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A two-phase approach to scheduling multi-category outpatient appointments - A case study of a women's clinic

HEALTH CARE MANAGEMENT SCIENCE 2013年第3期16卷 197-216页

作者： Qu, Xiuli Peng, Yidong Kong, Nan Shi, Jing N Carolina Agr & Tech State Univ Dept Ind & Syst Engn Greensboro NC 27411 USA N Dakota State Univ Dept Ind & Mfg Engn Fargo ND 58102 USA Purdue Univ Weldon Sch Biomed Engn W Lafayette IN 47907 USA

In this paper, we propose a two-phase approach for designing a weekly scheduling template for outpatient clinics providing multiple types of services. In many outpatient clinics, various service types are categorized to address the operational challenge of substantial changeover time between certain pairs of services. In the first phase of our approach, a mixed-integer program is formulated to assign service categories to clinic sessions during a week and determine the optimal number of appointments reserved for each service type in each clinic session. The objective in the first phase is to balance the workload of the providers among clinic sessions. In the second phase, a stochastic mixed-integer program is formulated for each clinic session to assign each contained appointment with a starting time based on several time-based performance measures. To solve the formulated stochastic program, we develop a Monte Carlo sampling based genetic algorithm. The two-phase approach is tested numerically with cases derived from a real women's clinic. Our results demonstrate that the two-phase approach can efficiently find promising weekly appointment scheduling templates for outpatient clinics. In addition, our results suggest that the best suboptimal scheduling templates found become more sensitive to the weighting coefficients of the time-based measures as the provider workload increases.

关键词： Appointment Scheduling Outpatient Clinics stochastic integer programming Monte Carlo Sampling Genetic Algorithms

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stochastics in discrete logistics models:What can we do?

Stochastics in discrete logistics models:What can we do?

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IEEE International Conference on Industrial Engineering and Engineering Management

作者： Wallace, Stein W. Kaut, Michal Crainic, Teodor Gabriel Lium, Arnt-Gunnar Chinese Univ Hong Kong Molde Univ Coll Hong Kong Hong Kong Peoples R China Molde Univ Coll Molde Norway Univ Quebec Montreal PQ G1V 2M3 Canada SINTEF Trondheim Norway

ISBN: (纸本)9781424415281

A large number of logistics problems involve integer programming. Adding stochastics to such a problem seems computationally prohibitive. On the other hand, in most cases, stochastics is an integral part of the underlying problem. And more importantly, without adding stochastics explicitly, we may lose important aspects of the solution we are searching for. In this presentation we give a new approach to such problems, and apply the approach to service network design.

关键词： stochastic integer programming flexibility robustness options

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The DNA Genetic Algorithm Applied for Solving Optimal Placement of Sensors

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2nd International Conference on Intelligent Structure and Vibration Control (ISVC 2012)

作者： Liu, Xin Feng, Zhongren Wang, Xiongjian Wang, Baofu Wuhan Univ Technol Wuhan 430070 Peoples R China

ISBN: (纸本)9783037853894

In order to solve optimal placement of bridge sensors based on the modal information, a multi-objective integer programming expected value model is established and the number of modal is considered as a stochastic variable in this paper, and here Fisher and MAC matrix are combined as the objective functions. Since DNA Genetic algorithm has the merits of plentiful coding, and decoding, conveying complex knowledge flexibly, these merits and the technique of stochastic simulation are also combined, which for estimating stochastic integer programming expected value models problem. And finally the feasibility of the algorithm is showed by XuGe Bridge as an example.

关键词： Optimal placement of bridge sensors Fisher and MAC matrix stochastic integer programming DNA genetic algorithm stochastic simulation

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A stochastic programming Model for Service Scheduling with Uncertain Demand: an Application in Open-Access Clinic Scheduling

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Operations Research Forum 2021年第3期2卷 43页

作者： Fu, Yu Banerjee, Amarnath Department of Industrial and Systems Engineering Texas A&M University College Station TX United States

This paper addressed a scheduling problem which handles urgent tasks along with existing schedules. The uncertainties in this problem come from random process of existing schedules and unknown upcoming urgent tasks. To deal with the uncertainties, this paper proposes a stochastic integer programming (SIP) based aggregated online scheduling method. The method is illustrated through a study case from the outpatient clinic block-wise scheduling system which is under a hybrid scheduling policy combining regular far-in-advance policy and the open-access policy. The COVID-19 pandemic brings more challenges for the healthcare system including the fluctuations of service time and increasing urgent requests which this paper is designed for. The schedule framework designed in the method is comprehensive to accommodate various uncertainties in the healthcare service system, such as: no-shows, cancellations and punctuality of patients as well as preference of patients over time slots and physicians. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Cancellation No-show Punctuality Service scheduling stochastic integer programming Urgent scheduling

