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Inpatient discharge planning under uncertainty

IISE TRANSACTIONS 2022年第4期54卷 332-347页

作者： Khatami, Maryam Alvarado, Michelle Kong, Nan Parikh, Pratik J. Lawley, Mark A. Indiana Univ Kelley Sch Business Dept Operat & Decis Technol Bloomington IN 47405 USA Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA Purdue Univ Weldon Sch Biomed Engn W Lafayette IN 47907 USA Univ Louisville Dept Ind Engn Louisville KY 40292 USA Texas A&M Univ Dept Ind & Syst Engn College Stn TX USA

Delay in inpatient discharge processes reduces patient satisfaction and increases hospital congestion and length of stay. Further, flow congestion manifests as patient boarding, where new patients awaiting admission are blocked by bed unavailability. Finally, length of stay is extended if the discharge delay incurs an extra overnight stay. These factors are often in conflict, thus, good hospital performance can only be achieved through careful balancing. We formulate the discharge planning problem as a two-stage stochastic program with uncertain discharge processing and bed request times. The model minimizes a combination of discharge lateness, patient boarding, and deviation from preferred discharge times. Patient boarding is integrated by aligning bed requests with bed releases. The model is solved for different instances generated using data from a large hospital in Texas. stochastic decomposition is compared with the extensive form and the L-shaped algorithm. A shortest expected processing time heuristic is also investigated. Computational experiments indicate that stochastic decomposition outperforms the L-shaped algorithm and the heuristic, with a significantly shorter computational time and small deviation from optimal. The L-shaped method solves only small problems within the allotted time budget. Simulation experiments demonstrate that our model improves discharge lateness and patient boarding compared to current practice.

关键词： Inpatient discharge planning healthcare stochastic programming scheduling

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Optimal Cruise Airspeed in the Presence of Wind Uncertainty

Optimal Cruise Airspeed in the Presence of Wind Uncertainty

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AIAA Science and Technology (SciTech) Forum

作者： Jung, Hyunho Clarke, John-Paul Univ Texas Austin Walker Dept Mech Engn Austin TX 78712 USA Univ Texas Austin Dept Aerosp Engn & Engn Mech Austin TX 78712 USA

ISBN: (数字)9781624106996

ISBN: (纸本)9781624106996

Aircraft ground speed during the cruise phase of flight is particularly sensitive to changes in wind speed and direction. These highly variable wind conditions are typically forecast on an hourly basis and in many cases only provided to aircraft prior to their departure. Thus the total flight time and the corresponding time of arrival are difficult to predict and control. We propose an approach to determining the airspeed such that there is a very high probability of achieving the required time of arrival (RTA) at a future point in the cruise phase while at the same time minimizing the fuel that is consumed in the interim. In the proposed approach, the cruise phase is divided into several segments and the historical correlations between the forecast winds in the segments are used within the context of a stochastic programming (SP) and receding horizon control (RHC) framework to determine the aforementioned optimum airspeed. Through a numerical study, we observe that fuel consumption decreases as the number of look-ahead segments is increased, albeit with non-linearly increasing computational time, and increases as the number of segments is increased from 8 to 14 segments.

关键词： stochastic programming

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Adaptive Resource Allocation in Quantum Key Distribution (QKD) for Federated Learning

Adaptive Resource Allocation in Quantum Key Distribution (QK...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Kaewpuang, Rakpong Xu, Minrui Niyato, Dusit Yu, Han Xiong, Zehui Shen, Xuemin (Sherman) Nanyang Technol Univ NTU Sch Comp Sci & Engn Singapore Singapore Singapore Univ Technol & Design SUTD Pillar Informat Syst Technol & Design Singapore Singapore Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada

ISBN: (纸本)9781665457194

Increasing privacy and security concerns in intelligence-native 6G networks require quantum key distribution-secured federated learning (QKD-FL), in which data owners connected via quantum channels can train an FL global model collaboratively without exposing their local datasets. To facilitate QKD-FL, the architectural design and routing management framework are essential. However, effective implementation is still lacking. To this end, we propose a hierarchical architecture for QKD-FL systems in which QKD resources (i.e., wavelengths) and routing are jointly optimized for FL applications. In particular, we focus on adaptive QKD resource allocation and routing for FL workers to minimize the deployment cost of QKD nodes under various uncertainties, including security requirements. The experimental results show that the proposed architecture and the resource allocation and routing model can reduce the deployment cost by 7.72% compared to the CO-QBN algorithm.

