the case in seasonal storage valuation of a set of cascaded reservoir chains in hydro management. A popular method is stochastic dual dynamic programming (SDDP), especially when the dimensionality of the problem is large and dynamic programming is no longer an option. The usual assumption of SDDP is that uncertainty is stage-wise independent, which is highly restrictive from a practical viewpoint. When possible, the usual remedy is to increase the state-space to account for some degree of dependency. In applications, this may not be possible or it may increase the state-space by too much. In this paper, we present an alternative based on keeping a functional dependency in the SDDP-cuts related to the conditional expectations in the dynamic programming equations. Our method is based on popular methodology in mathematical finance, where it has progressively replaced scenario trees due to superior numerical performance. We demonstrate the interest of combining this way of handling dependency in uncertainty and SDDP on a set of numerical examples. Our method is readily available in the open-source software package StOpt.

关键词： stochastic programming SDDP algorithm Nonsmooth optimization Conditional expectations

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Simultaneous stochastic optimisation of mining complexes: Integrating progressive reclamation and waste management with contextual bandits

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INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT 2024年第9期38卷 701-721页

作者： Levinson, Zachary Dimitrakopoulos, Roussos McGill Univ Dept Min & Mat Engn COSMO Stochast Mine Planning Lab FDA Bldg 3450 Univ St Montreal PQ H3A 0E8 Canada

Managing environmental performance of waste dump facilities in mining complexes is an integral part of long-term production planning. Sustainable long-term production scheduling solutions are desired to mitigate risk and return the environment to a productive post-mining state. A simultaneous stochastic optimisation framework for long-term production scheduling in mining complexes is developed that integrates waste management and progressive reclamation. The waste dump placement schedule is jointly optimised with the extraction sequence, destination policy, and stockpiling decisions in a single stochastic mathematical programming framework. This includes the timing of progressive reclamation activities in parallel with production to enhance waste dump rehabilitation. Uncertainty related to the production of acid rock drainage is quantified by simulating geochemical properties of waste and managing the blending of uncertain waste properties within the optimisation framework. With respect to the framework for simultaneous stochastic optimisation, contextual bandits are explored to improve the metaheuristic solution approach and solve the corresponding large-scale optimisation model. The framework is tested in a multi-mine copper-gold mining complex leading to improved environmental performance. Risk of acid rock drainage is decreased by 52.5% in the waste dump facilities. Reclamation planning activities for meeting environmental requirements are scheduled prior to closure. The solution approach more effectively improves the objective function with contextual bandits leading to a 24% improvement in the study presented.

关键词： Waste management reclamation reinforcement learning stochastic programming contextual bandits long-term mine planning

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stochastic model for physician staffing and scheduling in emergency departments with multiple treatment stages

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第2期324卷 492-505页

作者： Marchesi, Janaina F. Hamacher, Silvio Peres, Igor Tona Pontif Catholic Univ Rio de Janeiro PUC Rio Dept Ind Engn Rua Marques de Sao Vicente 225 BR-22451040 Rio De Janeiro RJ Brazil

We propose anew solution for the Emergency Department (ED) staffing and scheduling problem, considering uncertainty inpatient arrival patterns, multiple treatment stages, and resource capacity. A two-stage stochastic mathematical programming model was developed. We employed a Sample Average Approximation (SAA) method to generate scenarios and a discrete event simulation to evaluate the results. The model was applied in a large hospital, with 72,988 medical encounters and 85 physicians in a ten-month period. Compared to the hospital's actual scheduling, we obtained an overall average waiting time reduction from 54.6 (54.0-55.1) to 16.8 (16.7-17.0) minutes and an average Length of Stay reduction from 102.1 (101.7-102.4) to 64.3 (64.2-64.5) minutes. Therefore, this study offers a stochastic model that effectively addresses uncertainties in EDs, aligning physician schedules with patient arrivals and potentially improving the quality of service by reducing waiting times.

