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Managing appointment-based services with electronic visits

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第3期315卷 863-878页

作者： Cai, Yun Song, Haiqing Wang, Shan Sun Yat Sen Univ Lingnan Coll Guangzhou 510275 Peoples R China Sun Yat Sen Univ Sch Business Guangzhou 510275 Peoples R China

Electronic visits, or "E-visits"for short, have emerged as a promising channel for accessing healthcare and can significantly impact daily operations in healthcare facilities. However, there is a lack of research on how to efficiently manage appointments for outpatient care providers when faced with E-visits that exhibit different waiting cost patterns. Our study investigates how providers can use appointment scheduling as a "passive"control when patients have full access to the E-visit channel, to better utilise resources and reduce patient waiting. Specifically, we demonstrate that multimodularity still applies to the model with E-visits, despite their waiting costs being typically nonlinear. Furthermore, we analyse how providers can "actively"control the arrival of E-visits by scheduling their time windows. By examining the structures of the optimal joint schedule of appointments and E-visit time windows, and reformulating the problem into a two-stage program, we have designed an Accelerated Cut Generation Algorithm, which is shown to be efficient in our numerical study. To the best of our knowledge, this is the first study to explore the optimal scheduling of both appointments and E-visit time windows. By implementing proper scheduling, the negative impact of E-visits can be mitigated, their benefits to the provider can be enhanced, and overall operational efficiency can be improved.

关键词： OR in health services E-visits Appointment scheduling stochastic programming

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Convex approximations of two-stage risk-averse mixed-integer recourse models

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2024年第1期88卷 313-347页

作者： van Beesten, E. Ruben Romeijnders, Ward Roodbergen, Kees Jan Univ Groningen Fac Econ & Business Nettelbosje 2 NL-9747 AE Groningen Netherlands

We consider two-stage risk-averse mixed-integer recourse models with law invariant coherent risk measures. As in the risk-neutral case, these models are generally non-convex as a result of the integer restrictions on the second-stage decision variables and hence, hard to solve. To overcome this issue, we propose a convex approximation approach. We derive a performance guarantee for this approximation in the form of an asymptotic error bound, which depends on the choice of risk measure. This error bound, which extends an existing error bound for the conditional value at risk, shows that our approximation method works particularly well if the distribution of the random parameters in the model is highly dispersed. For special cases we derive tighter, non-asymptotic error bounds. Whereas our error bounds are valid only for a continuously distributed second-stage right-hand side vector, practical optimization methods often require discrete distributions. In this context, we show that our error bounds provide statistical error bounds for the corresponding (discretized) sample average approximation (SAA) model. In addition, we construct a Benders' decomposition algorithm that uses our convex approximations in an SAA-framework and we provide a performance guarantee for the resulting algorithm solution. Finally, we perform numerical experiments which show that for certain risk measures our approach works even better than our theoretical performance guarantees suggest.

关键词： stochastic programming Mixed-integer recourse Risk aversion Convex approximations

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Leveraging decision diagrams to solve two-stage stochastic programs with binary recourse and logical linking constraints

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第1期315卷 228-241页

作者： MacNeil, Moira Bodur, Merve Univ Toronto Dept Mech & Ind Engn Toronto ON Canada Univ Edinburgh Sch Math Edinburgh Scotland

We generalize an existing binary decision diagram -based (BDD-based) approach of Lozano and Smith (MP, 2022) to solve a special class of two -stage stochastic programs (2SPs) with binary recourse, where the first -stage decisions impact the second -stage constraints. First, we extend the second -stage problem to a more general setting where logical expressions of the first -stage solutions enforce constraints in the second stage. Then, as our primary contribution, we introduce a complementary problem, that appears more naturally for many of the same applications of the former approach, and a distinct BDD-based solution method, that is more efficient than the existing BDD-based approach on commonly applicable problem classes. In the novel problem, secondstage costs, rather than constraints, are impacted by expressions of the first -stage decisions. In both settings, we convexify the second -stage problems using BDDs and parameterize either the BDD arc costs or capacities with first -stage solutions. We extend this work by incorporating conditional value -at -risk and propose the first decomposition method for 2SP with binary recourse and a risk measure. We apply these methods to a novel stochastic problem, namely stochastic minimum dominating set problem, and present numerical results to support their effectiveness.