关键词： Federated learning quantum key distribution adaptive resource allocation stochastic programming

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Time-of-Use Pricing for Energy Storage Investment

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IEEE TRANSACTIONS ON SMART GRID 2022年第2期13卷 1165-1177页

作者： Zhao, Dongwei Wang, Hao Huang, Jianwei Lin, Xiaojun MIT MIT Energy Initiat 77 Massachusetts Ave Cambridge MA 02139 USA Monash Univ Fac Informat Technol Dept Data Sci & Artificial Intelligence Melbourne Vic 3800 Australia Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Peoples R China Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA

Time-of-use (ToU) pricing is widely used by the electricity utility to shave peak load. Such a pricing scheme provides users with incentives to invest in behind-the-meter energy storage and to shift peak load towards low-price intervals. However, without considering the implication on energy storage investment, an improperly designed ToU pricing scheme may lead to significant welfare loss, especially when users over-invest the storage, which leads to new energy consumption peaks. In this paper, we will study how to design a social-optimum ToU pricing scheme by explicitly considering its impact on storage investment. We model the interactions between the utility and users as a two-stage optimization problem. To resolve the challenge of asymmetric information due to users' private storage cost, we propose a ToU pricing scheme based on different storage types and the aggregate demand per type. Each user does not need to reveal his private cost information. We can further compute the optimal ToU pricing with only a linear complexity. Simulations based on real-world data show that the suboptimality gap of our proposed ToU pricing, compared with the social optimum achieved under complete information, is less than 5%.

关键词： Pricing Investment Costs Optimization Energy storage Lead acid batteries Complexity theory ToU pricing energy storage two-stage optimization stochastic programming storage investment

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Recourse in Kidney Exchange Programs

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INFORMS JOURNAL ON COMPUTING 2022年第2期34卷 1191-1206页

作者： Smeulders, Bart Bartier, Valentin Crama, Yves Spieksma, Frits C. R. Eindhoven Univ Technol Dept Math & Comp Sci NL-5612 AE Eindhoven Netherlands Grenoble INP Univ Grenoble Alpes G SCOP F-38031 Grenoble France Univ Liege HEC Management Sch B-4000 Liege Belgium

We introduce the problem of selecting patient-donor pairs in a kidney exchange program to undergo a crossmatch test, and we model this selection problem as a two-stage stochastic integer programming problem. The optimal solutions of this new formulation yield a larger expected number of realized transplants than previous approaches based on internal recourse or subset recourse. We settle the computational complexity of the selection problem by showing that it remains NP-hard even for maximum cycle length equal to two. Furthermore, we investigate to what extent different algorithmic approaches, including one based on Benders decomposition, are able to solve instances of the model. We empirically investigate the computational efficiency of this approach by solving randomly generated instances and study the corresponding running times as a function of maximum cycle length, and of the presence of nondirected donors. Summary of Contribution: This paper deals with an important and very complex issue linked to the optimization of transplant matchings in kidney exchange programs, namely, the inherent uncertainty in the assessment of compatibility between donors and recipients of transplants. Although this issue has previously received some attention in the optimization literature, most attempts to date have focused on applying recourse to solutions selected within restricted spaces. The present paper explicitly formulates the maximization of the expected number of transplants as a two-stage stochastic integer programming problem. The formulation turns out to be computationally difficulty, both from a theoretical and from a numerical perspective. Different algorithmic approaches are proposed and tested experimentally for its solution. The quality of the kidney exchanges produced by these algorithms compares favorably with that of earlier models.

关键词： Benders decomposition stochastic programming kidney exchange healthcare

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On rates of convergence for sample average approximations in the almost sure sense and in mean

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MATHEMATICAL programming 2022年第1期191卷 307-345页

作者： Banholzer, Dirk Fliege, Jorg Werner, Ralf Univ Southampton Dept Math Sci Southampton SO17 1BJ Hants England Univ Augsburg Inst Math D-86159 Augsburg Germany

We study the rates at which optimal estimators in the sample average approximation approach converge to their deterministic counterparts in the almost sure sense and in mean. To be able to quantify these rates, we consider the law of the iterated logarithm in a Banach space setting and first establish under relatively mild assumptions almost sure convergence rates for the approximating objective functions, which can then be transferred to the estimators for optimal values and solutions of the approximated problem. By exploiting a characterisation of the law of the iterated logarithm in Banach spaces, we are further able to derive under the same assumptions that the estimators also converge in mean, at a rate which essentially coincides with the one in the almost sure sense. This, in turn, allows to quantify the asymptotic bias of optimal estimators as well as to draw conclusive insights on their mean squared error and on the estimators for the optimality gap. Finally, we address the notion of convergence in probability to derive rates in probability for the deviation of optimal estimators and (weak) rates of error probabilities without imposing strong conditions on exponential moments. We discuss the possibility to construct confidence sets for the optimal values and solutions from our obtained results and provide a numerical illustration of the most relevant findings.

关键词： stochastic programming Sample average approximation Almost sure rates of convergence Rates of convergence in mean Law of the iterated logarithm

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A Framework for Capacity Expansion Planning in Failure-Prone Flow-Networks via Systemic Risk Analysis

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IEEE SYSTEMS JOURNAL 2022年第1期16卷 820-831页

作者： Aygun, Nazl Karatas Bulut, Onder Biyik, Emrah Yasar Univ Dept Ind Engn TR-35100 Izmir Turkey Yasar Univ Dept Energy Syst Engn TR-35100 Izmir Turkey

