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How a dedicated postdischarge unit can reduce hospital congestion and costs

DECISION SCIENCES 2025年第1期56卷 93-115页

作者： Khatami, Maryam Stauffer, Jon M. Lawley, Mark A. Univ North Texas G Brint Ryan Coll Business Dept Informat Technol & Decis Sci Denton TX 76203 USA Texas A&M Univ Mays Business Sch Dept Informat & Operat Management College Stn TX USA Texas A&M Univ Dept Ind & Syst Engn College Stn TX USA

Depending on the patient's condition, up to 60% of inpatients are discharged to post-acute care facilities (PACFs). These patients may experience several days of nonmedical inpatient stay until the hospital finds a facility that fits their needs, contributing to overcrowding in upstream units. This article studies the feasibility of creating a "postdischarge unit" (PDU) for medically ready-for-discharge patients who experience transfer delays, to improve access to inpatient beds. We use a multistage stochastic program, solved with a dual dynamic programming algorithm, to address the PDU size and capacity question. The random variable is the number of bed requests from upstream units (e.g., emergency department). Our numerical analysis, using data from a large hospital, shows that a PDU can reduce costs and significantly reduce the number of patients waiting for transfer to PACFs that are occupying inpatient beds, as long as the percentage of these patients in the hospital is more than 4%. Compared to current practice in our partner hospital, a PDU could increase access to inpatient beds by up to 13% and result in 2%-21% cost savings. Results show that PDU capacity in hospitals with a larger number of patients waiting for transfer is more sensitive to variation in PDU renovation and operational costs. In addition to using fewer medical staff, a PDU can improve discharge transitions to lower levels of care and more efficiently utilize social workers and physical therapists assisting these patients.

关键词： discharge planning healthcare operations multistage stochastic programming OR in health services patient flow

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Dynamic reverse supply chain network design under uncertainty: mathematical modeling and solution algorithm

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2022年第5期29卷 3161-3189页

作者： Jeihoonian, Mohammad Zanjani, Masoumeh Kazemi Gendreau, Michel Concordia Univ Dept Mech Ind & Aerosp Engn Montreal PQ H3G 1M8 Canada Polytech Montreal Dept Math & Ind Engn Montreal PQ H3C 3A7 Canada Interuniv Res Ctr Enterprise Networks Logist & Tr Montreal PQ H3C 3A7 Canada

Motivated by the recovery of modular-structured products, this study addresses the flexible design of a reverse supply chain (RSC) over a planning horizon while incorporating the dynamic uncertain behavior of product returns. The stochastic parameter is modeled as a scenario tree and therefore the concerned problem is formulated as a multistage mixed-integer stochastic program. To alleviate the computational complexity of the proposed model, it is decomposed into smaller scenario cluster submodels associated with a number of subtrees that share a certain number of predecessor nodes in the original scenario tree. The submodels are coordinated into an implementable solution via a Lagrangian-progressive hedging-based method that employs a viable Benders decomposition based algorithm for solving each scenario cluster submodel. Based on a realistic scale case, computational results indicate the superiority of the proposed flexible dynamic RSC design model compared to the existing models. Results also demonstrate the efficiency of the proposed solution approach.

关键词： flexible reverse supply chain design multistage stochastic programming scenario clustering decomposition Benders decomposition

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Process-based risk measures and risk-averse control of discrete-time systems

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MATHEMATICAL programming 2022年第1期191卷 113-140页

作者： Fan, Jingnan Ruszczynski, Andrzej Rutgers State Univ RUTCOR Piscataway NJ 08854 USA Rutgers State Univ Dept Management Sci & Informat Syst Piscataway NJ 08854 USA

For controlled discrete-time stochastic processes we introduce a new class of dynamic risk measures, which we call process-based. Their main feature is that they measure risk of processes that are functions of the history of a base process. We introduce a new concept of conditional stochastic time consistency and we derive the structure of process-based risk measures enjoying this property. We show that they can be equivalently represented by a collection of static law-invariant risk measures on the space of functions of the state of the base process. We apply this result to controlled Markov processes and we derive dynamic programming equations. We also derive dynamic programming equations for multistage stochastic programming with decision-dependent distributions.