关键词： stochastic programming Binary decision diagrams Benders decomposition

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A cooperative transactive multi-carrier energy control mechanism with P2P energy plus reserve trading using Nash bargaining game theory under renewables uncertainty

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APPLIED ENERGY 2024年 353卷

作者： Alizadeh, Ali Esfahani, Moein Dinar, Farid Kamwa, Innocent Moeini, Ali Mohseni-Bonab, Seyed Masoud Busvelle, Eric Univ Laval Coll Engn Elect Engn Dept Quebec City PQ Canada Toulon Univ Lab Informat & Syst Toulon France Hydroquebec IREQ Power Syst Simulat & Evolut Varennes PQ Canada Hydro Quebec IREQ Digital Syst Varennes PQ Canada

Transactive Energy Control (TEC) as a market-based control is a critical notion for scheduling Multi-Carrier Energy Systems (MCESs) in local networks and forming an Energy Hub (EH). Nevertheless, implementing TEC for scheduling and controlling MCESs is extremely difficult due to the lack of a cooperative TEC model that accounts for network constraints and the uncertainty of Renewable Energy Sources (RESs). This paper defines and formulates Prosumer-Based Multi-Carrier Energy Systems (PB-MCESs), which include electricity, heat, cooling, and gas hubs to enable internal coordination of resources and flexibility extraction for PB-MCESs. Subsequently, Nash Bargaining Game Theory is employed to construct a cooperative TEC that prioritizes P2P energy trade. In addition to P2P energy trading, PB-MCESs can trade their reserve in a P2P fashion to mitigate their uncertainty. PB-MCESs estimate the level of uncertainty using stochastic programming and allot a reserve capacity based on this estimation in order to manage their uncertainty via P2P reserve trading and internal reserves. PB-MCES can also control their risk by altering their risk-taking factor in accordance with the Conditional Value-at-Risk (CVAR) index. Implementations have demonstrated that the proposed cooperative TEC decreases total costs by 17.14% and that the proposed P2P reserve trading reduces total costs by 16.32%.

关键词： Transactive energy control Prosumer-based multi -carrier energy systems Energy hub Nash bargaining game theory stochastic programming Risk management P2P trading Conditional value-at-risk

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Problem-based scenario generation by decomposing output distributions

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第1期318卷 154-166页

作者： Narum, Benjamin S. Fairbrother, Jamie Wallace, Stein W. NHH Norwegian Sch Econ Dept Business & Management Sci Helleveien 30 N-5045 Bergen Hordaland Norway Univ Lancaster Dept Management Sci Lancaster LA1 4YX Lancs England

Scenario generation is required for most applications of stochastic programming to evaluate the expected effect of decisions made under uncertainty. We propose a novel and effective problem -based scenario generation method for two -stage stochastic programming that is agnostic to the specific stochastic program and kind of distribution. Our contribution lies in studying how an output distribution may change across decisions and exploit this for scenario generation. From a collection of output distributions, we find a few components that largely compose these, and such components are used directly for scenario generation. Computationally, the procedure relies on evaluating the recourse function over a large discrete distribution across a set of candidate decisions, while the scenario set itself is found using standard and efficient linear algebra algorithms that scale well. The method's effectiveness is demonstrated on four case study problems from typical applications of stochastic programming to show it is more effective than its distribution -based alternatives. Due to its generality, the method is especially well suited to address scenario generation for distributions that are particularly challenging.