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stochastic Resource Allocation for Semantic Communication-aided Virtual Transportation Networks in the Metaverse 25

Stochastic Resource Allocation for Semantic Communication-ai...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Ng, Wei Chong Du, Hongyang Lim, Wei Yang Bryan Xiong, Zehui Niyato, Dusit Miao, Chunyan Alibaba Grp Hangzhou Peoples R China Alibaba NTU Joint Res Inst Hangzhou Peoples R China Energy Res Inst NTU Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Singapore Univ Technol & Design Singapore Singapore

ISBN: (纸本)9798350303582;9798350303599

The physical-virtual world synchronization to develop the Metaverse will require a massive transmission and exchange of data. In this paper, we introduce semantic communication for the development of virtual transportation networks in the Metaverse. Leveraging the perception capabilities of edge devices, virtual service providers (VSPs) can subscribe to their preferred edge devices to receive the semantic data of interest. However, the demands of the VSPs are highly dependent on the users that they are serving. To address the resource allocation problem amid stochastic user demand, we propose a stochastic semantic transmission scheme (SSTS) based on two-stage stochastic integer programming. Using real data captured by edge devices we deploy in Singapore, the simulation results show that SSTS can minimize the transmission cost of the VSPs while accounting for the users' demand uncertainties.

关键词： Metaverse Semantic Communication Resource Allocation stochastic integer programming

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Optimization of Integrated Design and Operation of Microgrids Under Uncertainty 19

Optimization of Integrated Design and Operation of Microgrid...

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19th Power Systems Computation Conference (PSCC)

作者： Moshi, Godfrey Gladson Bovo, Cristian Berizzi, Alberto Taccari, Leonardo Politecn Milan Dipartimento Energia Via La Masa 34 Milan Italy Politecn Milan DEIB Operat Res Grp Piazza Leonardo da Vinci 32 I-20133 Milan Italy

ISBN: (纸本)9788894105124

We present two Mixed-integer Linear programming (MILP) models for a complete microgrid planning problem which consider uncertainties in the main input data (hourly solar irradiance, wind speed and electricity demand). The first model adopts a Two-Stage stochastic integer programming (2SSIP) formulation with discrete scenarios, whereas the second model adopts a Robust Optimization (RO) formulation with polyhedral uncertainty sets. The aim is to determine the optimal combination, capacities, and number of components to install in the microgrid considering long-term operations and uncertainty in the main input data. The 2SSIP model offers the possibility to obtain a planning solution using discrete scenarios sampled from appropriate probability distributions. The RO model gives a planning solution which is guaranteed to be feasible for any realization of input data within specified uncertainty sets. To show and compare the effectiveness of these models, we present a case study in which we apply the two models to plan a standalone microgrid in Singida, Tanzania. The proposed models can be applied for planning and detailed feasibility studies on generic microgrids with renewables, storage batteries and diesel generators.

关键词： microgrid planning optimization robust optimization stochastic integer programming uncertainty

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An adaptive model with joint chance constraints for a hybrid wind-conventional generator system

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COMPUTATIONAL MANAGEMENT SCIENCE 2018年第3-4期15卷 563-582页

作者： Singh, Bismark Morton, David P. Santoso, Surya Univ Texas Austin Grad Program Operat Res & Ind Engn Austin TX 78712 USA Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA Univ Texas Austin Dept Elect & Comp Engn Austin TX 78701 USA

We analyze scheduling a hybrid wind-conventional generator system to make it dispatchable, with the aim of profit maximization. Our models ensure that with high probability we satisfy the day-ahead power promised by the model, using combined output of the conventional and wind generators. We consider two scenarios, which differ in whether the conventional generator must commit to its schedule prior to observing the wind-power realizations or has the flexibility to adapt in near real-time to these realizations. We investigate the synergy between the conventional generator and wind farm in these two scenarios. Computationally, the non-adaptive model is relatively tractable, benefiting from a strong extended-variable formulation as an integer program. The adaptive model is a two-stage stochastic integer program with joint chance constraints. Such models have seen limited attention in the literature because of the computational challenges they pose. However, we develop an iterative regularization scheme in which we solve a sequence of sample average approximations under a growing sample size. This reduces computational effort dramatically, and our empirical results suggest that it heuristically achieves high-quality solutions. Using data from a wind farm in Texas, we demonstrate that the adaptive model significantly outperforms the non-adaptive model in terms of synergy between the conventional generator and the wind farm, with expected profit more than doubled.

