Algorithms to schedule medical appointments This paper was inspired by a field collaboration effort to develop and disseminate a real-time appointment scheduling decision support tool for an outpatient cancer infusion center in a large healthcare system. Two challenging aspects of scheduling daily medical appointments are that each patient is scheduled upon arrival without knowledge on future patients and that the appointments typically consume scarce physical resources (e.g., chairs, nurses, and doctors). A desirable schedule should have relatively smooth utilization over the course of a day to minimize the peak demand for the scarce resources. This paper develops new real-time (online) algorithms to schedule appointments in medical and other settings. It establishes theoretical properties of these algorithms, showing that they perform close to algorithms that could exploit full retrospective information on all the appointments. Additionally, it provides important insights to guide efficient real-time appointment scheduling policies in practice.
{"title":"Competitive Algorithms for the Online Minimum Peak Job Scheduling","authors":"Célia Escribe, Michael Hu, R. Levi","doi":"10.1287/opre.2021.0080","DOIUrl":"https://doi.org/10.1287/opre.2021.0080","url":null,"abstract":"Algorithms to schedule medical appointments This paper was inspired by a field collaboration effort to develop and disseminate a real-time appointment scheduling decision support tool for an outpatient cancer infusion center in a large healthcare system. Two challenging aspects of scheduling daily medical appointments are that each patient is scheduled upon arrival without knowledge on future patients and that the appointments typically consume scarce physical resources (e.g., chairs, nurses, and doctors). A desirable schedule should have relatively smooth utilization over the course of a day to minimize the peak demand for the scarce resources. This paper develops new real-time (online) algorithms to schedule appointments in medical and other settings. It establishes theoretical properties of these algorithms, showing that they perform close to algorithms that could exploit full retrospective information on all the appointments. Additionally, it provides important insights to guide efficient real-time appointment scheduling policies in practice.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87808884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A gaussian process-based random search framework for continuous optimization via simulation Stochastic optimization via simulation (OvS) is widely used for optimizing the performances of complex systems with continuous decision variables. Because of the existence of simulation noise and infinite feasible solutions, it is challenging to design an efficient mechanism to do the searching and estimation simultaneously to find the optimal solutions. In “Gaussian process-based random search for continuous optimization via simulation,” Wang et al. propose a Gaussian process-based random search (GPRS) framework for the design of single-observation and adaptive continuous OvS algorithms. This framework builds a Gaussian process surrogate model to estimate the objective function value of every solution based on a single observation of each sampled solution in each iteration and allow for a wide range of sampling distributions. They prove the global convergence and analyze the rate of convergence for algorithms under the GPRS framework. They also give a specific example of GPRS algorithms and validate its theoretical properties and practical efficiency using numerical experiments.
{"title":"Gaussian Process-Based Random Search for Continuous Optimization via Simulation","authors":"Xiuxian Wang, L. Hong, Zhibin Jiang, Haihui Shen","doi":"10.1287/opre.2021.0303","DOIUrl":"https://doi.org/10.1287/opre.2021.0303","url":null,"abstract":"A gaussian process-based random search framework for continuous optimization via simulation Stochastic optimization via simulation (OvS) is widely used for optimizing the performances of complex systems with continuous decision variables. Because of the existence of simulation noise and infinite feasible solutions, it is challenging to design an efficient mechanism to do the searching and estimation simultaneously to find the optimal solutions. In “Gaussian process-based random search for continuous optimization via simulation,” Wang et al. propose a Gaussian process-based random search (GPRS) framework for the design of single-observation and adaptive continuous OvS algorithms. This framework builds a Gaussian process surrogate model to estimate the objective function value of every solution based on a single observation of each sampled solution in each iteration and allow for a wide range of sampling distributions. They prove the global convergence and analyze the rate of convergence for algorithms under the GPRS framework. They also give a specific example of GPRS algorithms and validate its theoretical properties and practical efficiency using numerical experiments.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"18 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89467153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Optimal Nonstationary Optimization Without Knowing Function Changes Nonstationary stochastic optimization plays a vital role in a number of computer science and operations research applications. It is known how to design and analyze algorithms that optimize a sequence of strongly convex/concave and smooth functions with access to only one-point noisy function values with the underlying function sequence subject to maximum magnitude of function changes. In recent work from Wang titled “Technical Note: On Adaptivity in Nonstationary Stochastic Optimization with Bandit Feedback,” an optimization algorithm is designed and analyzed without assuming the magnitude of function changes is known in advance. Optimality of the designed algorithm is demonstrated.
