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Profit-based unit commitment models with price-responsive decision-dependent uncertainty

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第3期314卷 1052-1064页

作者： Lejeune, Miguel A. Dehghanian, Payman Ma, Wenbo George Washington Univ Washington DC 20052 USA

Highlighting the increasing importance of demand elasticity in electricity markets and its impact on the revenues of power generating companies, this paper proposes new profit-based unit commitment models that effectively capture the uncertainty in the willingness-to-pay the price set for the elastic demand. To develop a new revenue scheme for power generating companies, we use a coupling function to model the willingness-to-pay response of the elastic demand as a decision-dependent source of uncertainty. The coupling function reflects how power generating companies' pricing decisions may influence the market appeal (i.e., the buyer's willingness-to-pay) and how it affects their revenues. The optimization models are stochastic mixed-integer nonlinear problems with nonconvex continuous relaxations and are not amenable to a numerical solution in their original forms. We devise a convexification reformulation method and derive valid inequalities to strengthen the formulation. We propose a learning framework to parameterize the willingnessto-pay functions and the concept of the value of the decision-dependent solution to quantify the value of the uncertainty modeling approach. Numerical tests on power systems of various sizes, demand portfolios, and price elasticity levels show (i) how the valid inequalities speed up the solution process, (ii) the benefits of properly modeling decision-dependent uncertainty and demand elasticity, and (iii) how the incorporation of decision-dependent uncertainty in demand elasticity can change the power generating companies' decisions and revenue estimation.

关键词： stochastic programming Price responsiveness Profit-based unit commitment Willingness-to-pay Decision-dependent uncertainty

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stochastic optimal investment strategy for net-zero energy houses

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IET RENEWABLE POWER GENERATION 2024年第15期18卷 2880-2891页

作者： Li, Mengmou Tanaka, Taichi Carnerero, A. Daniel Wasa, Yasuaki Hirata, Kenji Fujisaki, Yasumasa Ushifusa, Yoshiaki Hatanaka, Takeshi Tokyo Inst Technol Sch Engn Dept Syst & Control Engn Tokyo Japan Waseda Univ Fac Sci & Engn Sch Adv Sci & Engn Dept Elect Engn & Biosci Tokyo Japan Univ Toyama Sch Engn Dept Elect & Elect Engn Toyama Japan Osaka Univ Grad Sch Informat Sci & Technol Dept Informat & Phys Sci Osaka Japan Univ Kitakyushu Fac Econ & Business Adm Fukuoka Japan Tokyo Inst Technol Sch Engn Dept Syst & Control Engn Tokyo 1528550 Japan

In this research, Net-Zero Energy Houses (ZEHs) is investigated that harness regionally produced electricity from photovoltaic (PV) panels and fuel cells, integrating them into a local power system in pursuit of achieving carbon neutrality. This article examines the impact of electricity sharing among users who are working towards attaining ZEH status through the integration of PV panels and battery storage devices. Two potential scenarios are proposed: the first assumes that all users individually invest in storage devices, hence minimizing their costs on a local level without energy sharing;the second envisions cost minimization through the collective use of a shared storage device, managed by a central manager. These two scenarios are formulated as a stochastic convex optimization and a cooperative game, respectively. To tackle the stochastic challenges posed by multiple random variables, the Monte Carlo sample average approximation (SAA) is applied to obtain solvable deterministic convex problems. Last, to demonstrate the practical applicability of these models, the proposed scenarios are implemented in the Jono neighborhood in Kitakyushu, Japan. This research explores the integration of locally produced solar and fuel cell power into Net-Zero Energy Houses (ZEH), focusing on the implications of electricity sharing among users through the use of photovoltaic panels and battery storage. The study proposes two scenarios for cost minimization: individual investment in storage devices without energy sharing, and a collective approach using a shared storage device. Utilizing Monte Carlo sample average approximation to address the stochastic nature of the problem, the applicability of these models is demonstrated in the Jono neighborhood in Kitakyushu, Japan. image

关键词： battery storage plants game theory Monte Carlo methods optimisation Solar Photovoltaic Systems stochastic programming

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Empirical risk minimization for big data driven prescriptive analytics: An exploration of two-stage stochastic programs with recourse

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 261卷

作者： Clausen, Johan Bjerre Bach Li, Hongyan Forget, Nicolas Aalborg Univ Fac Engn & Sci Dept Mat & Prod Fibigerstr 14 DK-9220 Aalborg Denmark Aarhus Univ Sch Business & Social Sci Dept Econ & Business Econ Fuglesangs 4 DK-8210 Aarhus Denmark Johannes Kepler Univ Linz Inst Prod & Logist Management Altenberger Str 69 A-4040 Linz Austria