关键词： OR in health services stochastic programming Physician staffing and scheduling Discrete event simulation Emergency department

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Enhancing disaster resilience: optimizing debris management for end-of-life buildings amidst uncertainty

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2025年第4期42卷 406-421页

作者： Nayeem, Moddassir Khan Abbaas, Omar Univ Texas San Antonio Dept Mech Aerosp & Ind Engn 1 UTSA CircleEB 3-04-54 San Antonio TX 78249 USA

This study develops a stochastic Mixed Integer Linear programming (SMILP) model to optimize the reverse logistics network for debris generated from proactive demolition of end-of-life buildings. Unlike most existing research, this study addresses proactive strategies for the Mitigation stage of disaster management. Our model identifies the optimal number of landfilling areas and sorting facilities, factoring in uncertainties in debris quantity and material quality. It incorporates environmental constraints, such as mandatory sorting processes and recycling thresholds. Multiple scenarios are considered, each with unique parameter values and occurrence probabilities, with the overall objective of minimizing net costs across all scenarios. A realistic case study is used to illustrate the model, demonstrating its capacity to reduce post-disaster recovery costs, improve operational efficiency, and balance financial and environmental considerations. This study offers insights for decision-makers, advocating proactive end-of-life buildings' management as a disaster preparedness and sustainable practices.

关键词： Disaster management end-of-life building stochastic programming recycling reverse logistics

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Optimal Risk Management in Grid-Connected Photovoltaic System Design Under Environmental and Economic Changes: A Case Study in Saudi Arabia's Climate

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PROCESS INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY 2025年第2期9卷 553-574页

作者： Attia, Ahmed M. Darghouth, Mohamed N. Mohammed, Awsan Abdel-Aal, Mohammed A. M. King Fahd Univ Petr & Minerals KFUPM Dept Ind & Syst Engn Dhahran 31261 Saudi Arabia Laval Univ Dept Mech Engn Quebec City PQ G1V0A6 Canada King Fahd Univ Petr & Minerals KFUPM Dept Construct Engn & Management Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Smart Mobil & Logist IRC Dhahran 31261 Saudi Arabia Interuniv Res Ctr Enterprise Networks Logist & Tra Montreal PQ Canada

Energy plays a pivotal role in addressing the climate crisis, with fossil fuel combustion being a significant source of greenhouse gas emissions. To combat this, industrial nations are urged to transition to renewable energy sources like solar photovoltaic systems. However, optimizing photovoltaic systems requires consideration of technical, economic, and environmental factors. In this study, we devised a two-phase stochastic programming method for optimizing a photovoltaic system connected to a power grid. Our focus is on incorporating economic and environmental uncertainties into risk scenarios. The aim is to ascertain the optimal number of photovoltaic array installations, accounting for importing energy from the grid and exporting surplus energy to external utilities. Environmental factors and market change parameters are dynamically determined during decision-making, while model parameters are deterministic. The initial optimization stage determines the necessary number of photovoltaic modules. Given the scenario-driven nature of photovoltaic module selection, energy allocation to clients, external utilities, and grid interactions vary accordingly. Our proposed approach employs risk metrics to address environmental, economic, and reliability considerations in decision-making. A case study for sizing a photovoltaic system to power a housing area at King Fahd University of Petroleum and Minerals demonstrates the effectiveness of our approach. Sensitivity analysis highlights key trade-offs, showing the proposed system can break even at $1.79 million per year, generate 388.53 megawatt-hours of electricity annually, and reduce carbon dioxide emissions by 50.52 kg per year. Our study underscores the importance of integrating renewable energy solutions into the global energy mix to mitigate climate change impacts.