{"title":"Technical Note—On Adaptivity in Nonstationary Stochastic Optimization with Bandit Feedback","authors":"Yining Wang","doi":"10.1287/opre.2022.0576","DOIUrl":"https://doi.org/10.1287/opre.2022.0576","url":null,"abstract":"Optimal Nonstationary Optimization Without Knowing Function Changes Nonstationary stochastic optimization plays a vital role in a number of computer science and operations research applications. It is known how to design and analyze algorithms that optimize a sequence of strongly convex/concave and smooth functions with access to only one-point noisy function values with the underlying function sequence subject to maximum magnitude of function changes. In recent work from Wang titled “Technical Note: On Adaptivity in Nonstationary Stochastic Optimization with Bandit Feedback,” an optimization algorithm is designed and analyzed without assuming the magnitude of function changes is known in advance. Optimality of the designed algorithm is demonstrated.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"24 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78995857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Call Center Staffing: On-Demand Is in Demand Traditional call centers face challenges in quickly adapting their service capacity to meet fluctuations in demand, even with short staffing periods. In their research article “Expanding Service Capabilities Through an On-Demand Workforce,” Sun and Liu propose a solution for call centers to improve service levels and reduce operating expenses. They develop a two-stage decision model that determines the optimal combination of permanent and on-demand staff, along with an optimal on-demand staffing and call scheduling policy that minimizes costs. By utilizing diffusion approximation, they derive approximate solutions for the second-stage problem. The optimal staffing rule employs switching boundaries to determine when to bring in or dismiss on-demand agents, while the scheduling rule employs a nested threshold rule that prioritizes customer urgency. Interestingly, the call scheduling rule exhibits an intriguing pattern arising from the interaction between on-demand staffing and call scheduling decisions. Their research findings highlight significant cost savings achievable through the implementation of an on-demand workforce.
{"title":"Expanding Service Capabilities Through an On-Demand Workforce","authors":"Xu Sun, Weiliang Liu","doi":"10.1287/opre.2021.0651","DOIUrl":"https://doi.org/10.1287/opre.2021.0651","url":null,"abstract":"Call Center Staffing: On-Demand Is in Demand Traditional call centers face challenges in quickly adapting their service capacity to meet fluctuations in demand, even with short staffing periods. In their research article “Expanding Service Capabilities Through an On-Demand Workforce,” Sun and Liu propose a solution for call centers to improve service levels and reduce operating expenses. They develop a two-stage decision model that determines the optimal combination of permanent and on-demand staff, along with an optimal on-demand staffing and call scheduling policy that minimizes costs. By utilizing diffusion approximation, they derive approximate solutions for the second-stage problem. The optimal staffing rule employs switching boundaries to determine when to bring in or dismiss on-demand agents, while the scheduling rule employs a nested threshold rule that prioritizes customer urgency. Interestingly, the call scheduling rule exhibits an intriguing pattern arising from the interaction between on-demand staffing and call scheduling decisions. Their research findings highlight significant cost savings achievable through the implementation of an on-demand workforce.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"17 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82148218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A New Approach to Contract Design with Private Inventory Information In a typical decentralized supply chain, a downstream retailer privately observes its inventory level and has an informational advantage over the upstream supplier. In “A Stationary Infinite-Horizon Supply Contract Under Asymmetric Inventory Information” by Bensoussan, Sethi, and Wang, the authors study how to optimally design a stationary, truth-telling, long-term contract in such a setting. In contrast to the classic first order approach in literature, they formulate the contract design as an optimization over a functional space and develop a solution approach based on the calculus of variations. They further apply their necessary optimality condition to the class of batch-order contracts, which replenish a prespecified inventory quantity for a fixed payment in each period only when the retailer has zero inventory on hand.
{"title":"A Stationary Infinite-Horizon Supply Contract Under Asymmetric Inventory Information","authors":"A. Bensoussan, S. Sethi, Shouqiang Wang","doi":"10.1287/opre.2020.0495","DOIUrl":"https://doi.org/10.1287/opre.2020.0495","url":null,"abstract":"A New Approach to Contract Design with Private Inventory Information In a typical decentralized supply chain, a downstream retailer privately observes its inventory level and has an informational advantage over the upstream supplier. In “A Stationary Infinite-Horizon Supply Contract Under Asymmetric Inventory Information” by Bensoussan, Sethi, and Wang, the authors study how to optimally design a stationary, truth-telling, long-term contract in such a setting. In contrast to the classic first order approach in literature, they formulate the contract design as an optimization over a functional space and develop a solution approach based on the calculus of variations. They further apply their necessary optimality condition to the class of batch-order contracts, which replenish a prespecified inventory quantity for a fixed payment in each period only when the retailer has zero inventory on hand.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"4 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80319154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jonathan Eckstein, Jean-Paul Watson, David L. Woodruff
In “Projective Hedging Algorithms for Multistage Stochastic Programming, Supporting Distributed and Asynchronous Implementation,” Eckstein, Watson, and Woodruff derive a new class of decomposition methods for convex multistage stochastic programs defined on finite but potentially large scenario trees. These methods resemble Rockafellar and Wets’ now-classical progressive hedging (PH) method but are based on a flexible projective operator-splitting scheme instead of the standard alternating direction method of multipliers (ADMM). The new algorithms only need to reoptimize subproblems for a subset of the scenarios at each iteration, instead of all of them, and are also amenable to a form of asynchronous implementation, without the algorithm randomization or small step-size requirements usually imposed in such contexts. In the online appendix, the authors demonstrate significant computational gains over PH, applying hundreds or thousands of processor cores to problem instances with up to a million scenarios.