In the operations research literature, data driven analyses using big data are receiving more and more interest and attention. However, big data driven operational analyses are still limited in scope, with only unconstrained big data driven problems, e.g. the newsvendor problem, being comprehensively explored. If big data driven analyses are to become a core part of operations research and practice, one must be able to formulate and solve constrained big data driven models. We therefore propose a direct empirical risk minimization (DERM) method for formulating and solving a class of constrained big data driven operation research problems. In big data driven problems, relevant operational prescriptions are dependent on a set of observed features. In this paper, we assume linear feature relationships, which enable us to fundamentally base the DERM method on linear stochastic programming. The assumption of linear feature relationships is also seen in the literature on big data driven newsvendor models. We prove that if a big data driven solution exists, the DERM method will always, for the class of constrained problems we study, arrive at a feasible solution of the initial opera tional problem. Moreover, we exemplify the DERM method by formulating and solving two specific big data driven operational problems. In the numerical study of the two operational problems, we show, that under linear demand, the DERM method outperforms, with regard to cost, two benchmark methods and a big data driven method from literature.

关键词： Operations research Big data Data driven operations research Empirical risk minimization stochastic programming

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Home-based care and center-based care: From being alternatives to being synergistic. Optimization models to support flexible care delivery

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2025年 131卷

作者： Cappanera, Paola Visintin, Filippo Vannelli, Sara Univ Florence Dipartimento Ingn Informaz Via Santa Marta 3 I-50139 Florence Italy Univ Florence Dipartimento Ingn Ind Viale Morgagni 40-44 I-50134 Florence Italy

The delivery of health care services, especially for patients with chronic conditions and/or requiring cyclical treatment, can be accomplished by resorting to two organizational models: center-based care and home-based care. In developed countries, government pressure to reduce healthcare spending and the COVID-19 pandemic have led to the spread of home-based care. This type of care is proven to outperform the center-based one in terms of cost-effectiveness, patient satisfaction, and adherence to treatment guidelines. However, even for treatments usually suitable to be delivered at home, the patient's health status or other constraints may make it inappropriate to deliver service at the patient's home. This calls for a new organizational model, referred to as flexible care, where home-based and center-based care are not seen as mutually exclusive models but as options that can be activated according to the patient's and provider's needs. This paper presents a novel network-based deterministic optimization model and two matheuristics to address a scheduling problem typically faced by providers adopting a flexible care model. The model considers a provider relying on a treatment room with a fixed number of medical chairs, a fleet of vehicles, and a team of operators. It allows for determining on which days of the planning horizon, in which setting (home or center), and by which operator each patient will be treated. The model takes into account patients' preferences and considers two objective functions: minimizing provider costs and patients' travel time. In addition, we propose a two-stage mixed-integer stochastic programming model with recourse actions. This model allows incorporating the uncertainty due to the occurrence of adverse events. Adverse events are sudden changes in the patient's condition randomly happening at a specific point in the planning horizon. These events render the patient unsuitable for home care and require them to be visited at the center f

关键词： Home care Health care Network optimization Matheuristics Flexible care stochastic programming

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Coordinating oncologist appointments with chemotherapy treatments under uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 204卷

作者： Ozyuksel, Ege Gul, Serhat Unluyurt, Tonguc Celik, Batuhan Sabanci Univ Fac Engn & Nat Sci Istanbul Turkiye TED Univ Dept Ind Engn Ankara Turkiye Rensselaer Polytech Inst Dept Ind & Syst Engn Troy NY 12180 USA

Scheduling chemotherapy treatments in outpatient chemotherapy clinics (OCCs) presents significant challenges due to limited resources, uncertainty in infusion durations, and the need for coordinating with oncologist consultations. This study addresses these challenges through integrating oncologist consultation and chemotherapy scheduling by determining appointment times for a daily list of patients. A two-stage stochastic mixed-integer programming model is developed, considering stochastic factors such as infusion durations and the statuses of chemotherapy treatment approvals after consultations. In the first stage of the model, patients of each oncologist are organized in a sequence, and appointment times are set. In the second stage, patients are assigned to chairs and nurses using an optimal myopic policy. The objective function penalizes the expected weighted sum of the total working time of the OCC and the waiting times of patients. To represent the original scenario set by a reduced scenario set, a scenario reduction algorithm is employed. The algorithm, a Wasserstein Distance-Based Local Search Algorithm (WD-LSA), is tested using real data from a major academic oncology hospital in Turkey. The performance of the WD-LSA algorithm is demonstrated by comparing with CPLEX for smaller number of scenarios and with heuristic algorithms for larger number of scenarios. We find that the gap is quite small when compared with CPLEX solutions and the solutions are much better than the solutions found by the practical scheduling heuristics from the literature. The trade-off between patient waiting time and total working time is assessed. The dependency of the performance measures to the number of oncologists and nurses is investigated. Lastly, the value of stochastic solution is estimated.