关键词： PV system optimization Environmental and economic uncertainties Risk management stochastic programming Scenario-based optimization

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Risk-constrained Energy Management Strategy for a Commercial Campus Considering Comprehensive Reserves Against Islanding Conditions

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CSEE Journal of Power and Energy Systems 2023年第5期9卷 1797-1807页

作者： Zheming Liang Desong Bian Xiaohu Zhang Di Shi GEIRI North America San JoseCAUSA.Z.M.Liang is also with Huaneng Renewables Co.Ltd.Beijing 100036China

This paper proposes an optimal risk-constrained energy management strategy for commercial buildings in a commercial campus with islanding *** goal is to minimize the total operation and maintenance costs,while maximizing comprehensive comfort levels for the occupants.A two-stage riskconstrained,scenario-based stochastic optimization approach is adopted to handle various uncertainties associated with the energy management process,such as power generation of rooftop solar panels,arrival state-of-charges,and arrival/departure time of plug-in electric vehicles,intermittent load demand,and uncertain grid-connection conditions.A conditional-valueat-risk method is introduced to provide a risk-averse energy management *** face the challenge of both reducing the computational burden and maintaining the accuracy of the stochastic programming,an advanced scenario reduction method is *** simulation results validate the effectiveness of the proposed energy management strategy for minimizing total operating and maintenance costs of commercial buildings with islanding capabilities,while maximizing comprehensive comfort levels of the occupants.

关键词： Comprehensive comfort levels CVaR energy management islanding capability reserve stochastic programming uncertainty

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Scenario-robust pre-disaster planning for multiple relief items

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ANNALS OF OPERATIONS RESEARCH 2024年第3期335卷 1241-1266页

作者： Yang, Muer Kumar, Sameer Wang, Xinfang Fry, Michael J. Univ St Thomas Opus Coll Business Dept Operat & Supply Chain Management Minneapolis MN 55403 USA Georgia Southern Univ Parker Coll Business Dept Enterprise Syst & Analyt POB 7998 Statesboro GA 30460 USA Univ Cincinnati Carl H Lindner Coll Business Univ Dept Operat Business Analyt & Informat Syst Cincinnati OH 45221 USA

The increasing vulnerability of the population from frequent disasters requires quick and effective responses to provide the required relief through effective humanitarian supply chain distribution networks. We develop scenario-robust optimization models for stocking multiple disaster relief items at strategic facility locations for disaster response. Our models improve the robustness of solutions by easing the difficult, and usually impossible, task of providing exact probability distributions for uncertain parameters in a stochastic programming model. Our models allow decision makers to specify uncertainty parameters (i.e., point and probability estimates) based on their degrees of knowledge, using distribution-free uncertainty sets in the form of ranges. The applicability of our generalized approach is illustrated via a case study of hurricane preparedness in the Southeastern United States. In addition, we conduct simulation studies to show the effectiveness of our approach when conditions deviate from the model assumptions.

关键词： Humanitarian logistics Mixed-integer optimization OR in disaster relief stochastic programming Scenario-robust optimization

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Optimal sizing and allocation of PV-DG and DSTATCOM in the distribution network with uncertainty in consumption and generation

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DISCOVER APPLIED SCIENCES 2025年第5期7卷 1-25页

作者： Ebrahimi, Ahad Moradlou, Majid Bigdeli, Mehdi Mashhadi, Mostafa Rajabi Islamic Azad Univ Dept Elect Engn Zanjan Branch Zanjan Iran Sadjad Univ Technol Dept Elect Engn Mashhad Iran

