When having access to demand forecasts, a crucial question is how to effectively use this information to make better resource allocation decisions, especially during demand surges like the COVID-19 pandemic. Despite the emergence of various advanced prediction models for hospital resources, there has been a lack of prescriptive solutions for hospital managers seeking concrete decision support, for example, guidance on whether to allocate beds from other specialties to meet the surge demand from COVID-19 patients by postponing elective surgeries. In their paper “Optimal Routing under Demand Surge: the Value of Future Arrival Rate,” the authors present a systematic framework to incorporate future demand into routing decisions in parallel server systems with partial flexibility and quantify the benefits of doing so. They propose a simple and interpretable two-stage index-based policy that explicitly incorporates demand forecasts into real-time routing decisions. Their analytical and numerical results demonstrate the policy’s effectiveness, even in the presence of large prediction errors.
{"title":"Optimal Routing Under Demand Surges: The Value of Future Arrival Rates","authors":"Jinsheng Chen, Jing Dong, P. Shi","doi":"10.1287/opre.2022.0282","DOIUrl":"https://doi.org/10.1287/opre.2022.0282","url":null,"abstract":"When having access to demand forecasts, a crucial question is how to effectively use this information to make better resource allocation decisions, especially during demand surges like the COVID-19 pandemic. Despite the emergence of various advanced prediction models for hospital resources, there has been a lack of prescriptive solutions for hospital managers seeking concrete decision support, for example, guidance on whether to allocate beds from other specialties to meet the surge demand from COVID-19 patients by postponing elective surgeries. In their paper “Optimal Routing under Demand Surge: the Value of Future Arrival Rate,” the authors present a systematic framework to incorporate future demand into routing decisions in parallel server systems with partial flexibility and quantify the benefits of doing so. They propose a simple and interpretable two-stage index-based policy that explicitly incorporates demand forecasts into real-time routing decisions. Their analytical and numerical results demonstrate the policy’s effectiveness, even in the presence of large prediction errors.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"23 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80976538","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}
In “Demand Estimation Under Uncertain Consideration Sets,” Jagabathula, Mitrofanov, and Vulcano investigate statistical properties of the consider-then-choose (CTC) models, which gained recent attention in the operations literature as an alternative to the classical random utility (RUM) models. The general class of CTC models is defined by a general joint distribution over ranking lists and consideration sets. Starting from the important result that the CTC and RUM classes are equivalent in terms of explanatory power, the authors characterize conditions under which CTC models become identified. Then, they propose expectation-maximization (EM) methods to solve the related estimation problem for different subclasses of CTC models, building from the provably convergent outer-approximation algorithm. Finally, subclasses of CTC models are tested on a synthetic data set and on two real data sets: one from a grocery chain and one from a peer-to-peer (P2P) car sharing platform. The results are consistent in assessing that CTC models tend to dominate RUM models with respect to prediction accuracy when the training data are noisy (i.e., transaction records do not necessarily reflect the physical inventory records) and when there is significant asymmetry between the training data set and the testing data set. These conditions are naturally verified in P2P sharing platforms and in retailers working on long-term forecasts (e.g., semester long) or geographical aggregate forecasts (e.g., forecasts at the distribution center level).