关键词： Chemotherapy scheduling Care coordination stochastic programming Scenario reduction

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Space-to-Ground Optical Communication Downlink Scheduling Under Uncertainty of Link Availability

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IEEE COMMUNICATIONS LETTERS 2025年第5期29卷 1136-1140页

作者： Lyu, Pei Zhao, Kanglian Wang, Shihao Zhao, Hangsheng Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Peoples R China Nanjing Univ Posts & Telecommun Sch Telecommun & Informat Engn Nanjing 210003 Peoples R China

Space-to-ground optical communication (STGOC) utilizes laser beams to establish bidirectional links between satellites and ground stations (GSs), which are sensitive to cloud blockage. In practical STGOC downlink scheduling, the uncertainty of link availability caused by cloud motion and dissipation is inevitable. We are the first to address STGOC downlink scheduling under the uncertainty of link availability, where the objective function aims to maximize the amount of data downloaded from the satellites. We provide a formulation of the scheduling problem based on Scenario Generation Approximating (SGA) while preserving the formulation linearity. A Greedy Heuristic-based algorithm is designed to solve the problem. Simulation results indicate that considering uncertainty can enhance data throughput, with the average optimality gap being 0.61%, while the running time is reduced compared to Gurobi and Kuhn-Munkres-based methods.

关键词： Satellites Uncertainty Optical fiber communication Downlink Throughput Job shop scheduling Satellite broadcasting Heuristic algorithms Earth Observing System Laser beams Space-to-ground optical communication downlink scheduling stochastic programming linear integer programming

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Policies and Power Systems Resilience Under Time-Based stochastic Process of Contingencies in Networked Microgrids

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2024年 71卷 9460-9480页

作者： Pan, Weijie Shittu, Ekundayo George Washington Univ Dept Engn Management & Syst Engn Washington DC 20052 USA

Given the increasing occurrence of high-impact low-probability (HILP) contingencies in existing power systems, strengthening the resilience of these systems has become of paramount importance. Enhancing the resilience of power systems is not solely a technical issue but also a socio-economic and policy concern. Therefore, improving the performance of power systems greatly relies on the guidance provided by energy policies. While the decarbonization response, supported by these policies to mitigate climate change, influences the adoption of energy technologies, its impact on the resilience of the system remains uncertain. To uncover the interactions between technologies, policies, and economics concerning power systems resilience, this study focuses on constructing resilience-oriented networked microgrid systems. It develops a two-stage stochastic programming model by integrating a method for selecting power outage scenarios identified by users, in the presence of emissions policies. The results confirm the contributions of integrated systems in enhancing resilience, but they also reveal that low-carbon emissions policies play an inhibiting role by increasing the financial costs associated with resilience planning and operations. Nevertheless, a 30% emissions reduction threshold can still be achieved from the integrated network, facilitating the dual benefits of maximizing emissions reduction and minimizing the burden of emissions taxes. The study's contributions are threefold: firstly, it incorporates techno-economic incentives and regulations simultaneously;secondly, it quantifies the unintended consequences of policies on resilience;and thirdly, it provides constructive guidance for future energy policymaking, particularly in maintaining system resilience.

关键词： Carbon emissions taxation energy policies networked microgrids power systems resilience stochastic programming

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A Planner-Trader Decomposition for Multimarket Hydro Scheduling

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OPERATIONS RESEARCH 2024年第1期72卷 185页

作者： Schindler, Kilian Rujeerapaiboon, Napat Kuhn, Daniel Wiesemann, Wolfram Ecole Polytech Fed Lausanne Risk Analyt & Optimizat Chair CH-1015 Lausanne Switzerland Natl Univ Singapore Dept Ind Syst Engn & Management Singapore 117576 Singapore Imperial Coll London Imperial Coll Business Sch London SW7 2AZ England