The use of distributed generation resources and Flexible AC transmission (FACT) devices to improve technical constraints and reduce dependence on the upstream network eliminates the need to build new power plants. Maximizing line capacity utilization is a priority for the electricity industry. The advantages of using FACT devices include increasing line throughput and preventing line and bus congestion, improving bus voltage profiles, reducing line losses, preventing sub synchronous resonance, and so on. This study examined optimal sizing and allocation of photovoltaic distributed generation (PV-DG) and DSTATCOM. To solve the optimization problem, teaching-learning-based optimization (TLBO) was employed. The algorithm was run in the IEEE 33-bus standard test system. Because of the random nature of the consumption load and the random production nature of renewable DG units, the uncertainty of consumption and production was examined through stochastic programming methods. Moreover, to choose the scenario with the highest probability, the Monte Carlo method was employed. The scenarios included load certainty-PV generation uncertainty, load-PV generation uncertainty, and load-PV generation certainty. Injecting reactive and active power of PV-DG and DSTATCOM improved voltage stability index (VSI) to 0.9745 p.u (36.8%) and reduced the amount of power loss to 10.95 kW (94.8%). Furthermore, comparing TLBO's results with other algorithms verified its accuracy and minimum solution time.

关键词： DSTATCOM DG Uncertainty TLBO Monte Carlo method stochastic programming

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Multi-period emergency facility location-routing problems under uncertainty and risk aversion

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SOCIO-ECONOMIC PLANNING SCIENCES 2024年 96卷

作者： Hu, Qing-Mi Hu, Yan Li, Xiaoping Jiangsu Univ Sci & Technol Sch Econ & Management Zhenjiang 212100 Peoples R China

This paper addresses a multi-period emergency facility location-routing problem, in which the uncertainties in material demands and transportation time, as well as dynamic inventory replenishment and carryover are incorporated in the design of multi-level emergency logistics networks. To measure the risks stemming from uncertain transportation time, mean-CVaR method is used. Then, a risk-averse stochastic programming model for the presented problem is formulated to minimize the total rescue time of the network. Moreover, a genetic algorithm is developed to solve the proposed model. Extensive numerical experiments including the randomly generated instances and a case study on the Wenchuan earthquake in China are conducted to verify the effectiveness of the presented model and algorithm. Experimental results show that the genetic algorithm significantly performs better than the Gurobi solver in terms of both solution quality and solution time.

关键词： Emergency facility location-routing Uncertainty Dynamic inventory replenishment and carryover Risk-averse stochastic programming Genetic algorithm

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A non-cooperative multi-leader one-follower integrated generation maintenance scheduling problem under the risk of generation units' disruption and variation in demands

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ANNALS OF OPERATIONS RESEARCH 2024年第3期336卷 1591-1635页

作者： Hassanpour, Atefeh Roghanian, Emad Bashiri, Mahdi KN Toosi Univ Technol Dept Ind Engn Tehran Iran Coventry Univ Ctr Business Soc Coventry England

The generation maintenance scheduling deals with a time sequence of preventive maintenance outages for a given set of generation units in an electricity market subject to power system restrictions. Incorporating a leader-follower structure in generation maintenance scheduling models is essential because of the inherent conflict between the interests of an independent system operator (ISO) and generation companies (GENCOs). The present paper proposes a new preventive maintenance scheduling model for generation companies facing the risk of involving generation units' disruption and demand variations while ensuring the reliability of the power system. Each GENCO proposes the maintenance schedule of its generation units to the ISO in a non-cooperative manner intending to maximize its net profit. Then ISO reacts to the aggregated schedule according to the power system's reliability index. Thus, a new formula is developed to consider all the interactions between the power system's stakeholders. In this regard, a stochastic multi-leader one-follower approach is applied. The GENCOs are considered independent leaders at the upper-level and the ISO is considered a follower at the lower-level. Then an equivalent single-level counterpart model is presented for each leader. So, the whole problem is converted into multiple individual stochastic single-level models, and then the Nash Equilibrium concept is used to determine GENCO equilibrium strategies. The proposed methodology is evaluated using some modified IEEE reliability test systems. The numerical analysis confirms that the proposed model is more effective in cases with higher uncertainties. Moreover, the performed analysis demonstrated the importance of applying a bi-level approach to the problem. Finally, the superiority of the proposed approach compared to the existing one is confirmed.

关键词： Generation maintenance scheduling Non-cooperative game Multi-leader one-follower approach Power system's reliability stochastic programming

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