{"title":"Projective Hedging Algorithms for Multistage Stochastic Programming, Supporting Distributed and Asynchronous Implementation","authors":"Jonathan Eckstein, Jean-Paul Watson, David L. Woodruff","doi":"10.1287/opre.2022.0228","DOIUrl":"https://doi.org/10.1287/opre.2022.0228","url":null,"abstract":"In “Projective Hedging Algorithms for Multistage Stochastic Programming, Supporting Distributed and Asynchronous Implementation,” Eckstein, Watson, and Woodruff derive a new class of decomposition methods for convex multistage stochastic programs defined on finite but potentially large scenario trees. These methods resemble Rockafellar and Wets’ now-classical progressive hedging (PH) method but are based on a flexible projective operator-splitting scheme instead of the standard alternating direction method of multipliers (ADMM). The new algorithms only need to reoptimize subproblems for a subset of the scenarios at each iteration, instead of all of them, and are also amenable to a form of asynchronous implementation, without the algorithm randomization or small step-size requirements usually imposed in such contexts. In the online appendix, the authors demonstrate significant computational gains over PH, applying hundreds or thousands of processor cores to problem instances with up to a million scenarios.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"94 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79813521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
California Utility Firm Implements Innovative Model, Reducing Costs by 4% A California utility firm has successfully implemented a pioneering model to balance electricity demand and supply while minimizing costs. By utilizing direct load control contracts (DLCCs), the firm can reduce energy consumption during peak hours. Researchers developed an integer stochastic dynamic optimization problem that considers monthly and annual constraints, allowing for effective execution of DLCCs. Incorporating a “reduce-to-threshold” policy to flatten energy-consumption curves during high demand, the model was verified using real data from the California Independent System Operator. When implemented, the utility firm achieved an impressive cost reduction of approximately 4%. Sensitivity analysis was conducted to enhance customer experience and improve DLCC contract features. The success of this innovative model highlights the potential of DLCCs and advanced optimization techniques in the energy sector, offering a blueprint for other utility companies seeking to optimize grid stability and reduce costs.
{"title":"Flattening Energy-Consumption Curves by Monthly Constrained Direct Load Control Contracts","authors":"A. Fattahi, S. Ghodsi, S. Dasu, R. Ahmadi","doi":"10.1287/opre.2021.0638","DOIUrl":"https://doi.org/10.1287/opre.2021.0638","url":null,"abstract":"California Utility Firm Implements Innovative Model, Reducing Costs by 4% A California utility firm has successfully implemented a pioneering model to balance electricity demand and supply while minimizing costs. By utilizing direct load control contracts (DLCCs), the firm can reduce energy consumption during peak hours. Researchers developed an integer stochastic dynamic optimization problem that considers monthly and annual constraints, allowing for effective execution of DLCCs. Incorporating a “reduce-to-threshold” policy to flatten energy-consumption curves during high demand, the model was verified using real data from the California Independent System Operator. When implemented, the utility firm achieved an impressive cost reduction of approximately 4%. Sensitivity analysis was conducted to enhance customer experience and improve DLCC contract features. The success of this innovative model highlights the potential of DLCCs and advanced optimization techniques in the energy sector, offering a blueprint for other utility companies seeking to optimize grid stability and reduce costs.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"36 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89239430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Solving realistic security-constrained optimal power flow problems In “A surrogate-based asynchronous decomposition technique for realistic security-constrained optimal power flow problems,” we propose a new algorithm for solving a classical problem in power grid operations: the security-constrained optimal power flow, considering its nonlinearities and realistic transitions between nominal and emergency post-contingency operations. Solving security-constrained optimal power flow problems accurately is a critical function, upon which depends the reliability, security, and efficiency of power systems as well as the correct functioning of other critical infrastructure dependent on electricity. The proposed algorithm was extensively tested against many state-of-the-art approaches using realistic and real instances in the ARPA-E Grid Optimization Competition Challenge 1, where it found the best-known solution for 58% of the instances, attained an average gap of less than 0.2%, and obtained the best overall scores, thereby winning all divisions of Challenge 1 with a very strong first place.