In this article, a capacity expansion framework is proposed for failure-prone flow-networks. A systemic risk measure that quantifies the risk of unsatisfied demand due to cascaded edge failures is considered. To minimize the total cost of additional edge capacities, while keeping the risk of unsatisfied demand below a certain threshold, a general stochastic optimization problem is formulated. The distribution of unsatisfied demand is calculated via Monte-Carlo simulations embodied within a grid search algorithm that identifies the feasible region. Thereafter, the cost-optimal edge capacity expansion plan is computed by a differential evolution algorithm. Contributions of this article are: 1) consideration of both immediate investment and future risk costs of capacity expansion plans;2) a generic flow-network model that can be tuned for different real-life applications;3) addressing the stochastic nature of both supply and demand simultaneously within a systemic risk framework;4) use of eigenvector centrality for edge grouping in systemic risk analysis. An extensive numerical study is performed to investigate the effects of different edge grouping methods, characteristics of stochastic components, and cost parameters on the feasible region and optimal solution. The proposed framework is also demonstrated on a case study adapted from ERCOT 13-bus test system.

关键词： Optimization stochastic processes Electric shock Capacity planning Indexes Supply and demand Probabilistic logic Cascading failures differential evolution (DE) flow-networks grid search algorithm (GSA) stochastic programming systemic risk

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Workforce training and operations planning for maintenance centres under demand uncertainty

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第5期60卷 1587-1599页

作者： Kafiabad, Shayan Tavakoli Zanjani, Masoumeh Kazemi Nourelfath, Mustapha Interuniv Res Ctr Enterprise Networks Logist & Tr Montreal PQ Canada Concordia Univ Dept Mech & Ind Engn Montreal PQ Canada Laval Univ Dept Mech Engn Quebec City PQ Canada

Companies that provide repair & overhaul services to the users of complex technical systems are confronted with uncertain volume of demand when making tactical decisions such as workforce training and planning of repair operations over an annual planning horizon. Given the high importance of equipment availability (e.g. gas turbines) to the users (e.g, power plants), any delay in the delivery of repaired equipment caused by demand uncertainty would lead to significant penalties and loss of customer goodwill. In this paper, a two-stage stochastic programming model is proposed to obtain the optimal number of items to repair, spare part inventory, and the number of operators to train with the goal of minimising the total expected cost of maintenance operations and late delivery. Outsourcing and borrowing strategies are adopted as corrective measures to reduce the probability of late delivery in the emerge of demand uncertainty. Numerical findings illustrate the importance of integrating uncertainty into these operations planning decisions as well as the mitigation strategies in handling the cost of the system.

关键词： Maintenance centres operations planning workforce training outsourcing stochastic programming

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Lagrangian dual decomposition for the ambulance relocation and routing considering stochastic demand with the truncated Poisson

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2022年 157卷 1-23页

作者： Lee, Yu-Ching Chen, Yu-Shih Chen, Albert Y. Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu Taiwan Natl Taiwan Univ Dept Civil Engn 1Sec 4Roosevelt Rd Taipei 106 Taiwan

The pre-hospital Emergency Medical Service (EMS) provides the critical care to the ill or injured patients, and evaluates and manages those patients at scene before their transport to an emergency medical facility. The Time to Arrive at Hospital (TAH) is a useful performance measurement defined as the time interval from the dispatch of an ambulance until the arrival of the patient at the destination facility. By taking into consideration of the short-term demand estimation, there is chance to improve the management of ambulances and reduce the TAH. This study proposes a new stochastic programming model to minimize the TAH within a complete dynamic relocation system. In this system, a truncated Poisson distribution is utilized for forecasting near future EMS requests, and a Lagrangian dual decomposition with branch-and bound is adapted as the solution methodology. By dynamically generating near future scenarios for the planning of ambulance relocation among bases, we obtain close-to-real-time ambulance relocation decisions. Scenarios collected from New Taipei City, Taiwan have shown that the proposed system has the potential to enhance the performance of the pre-hospital EMS.

关键词： Ambulance dynamic relocation Emergency Medical Service Dual decomposition stochastic programming

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Optimizing diesel fuel supply chain operations to mitigate power outages for hurricane relief

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IISE TRANSACTIONS 2022年第10期54卷 936-949页

作者： Yang, Haoxiang Duque, Daniel Morton, David P. Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA Chinese Univ Hong Kong Sch Data Sci Shenzhen Peoples R China Google Res Cambridge MA USA

Hurricanes can cause severe property damage and casualties in coastal regions. Diesel fuel plays a crucial role in hurricane disaster relief. It is important to optimize fuel supply chain operations so that emergency diesel fuel demand for power generation in a hurricane's immediate aftermath can be mitigated. It can be challenging to estimate diesel fuel demand and make informed decisions in the distribution process, accounting for the hurricane's path and severity. We develop predictive and prescriptive models to guide diesel fuel supply chain operations for hurricane disaster relief. We estimate diesel fuel demand from historical weather forecasts and power outage data. This predictive model feeds a prescriptive stochastic programming model implemented in a rolling-horizon fashion to dispatch tank trucks. This data-driven optimization tool provides a framework for decision support in preparation for approaching hurricanes, and our numerical results provide insights regarding key aspects of operations.

关键词： Diesel fuel supply chain hurricane relief stochastic programming

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