关键词： stochastic programming Problem-based scenario generation Singular value decomposition

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stochastic dual dynamic programming with stagewise-dependent objective uncertainty

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OPERATIONS RESEARCH LETTERS 2020年第1期48卷 33-39页

作者： Downward, Anthony Dowson, Oscar Baucke, Regan Univ Auckland Dept Engn Sci Auckland New Zealand Northwestern Univ Dept Ind Engn & Management Sci Evanston IL USA Ecole Ponts ParisTech CERMICS Champs Sur Marne France

We present a new algorithm for solving linear multistage stochastic programming problems with objective function coefficients modeled as a stochastic process. This algorithm overcomes the difficulties of existing methods which require discretization. Using an argument based on the finiteness of the set of possible cuts, we prove that the algorithm converges almost surely. Finally, we demonstrate the practical application of the algorithm on a hydro-bidding example with the spot-price modeled as an auto-regressive process. (C) 2019 Elsevier B.V. All rights reserved.

关键词： stochastic programming Dynamic programming Decomposition Multistage SDDP

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Joint charging scheduling of electric buses and active power flexibility integration

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 197卷

作者： Najafi, Arsalan Gao, Kun Wang, Hua Jasinski, Michal Parishwad, Omkar Cui, Shaohua Liu, Xiaohan Chalmers Univ Technol Dept Architecture & Civil Engn SE-41296 Gothenburg Sweden Wroclaw Univ Sci & Technol Dept Elect Engn Fundamentals PL-50370 Wroclaw Poland Hefei Univ Technol Sch Automot & Transportat Engn Hefei 230009 Peoples R China

Public transport electrification stands out as a notable response to the environmental concerns in the transport sector. This study proposes a joint optimization framework for the coupled battery electric bus (BEB) transit system and active power distribution network (APDN) integrated with the flexibility of demand response (DR). The primary objective is to effectively support BEB mobility services by addressing their spatial and temporal charging demands. Special emphasis is placed on leveraging APDN capabilities to facilitate BEB operations with minimal costs. The problem is formulated as a bi-level stochastic programming by incorporating the nonprofit agent at the upper level and the DR aggregators at the lower level. The upper level aims to minimize the joint costs of the APDN and BEB transit system, while the lower level seeks to maximize its profit through interaction with the upper level. The problem is then reformulated into an equivalent single-level model using Karush-Kuhn-Tucker conditions. The findings underscore the effective coupling framework in tackling the charging scheduling in the BEB sub-transit system in Sk & ouml;vde, Sweden, alongside the proper DR activation to meet the technical constraints of the coupled BEB transit and APDN. The proposed optimization framework can compensate for the additional burden of charging demands from BEBs by curtailing 6.4% of energy during peak hours.

关键词： Battery electric bus Active distribution network Coupled transportation and power networks Demand response stochastic programming

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Enhancing Automatic Generation Control Performance Under Uncertainty and Congestion Through Detailed Transmission Modeling and Strategic Selection of Units

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IEEE TRANSACTIONS ON POWER SYSTEMS 2025年第3期40卷 2389-2400页

作者： Hinojosa, Victor H. Bolbaran, Daniel Gil, Esteban Gutierrez-Alcaraz, Guillermo Univ Tecn Feder Santa Maria Dept Elect Engn Valparaiso 23901223 Chile Univ Tecn Federico Santa Maria Dept Elect Engn Valparaiso Chile Univ Tecn Federico Santa Maria Adv Ctr Elect & Elect Engn Valparaiso Chile Tecnol Nacl Mexico Inst Tecnol Morelia ITM Programa Grad Invest Ingn Electr PGIIE Morelia 58120 Mich Mexico

Considering the automatic generation control (AGC) system as an intermediary between the economic dispatch problem and synchronous generator dynamics, this study introduces a novel formulation for the AGC system. The proposed scheduling framework is based on a stochastic $N-1$ lossy network-constrained economic dispatch problem formulated as a mixed-integer linear programming instance. Transmission losses are represented through piecewise linear expressions. After the primary frequency control finishes, the proposed scheduling methodology selects the generation units that will be activated and determines their regulation participation factors to minimize the activation and operational costs in a two-stage stochastic problem, incorporating the load-voltage dependency phenomenon, and modeling transmission power flow and power losses using linear lossy shift-factors. The proposed formulation also considers the distinctive possibility of AGC units to both increase and decrease power for addressing under-frequency events economically, offering an effective generation capability that co-optimizes energy and reserves, providing a more flexible and efficient control strategy compared to traditional AGC systems. The effectiveness of the proposed methodology is demonstrated in a 50-bus electrical system, evaluating its performance with diverse operational conditions and contingency generation events using DIgSILENT PowerFactory. Extensive computational RMS experiments are conducted to assess the frequency stability in the electrical power system.