{"title":"A Surrogate-Based Asynchronous Decomposition Technique for Realistic Security-Constrained Optimal Power Flow Problems","authors":"C. Petra, I. Aravena","doi":"10.1287/opre.2022.0229","DOIUrl":"https://doi.org/10.1287/opre.2022.0229","url":null,"abstract":"Solving realistic security-constrained optimal power flow problems In “A surrogate-based asynchronous decomposition technique for realistic security-constrained optimal power flow problems,” we propose a new algorithm for solving a classical problem in power grid operations: the security-constrained optimal power flow, considering its nonlinearities and realistic transitions between nominal and emergency post-contingency operations. Solving security-constrained optimal power flow problems accurately is a critical function, upon which depends the reliability, security, and efficiency of power systems as well as the correct functioning of other critical infrastructure dependent on electricity. The proposed algorithm was extensively tested against many state-of-the-art approaches using realistic and real instances in the ARPA-E Grid Optimization Competition Challenge 1, where it found the best-known solution for 58% of the instances, attained an average gap of less than 0.2%, and obtained the best overall scores, thereby winning all divisions of Challenge 1 with a very strong first place.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"35 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85612412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A major focus of the simulation literature is the study of optimal budget allocation. The goal is to divide a simulation budget between alternatives with unknown values in a manner that leads to efficient identification of the best alternative. Existing analytical techniques, based on large deviations theory, are limited to finite sets of alternatives, each of which is assigned a certain proportion of the budget. In “A New Rate-Optimal Sampling Allocation for Linear Belief Models,” Zhou and Ryzhov develop the first provably optimal budget allocation for a continuous problem where linear regression is used to model the value of a choice. The allocation is expressible in closed form and is simpler and easier to implement than analogous solutions for the discrete setting. This work bridges the emerging literature on contextual (regression-based) learning and the well-known statistical problem of optimal experimental design.
{"title":"Technical Note—A New Rate-Optimal Sampling Allocation for Linear Belief Models","authors":"Jiaqi Zhou, I. Ryzhov","doi":"10.1287/opre.2022.2337","DOIUrl":"https://doi.org/10.1287/opre.2022.2337","url":null,"abstract":"A major focus of the simulation literature is the study of optimal budget allocation. The goal is to divide a simulation budget between alternatives with unknown values in a manner that leads to efficient identification of the best alternative. Existing analytical techniques, based on large deviations theory, are limited to finite sets of alternatives, each of which is assigned a certain proportion of the budget. In “A New Rate-Optimal Sampling Allocation for Linear Belief Models,” Zhou and Ryzhov develop the first provably optimal budget allocation for a continuous problem where linear regression is used to model the value of a choice. The allocation is expressible in closed form and is simpler and easier to implement than analogous solutions for the discrete setting. This work bridges the emerging literature on contextual (regression-based) learning and the well-known statistical problem of optimal experimental design.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"19 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74186718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper addresses decision making in multiple stages, where prior information is available and where consecutive and successive decisions are made. Risk measures assess the random outcome by taking various candidate probability measures into account. To justify decisions in multiple stages, it is essential to have conditional risk measures available, which respect the information, which was already revealed in the past. The paper addresses different variants of risk measures, discusses their properties in the specific context and their implications in multistage decision making. Various examples of risk measures on simple probability spaces with finite support illustrate the content. The Wasserstein and nested distance are involved to make decision making with numerous scenarios numerically tractalbe.
{"title":"Conditional Distributionally Robust Functionals","authors":"A. Shapiro, A. Pichler","doi":"10.1287/opre.2023.2470","DOIUrl":"https://doi.org/10.1287/opre.2023.2470","url":null,"abstract":"This paper addresses decision making in multiple stages, where prior information is available and where consecutive and successive decisions are made. Risk measures assess the random outcome by taking various candidate probability measures into account. To justify decisions in multiple stages, it is essential to have conditional risk measures available, which respect the information, which was already revealed in the past. The paper addresses different variants of risk measures, discusses their properties in the specific context and their implications in multistage decision making. Various examples of risk measures on simple probability spaces with finite support illustrate the content. The Wasserstein and nested distance are involved to make decision making with numerous scenarios numerically tractalbe.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"224 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86685745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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