Peak/off-peak spreads on European electricity forward and spot markets are eroding due to the ongoing nuclear phaseout in Germany and the steady growth in photovoltaic capacity. The reduced profitability of peak/off-peak arbitrage forces hydropower producers to recover part of their original profitability on the reserve markets. We propose a bilayer stochastic programming framework for the optimal operation of a fleet of interconnected hydropower plants that sells energy on both the spot and the reserve markets. The outer layer (the planner's problem) optimizes end-of-day reservoir filling levels over one year, whereas the inner layer (the trader's problem) selects optimal hourly market bids within each day. Using an information restriction whereby the planner prescribes the end of-day reservoir targets one day in advance, we prove that the trader's problem simplifies from an infinite-dimensional stochastic program with 25 stages to a finite two-stage stochastic program with only two scenarios. Substituting this reformulation back into the outer layer and approximating the reservoir targets by affine decision rules allows us to simplify the planner's problem from an infinite-dimensional stochastic program with 365 stages to a two-stage stochastic program that can conveniently be solved via the sample average approximation. Numerical experiments based on a cascade in the Salzburg region of Austria demonstrate the effectiveness of the suggested framework.

关键词： hydro scheduling reserve markets planner-trader decomposition stochastic programming

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Increasing schedule reliability in the multiple depot vehicle scheduling problem with stochastic travel time

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2024年 127卷

作者： Ricard, Lea Desaulniers, Guy Lodi, Andrea Rousseau, Louis -Martin Univ Montreal Dept Comp Sci Montreal PQ Canada Polytech Montreal Dept Math & Ind Engn Montreal PQ Canada Cornell Tech & Technion IIT Jacobs Technion Cornell Inst New York NY USA

The multiple depot vehicle scheduling problem (MDVSP) is one of the most studied problems in public transport service planning. It consists of assigning buses to each timetabled trip while respecting vehicle availability at each depot. Although service quality, and especially reliability, is the core of most transport agencies, the MDVSP is more often than not solved solely in a cost-efficient way. This work introduces a data -driven model to the reliable MDVSP with stochastic travel time (R-MDVSP-STT). The reliability of a schedule is assessed and accounted for by propagating delays using the probability mass function of the travel time of each timetabled trip. We propose a heuristic branch -and -price algorithm to solve this problem and a labeling algorithm with a stochastic dominance criterion for the associated subproblems. The solutions obtained are compared based on three metrics - under normal and extraordinary circumstances. Computational results on real -life instances show that our method can efficiently find good trade-offs between operational costs and reliability, improving the reliability of the solutions with little cost increase.

关键词： Vehicle scheduling Reliability Column generation stochastic programming Delay propagation

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Coordinated optimization of logistics scheduling and electricity dispatch for electric logistics vehicles considering uncertain electricity prices and renewable generation

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APPLIED ENERGY 2024年 364卷

作者： Chen, Yuanyi Hu, Simon Zheng, Yanchong Xie, Shiwei Yang, Qiang Wang, Yubin Hu, Qinru Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ ZJU UIUC Inst Haining 314400 Peoples R China Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310027 Peoples R China Alibaba Zhejiang Univ Joint Res Inst Frontier Tech Hangzhou 310058 Peoples R China Fuzhou Univ Sch Elect Engn & Automat Fuzhou 350108 Peoples R China

Electric logistics vehicles (ELVs) have the potential to significantly reduce pollution and carbon emissions, which are considered highly promising for achieving green logistics. However, the challenges posed by time-consuming charging processes, indirect carbon emissions, and fluctuating electricity prices hinder the economic viability of ELVs. To enhance the competitiveness of ELVs compared to internal combustion engine vehicles, this study introduces a coordinated optimization framework that aims to minimize both logistics and electricity costs within an urban logistics system. In terms of logistics, a simultaneous loading and partial charging strategy is proposed to improve the logistics efficiency of ELVs by effectively utilizing the charging time. In terms of electricity, we integrate suburban renewable energy sources with ELVs to reduce indirect carbon emissions resulting from thermal power generation. Additionally, ELVs are aggregated to participate in bidding in the dayahead and real-time electricity markets through charging scheduling. To address uncertainties related to electricity prices and renewable generation, optimal logistics scheduling and electricity dispatch strategies for ELVs are formulated as a stochastic programming problem. This problem is then solved using a specially designed matheuristic approach. The effectiveness of the proposed coordinated optimization framework is confirmed through case studies on a large-scale urban logistics system.

关键词： Coordinated optimization Electric logistics vehicles Electricity market Renewable energy sources stochastic programming Urban logistics

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