关键词： stochastic integer programming Chance-constrained optimization Wind power Hybrid renewable system

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A Learning-Based Algorithm to Quickly Compute Good Primal Solutions for stochastic integer Programs 17th

A Learning-Based Algorithm to Quickly Compute Good Primal So...

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17th International Conference on the Integration of Constraint programming, Artificial Intelligence, and Operations Research (CPAIOR)

作者： Bengio, Yoshua Frejinger, Emma Lodi, Andrea Patel, Rahul Sankaranarayanan, Sriram Polytech Montreal Canada Excellence Res Chair Montreal PQ Canada Univ Montreal Dept Comp Sci & Operat Res Montreal PQ Canada Mila Quebec Artificial Intelligence Inst Montreal PQ Canada

ISBN: (纸本)9783030589424;9783030589417

We propose a novel approach using supervised learning to obtain near-optimal primal solutions for two-stage stochastic integer programming (2SIP) problems with constraints in the first and second stages. The goal of the algorithm is to predict a representative scenario (RS) for the problem such that, deterministically solving the 2SIP with the random realization equal to the RS, gives a near-optimal solution to the original 2SIP. Predicting an RS, instead of directly predicting a solution ensures first-stage feasibility of the solution. If the problem is known to have complete recourse, second-stage feasibility is also guaranteed. For computational testing, we learn to find an RS for a two-stage stochastic facility location problem with integer variables and linear constraints in both stages and consistently provide near-optimal solutions. Our computing times are very competitive with those of general-purpose integer programming solvers to achieve a similar solution quality.

关键词： stochastic integer programming Machine learning Heuristics

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Appointment Scheduling of Diagnostic Facilities Subject to Non-stationary Emergency Demand and Waiting Time Targets 21

Appointment Scheduling of Diagnostic Facilities Subject to N...

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21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Wen, Jing Geng, Na Xie, Xiaolan Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Ecole Natl Super Mines F-42023 St Etienne France

ISBN: (纸本)9781509013142

Diagnostic facility is one of the most important critical resources in the hospital. Patient scheduling plays an important role in managing these facilities, especially when they are shared between regular and emergency patients. Due to the non-stationary emergency arrival and waiting time targets of emergency patients, it is challenging for hospital managers to make appointment scheduling decision and real time scheduling decisions, i.e., how many regular patients could reserve the service and how to coordinate the service of both types of patients. To deal with this problem, this paper proposes a stochastic integer programming model by considering the uncertainty of emergency patients and their waiting time requirement. The objective is to minimize the weighted idle time, overtime, and patients' waiting time. Monte Carlo optimization is used to solve this model. Numerical experiments are proposed to show the usefulness of the proposed model for investigation of the influence of different parameters.

关键词： healthcare appointment scheduling stochastic integer programming Monte Carlo optimization

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Decomposition Algorithms and Parallel Computing for Chance-Constrained and stochastic integer Programs with Applications

Decomposition Algorithms and Parallel Computing for Chance-C...

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作者： Deng, Yan University of Michigan

学位级别：Ph.D.

The focus of this dissertation is to develop solution methods for stochastic programs with binary decisions and risk-averse features such as chance constraint or risk-minimizing objective. We approach these problems through scenario-based reformulations, which are often of intractable scale due to the use of a large number of scenarios to represent the uncertainty. Our goal is to develop specialized decomposition algorithms for solving the problem in reasonable time. We first study a surgery planning problem with uncertainty in surgery durations. A common practice is to first assign operating rooms to surgeries and then to develop schedules. We propose a chance-constrained model that integrates these two steps. A branch-and-cut algorithm is developed, which exploits valid inequalities derived from a bin packing problem and single-machine scheduling problems. We also discuss models and solutions given ambiguous distributional information. Computational results demonstrate the efficacy of the proposed algorithm and provide insights into enhancing performance by the proposed model. Next, we study general chance-constrained 0-1 programs, where decisions made before realization of uncertainty are binary. We develop dual decomposition algorithms that find solutions through bounds and cuts efficiently. We derive a proposition about computing the Lagrangian dual whose application substantially reduces the number of subproblems to solve, and deploy cut aggregation that accelerates the solution of subproblems. We also explore parallel schemes to implement our algorithms in a distributed system. All of them improve the efficacy effectively. We then study dual decomposition for risk-averse stochastic 0-1 programs, which minimize the risk of some random outcome measured by a coherent risk function. Using generic dual representations for coherent risk measures, we derive equivalent risk-neutral minimax reformulations, to which dual decomposition methods apply. We investigate how

关键词： stochastic integer programming Decomposition algorithms Chance-constrained programming Risk-averse stochastic programming Parallel computing Thesis

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