关键词： Dynamic risk measures Time consistency Dynamic programming multistage stochastic programming

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Day-Ahead Optimal Dispatch Strategy for Active Distribution Network Based on Improved Deep Reinforcement Learning

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IEEE ACCESS 2022年 10卷 9357-9370页

作者： Li, Xiaoyu Han, Xueshan Yang, Ming Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China

To effectively deal with the challenge of optimal dispatch caused by uncertainties such as renewable energy in active distribution network, a day-ahead optimal dispatch strategy for active distribution network based on improved deep reinforcement learning is proposed. First, the day-ahead optimal dispatch problem for the active distribution network is modeled as a multistage stochastic programming model. multistage models attempt to capture the dynamics of unfolding uncertainties over time, so that the interaction between decision making and uncertainty unfolding is represented more accurately and realistically, and the decision can be adjusted dynamically and adaptively. Second, with the help of deep neural network to approximate the power flow model of the active distribution network in a data-driven manner, an improved deep reinforcement learning algorithm is proposed to solve the corresponding multistage stochastic programming problem. The proposed improved deep reinforcement learning algorithm effectively solves the problem that trial-and-error information acquisition methods are difficult to apply to high-cost power systems. Finally, the effectiveness of the proposed day-ahead optimization dispatch strategy for active distribution network based on improved deep reinforcement learning is verified by a modified IEEE33 case.

关键词： Uncertainty Optimization Renewable energy sources Reinforcement learning Heuristic algorithms Adaptation models Active distribution networks Active distribution network multistage stochastic programming optimal dispatch data driven deep learning deep reinforcement learning

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Assessing the Cost of Network Simplifications in Long-Term Hydrothermal Dispatch Planning Models

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2022年第1期13卷 196-206页

作者： Rosemberg, Andrew W. Street, Alexandre Garcia, Joaquim Dias Valladao, Davi M. Silva, Thuener Dowson, Oscar Pontifical Catholic Univ Rio de Janeiro PUC Rio Lab Appl Math Programming & Stat LAMPS PUC Rio BR-22251030 Rio De Janeiro Brazil Pontifical Catholic Univ Rio de Janeiro PUC Rio Dept Elect Engn BR-22251030 Rio De Janeiro Brazil PSR BR-22250145 Rio De Janeiro Brazil Pontificia Univ Catolica Rio de Janeiro LAMPS PUC Rio BR-22251030 Rio de Janeiro Brazil Pontificia Univ Catolica Rio de Janeiro Dept Ind Engn BR-22251030 Rio de Janeiro Brazil Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA

The sustainable utilization of hydro energy relies on accurate estimates of the opportunity cost of the water. This value is calculated through long-term hydrothermal dispatch problems (LTHDP), and the recent literature has raised awareness about the consequences of modeling simplifications in these problems. The inaccurate representation of Kirchhoff's voltage law under the premise of a DC power flow is an example. Under a non-linear AC model, however, the LTHDP becomes intractable, and the literature lacks an accurate evaluation method of different modeling alternatives. In this paper, we extend the state-of-the-art cost-assessment framework of network approximations for LTHDP and bring relevant and practical new insights. First, we increase the quality of the assessment by using an AC power flow to simulate and compare the performance of five policies based on different network approximations. Second, we find that the tightest network relaxation (based on semidefinite programming) is not the one exhibiting the best performance. Results show that the DC power flow with quadratic losses approximation exhibits the lowest expected cost and inconsistency gaps. Finally, its computational burden is lower than that exhibited by the semidefinite relaxation, whereas market distortions are significantly reduced in comparison to previously published benchmarks based on DC power flow.

关键词： Long-term hydrothermal dispatch multistage stochastic programming network model relaxations optimal power flow time consistency

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Combining Polyhedral Approaches and stochastic Dual Dynamic Integer programming for Solving the Uncapacitated Lot-Sizing Problem Under Uncertainty

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INFORMS JOURNAL ON COMPUTING 2022年第2期34卷 1024-1041页

作者： Quezada, Franco Gicquel, Celine Kedad-Sidhoum, Safia Sorbonne Univ Lab Informat Paris 6 LIP6 Ctr Natl Rech Sci CNRS F-75005 Paris France Univ Santiago Chile USACH Program Dev Sustainable Prod Syst PDSPS Fac Engn Santiago Chile Univ Paris Saclay Lab Interdisciplinaire Sci Numer LISN F-91190 Gif Sur Yvette France Ctr Etud & Rech Informat & Commun CEDRIC Conservatoire Natl Arts & Metiers CNAM F-75003 Paris France