关键词： Automatic generation control Biological system modeling Propagation losses Power system dynamics Frequency control Load flow Power system stability Load modeling Uncertainty stochastic processes DC lossy formulation DIgSILENT PowerFactory scheduling methodology shift-factors stochastic programming transmission constraints

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Designing a resilient humanitarian supply chain by considering viability under uncertainty: A machine learning embedded approach

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 194卷

作者： Yilmaz, Omer Faruk Guan, Yongpei Yilmaz, Beren Gursoy Karadeniz Tech Univ Dept Ind Engn TR-61080 Trabzon Turkiye Univ Florida Ctr Appl Optimizat Dept Ind & Syst Engn Gainesville FL USA Univ Florida Dept Ind & Syst Engn Gainesville FL USA Univ Florida Dept Ind & Syst Engn Supply Chain & Logist Engn Lab Gainesville FL USA

Humanitarian supply chains (HSCs) play a crucial role in mitigating the impacts of natural disasters and preventing humanitarian crises. Designing resilient HSCs is critically important to ensure effective recovery and long-term sustainability during and after such events. This study addresses the design of resilient HSCs with viability consideration under known-unknown demand and capacity uncertainties by formulating a two-stage stochastic programming model. To solve this problem and achieve high-quality solutions, three solution approaches are developed and compared. The first approach introduces risk aversion into a genetic algorithm (GA) through chance constraints, termed GA with chance constraints (GAC). The other two approaches integrate the Random Forest (RF) algorithm with GAC, employing incremental learning (GACRFI) and non-incremental learning (GACRFNI). To evaluate the performance of these algorithms and provide insights into designing a resilient HSC, a full factorial design of experiments (DoE) is established using controllable factors. Problems are generated for three cases, each of which corresponds to a distinct disruption and ripple effect severity degree. Computational analysis shows that integrating the machine learning algorithm into the GA yields superior results across all risk level settings, leading to a win-win situation for all stakeholders in HSCs. This study provides valuable insights for designing resilient HSCs that ensure both short-term recovery and long-term sustainability by considering viability under varying risk levels and severity degrees.

关键词： Humanitarian supply chain Resilience Viability stochastic programming Machine learning Genetic algorithm

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stochastic approach for price optimization problems with decision-dependent uncertainty

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第2期322卷 541-553页

作者： Hikima, Yuya Takeda, Akiko Univ Tokyo Grad Sch Informat Sci & Technol 7-3-1 HongoBunkyo Ku Tokyo 1138656 Japan RIKEN Ctr Adv Intelligence Project 1-4-1 NihonbashiChuo Ku Tokyo 1030027 Japan

Price determination is a central research topic of revenue management in marketing. The important aspect in pricing is controlling the stochastic behavior of demand, and the previous studies have tackled price optimization problems with uncertainties. However, many of those studies assumed that uncertainties are independent of decision variables (i.e., prices) and did not consider situations where demand uncertainty depends on price. Although some price optimization studies have dealt with decision-dependent uncertainty, they make application-specific assumptions in order to obtain optimal solutions. To handle a wider range of applications with decision-dependent uncertainty, we propose a general non-convex stochastic optimization formulation. This approach aims to maximize the expectation of a revenue function with respect to a random variable representing demand under a decision-dependent distribution. We derived an unbiased stochastic gradient estimator by using a well-tuned variance reduction parameter and used it fora projected stochastic gradient descent method to find a stationary point of our problem. We conducted synthetic experiments and simulation experiments with real data on a retail service application. The results show that the proposed method outputs solutions with higher total revenues than baselines.

关键词： Pricing stochastic programming OR in marketing Decision-dependent uncertainty

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