We study the uncapacitated lot-sizing problem with uncertain demand and costs. The problem is modeled as a multistage stochastic mixed-integer linear program in which the evolution of the uncertain parameters is represented by a scenario tree. To solve this problem, we propose a new extension of the stochastic dual dynamic integer programming algorithm (SDDiP). This extension aims at being more computationally efficient in the management of the expected cost-to-go functions involved in the model, in particular by reducing their number and by exploiting the current knowledge on the polyhedral structure of the stochastic uncapacitated lot-sizing problem. The algorithm is based on a partial decomposition of the problem into a set of stochastic subproblems, each one involving a subset of nodes forming a subtree of the initial scenario tree. We then introduce a cutting plane-generation procedure that iteratively strengthens the linear relaxation of these subproblems and enables the generation of an additional strengthened Benders' cut, which improves the convergence of the method. We carry out extensive computational experiments on randomly generated large-size instances. Our numerical results show that the proposed algorithm significantly outperforms the SDDiP algorithm at providing good-quality solutions within the computation time limit. Summary of Contribution: This paper investigates a combinatorial optimization problem called the uncapacitated lot-sizing problem. This problem has been widely studied in the operations research literature as it appears as a core subproblem in many industrial production planning problems. We consider a stochastic extension in which the input parameters are subject to uncertainty and model the resulting stochastic optimization problem as a multistage stochastic integer program. To solve this stochastic problem, we propose a novel extension of the recently published stochastic dual dynamic integer programming (SDDiP) algorithm. The prop

关键词： multistage stochastic programming stochastic lot sizing stochastic dual dynamic integer programming node aggregation valid inequalities partial decomposition

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Mitigation strategies against supply disruption risk: a case study at the Ford Motor Company

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第19期60卷 5956-5976页

作者： Sanci, Ece Daskin, Mark S. Hong, Young-Chae Roesch, Steve Zhang, Don Univ Michigan Ind & Operat Engn Ann Arbor MI 48109 USA Univ Bath Sch Management Bath Avon England Ford Motor Co Dearborn MI 48121 USA

Supply chains are exposed to different risks, which can be mitigated by various strategies based on the characteristics and needs of companies. In collaboration with Ford, we develop a decision support framework to choose the best mitigation strategy against supply disruption risk, especially for companies operating with a small supplier base and low inventory levels. Our framework is based on a multistage stochastic programming model which incorporates a variety of plausible strategies, including reserving backup capacity from the primary supplier, reserving capacity from a secondary supplier, and holding backup inventory. We reflect disruption risk into the framework through decision makers' input on the time to recover and the disruption probability. Our results demonstrate that relying on the strategy which is optimal when there is no disruption risk can increase the expected total cost substantially in the presence of disruption risk. However, this increase can be reduced significantly by investing in the mitigation strategy recommended by our framework. Our results also show that this framework removes the burden of estimating the time to recover and the disruption probability precisely since there is often a small loss associated with using another strategy that is optimal in the neighbourhood of the estimated values.

关键词： Supply chain risk management disruption risk mitigation sourcing mitigation contingency planning multistage stochastic programming

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Long-Term Generation Scheduling: A Tutorial on the Practical Aspects of the Problem Solution

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2022年第3期33卷 806-821页

作者： Pedrini, R. Finardi, E. C. Univ Fed Santa Catarina LabPlan Florianopolis SC Brazil INESC P&D Santos SP Brazil

Managing power production for hydro-dominated electrical energy systems in the long-term horizon is a challenging task that requires a trade-off between costs and electricity shortage risks. Because of the inflow uncertainty, this trade-off can be assessed by an operating policy that determines how much water should be used to generate power at the beginning of each time-step and how much should be stored for the future. The long-term generation scheduling (LTGS) is often solved by system operators using multistage stochastic optimization and stochastic dual dynamic programming (SDDP) approaches. Although the literature related to the LTGS problem is vast, several practical aspects used in SDDP are not demonstrated clearly, which can prevent the interest of young researchers and hinder the advances and new applications usually employed by senior professionals in industry and universities. Thus, this paper provides a tutorial on (but not limited to) (i) the application of SDDP for solving the LTGS problem, including water inflow inter-stage dependence, (ii) assessment of the impacts of hydropower function in the policy and (iii) inclusion of a risk measure to obtain a more reliable operation in unfavorable (dry) inflow scenarios. We present the main ideas behind each aspect's inclusion in the SDDP algorithm, using simple (but comprehensive) numerical examples. The overview is intended to understand these main characteristics, presenting them as powerful tools to investigate the LTGS problem in detail, searching for a good or even (near) optimal operating policy.

关键词： Long-term generation scheduling problem multistage stochastic programming stochastic dual dynamic programming Hydropower function Risk-averse model

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Foreign exchange trading and management with the stochastic dual dynamic programming method

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Financial Innovation 2023年第1期9卷 583-620页

作者： Lorenzo Reus Guillermo Alexander Sepulveda‑Hurtado Escuela de Negocios Universidad Adolfo IbanezDiagonal Las Torres2640 SantiagoChile

We present a novel tool for generating speculative and hedging foreign exchange(FX)trading *** solution provides a schedule that determines trades in each rebalancing period based on future currency prices,net foreign account positions,and incoming(outgoing)flows from business *** obtain such policies,we construct a multistage stochastic programming(MSP)model and solve it using the stochastic dual dynamic programming(SDDP)numerical method,which specializes in solving high-dimensional MSP *** construct our methodology within an open-source SDDP package,avoiding implementing the method from *** measure the performance of our policies,we model FX prices as a mean-reverting stochastic process with random events that incorporate stochastic *** calibrate this price model on seven currency pairs,demonstrating that our trading policies not only outperform the benchmarks for each currency,but may also be close to ex-post optimal *** also show how the tool can be used to generate more or less conservative strategies by adjusting the risk tolerance,and how it can be used in a vari-ety of contexts and time scales,ranging from intraday speculative trading to monthly hedging for business ***,we examine the impact of increasing trade policy uncertainty(TPU)levels on our *** findings show that the volatility of currencies from emerging economies rises in comparison to currencies from devel-oped *** discover that an increase in the TPU level has no effect on the aver-age profit obtained by our ***,the risk exposure of the policies increases(decreases)for the group of currencies from emerging(developed)markets.

关键词： FX trading FX risk SDDP multistage stochastic programming Julia Trade policy uncertainty

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Optimal long-term Tier 1 employee pension management with an application to Chinese urban areas

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QUANTITATIVE FINANCE 2022年第9期22卷 1759-1784页

作者： Ji, Bingbing Chen, Zhiping Consigli, Giorgio Yan, Zhe Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Xian Int Acad Math & Math Technol Ctr Optimizat Tech & Quantitat Finance Xian 710049 Peoples R China Khalifa Univ Sci & Technol Dept Math POB 127788 Abu Dhabi U Arab Emirates Univ Bergamo Dept Econ Via Caniana 2 I-24127 Bergamo Italy

We formulate a stochastic optimization problem from the perspective of an investment committee responsible for Tier 1 social security pension policies and whose decisions are bound to have relevant economic and social consequences. The adopted modelling approach combines canonical multistage stochastic programming (MSP) with dynamic stochastic control (DSC): the first applies to the short-medium term, the second to the long-term. Through the combined framework, we are able to span a long planning horizon without jeopardizing the accuracy of scenario tree based medium-term planning. We apply this methodology to the Chinese pension system, which relies on two large reference areas for rural and urban populations. In this article, we concentrate on the ever-growing urban public pension system, which is facing significant challenges due to a declining workforce and a rapidly ageing population. This welfare area, originally conceived as a pay-as-you-go (PAYG) system, has undergone several recent reforms to enhance its long-term sustainability and reduce the interventions of the central government required to improve its funding condition. Among those relevant in our setting, is the reduction of policy constraints that until 2015 severely limited the possibility to invest in assets other than traditional, locally traded, long-term fixed income securities. We propose an optimization model in which the decisions of the investment management aim at significantly reducing central government interventions as a last resort liquidity provider and progressively improving the system funding condition. A rich set of computational and economic evidence is presented to validate the methodology and clarify its potential benefits to pension system efficiency.

关键词： multistage stochastic programming Dynamic programming Long-term sustainability Sensitivity analysis Strategic asset allocation Chinese public pension fund

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