{"title":"Demand Estimation Under Uncertain Consideration Sets","authors":"Srikanth Jagabathula, Dmitry Mitrofanov, Gustavo Vulcano","doi":"10.1287/opre.2022.0006","DOIUrl":"https://doi.org/10.1287/opre.2022.0006","url":null,"abstract":"In “Demand Estimation Under Uncertain Consideration Sets,” Jagabathula, Mitrofanov, and Vulcano investigate statistical properties of the consider-then-choose (CTC) models, which gained recent attention in the operations literature as an alternative to the classical random utility (RUM) models. The general class of CTC models is defined by a general joint distribution over ranking lists and consideration sets. Starting from the important result that the CTC and RUM classes are equivalent in terms of explanatory power, the authors characterize conditions under which CTC models become identified. Then, they propose expectation-maximization (EM) methods to solve the related estimation problem for different subclasses of CTC models, building from the provably convergent outer-approximation algorithm. Finally, subclasses of CTC models are tested on a synthetic data set and on two real data sets: one from a grocery chain and one from a peer-to-peer (P2P) car sharing platform. The results are consistent in assessing that CTC models tend to dominate RUM models with respect to prediction accuracy when the training data are noisy (i.e., transaction records do not necessarily reflect the physical inventory records) and when there is significant asymmetry between the training data set and the testing data set. These conditions are naturally verified in P2P sharing platforms and in retailers working on long-term forecasts (e.g., semester long) or geographical aggregate forecasts (e.g., forecasts at the distribution center level).","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81709717","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}
Routing control is an important component in many engineering and management systems consisting of multiple and possibly heterogeneous servers. Imagine that upon the arrival of each job (or customer), a controller will evaluate the available (dynamic) state information and make a decision to dispatch the job to one of the servers. The state information can be queue length, arrival history, service history, and so on, depending on the nature of the application. How will the controller use the available state information to minimize the average waiting time an arriving job may experiences? In the paper, “Optimal Routing to Parallel Servers in Heavy Traffic,” Ye carries out the heavy traffic analysis to identify the routing policies that best use the available state information. For example, when there is no state information available for routing control, the best “blind” strategy is to dispatch the incoming jobs in a weighted round-robin fashion that exhibits certain form of the square-root rule. Although in the case that the job arrival history is available, the controller should use the information by closely chasing a kind of “arrival deviation,” which can reduce up to 50% of the waiting time compared with the best blind strategy. This study sheds new insights into the value of state information for routing control and provides new tools for engineering and service system design.
{"title":"Optimal Routing to Parallel Servers in Heavy Traffic","authors":"H. Ye","doi":"10.1287/opre.2022.0055","DOIUrl":"https://doi.org/10.1287/opre.2022.0055","url":null,"abstract":"Routing control is an important component in many engineering and management systems consisting of multiple and possibly heterogeneous servers. Imagine that upon the arrival of each job (or customer), a controller will evaluate the available (dynamic) state information and make a decision to dispatch the job to one of the servers. The state information can be queue length, arrival history, service history, and so on, depending on the nature of the application. How will the controller use the available state information to minimize the average waiting time an arriving job may experiences? In the paper, “Optimal Routing to Parallel Servers in Heavy Traffic,” Ye carries out the heavy traffic analysis to identify the routing policies that best use the available state information. For example, when there is no state information available for routing control, the best “blind” strategy is to dispatch the incoming jobs in a weighted round-robin fashion that exhibits certain form of the square-root rule. Although in the case that the job arrival history is available, the controller should use the information by closely chasing a kind of “arrival deviation,” which can reduce up to 50% of the waiting time compared with the best blind strategy. This study sheds new insights into the value of state information for routing control and provides new tools for engineering and service system design.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"75 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89636518","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}
Contract Design for Outsourcing Search Firms commonly outsource search for new employees, real estate, or technology to external agents. What should the contracts with these agents look like, and under which conditions should companies even hire agents (as opposed to doing the search in house)? These are the questions studied in “The when and how of delegated search” by Zorc et al. The authors find that the optimal contracts pay the agent a per-time fee as well as a bonus for finding an acceptable alternative. The size of this bonus is defined on signing of the contract and decreases over time. The decision of whether to outsource at all hinges on the firm’s trade-off between speed and quality; in-house search becomes optimal for a firm that prioritizes quality, but outsourcing offers better speed.
{"title":"The When and How of Delegated Search","authors":"Saša Zorc, Ilia Tsetlin, Sameer Hasija, S. Chick","doi":"10.1287/opre.2019.0498","DOIUrl":"https://doi.org/10.1287/opre.2019.0498","url":null,"abstract":"Contract Design for Outsourcing Search Firms commonly outsource search for new employees, real estate, or technology to external agents. What should the contracts with these agents look like, and under which conditions should companies even hire agents (as opposed to doing the search in house)? These are the questions studied in “The when and how of delegated search” by Zorc et al. The authors find that the optimal contracts pay the agent a per-time fee as well as a bonus for finding an acceptable alternative. The size of this bonus is defined on signing of the contract and decreases over time. The decision of whether to outsource at all hinges on the firm’s trade-off between speed and quality; in-house search becomes optimal for a firm that prioritizes quality, but outsourcing offers better speed.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"63 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75858369","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 Little Pessimism Goes a Long Way Data-driven optimization is concerned with finding a decision, using data and perhaps a model, that performs well when it is applied on a new unseen data point. Data-driven optimization is challenging because data are limited or the model is wrong or the environment in which the decision is being applied is different from the one in which the training data were collected. Distributionally robust optimization (DRO), a worst case optimization method for finding decisions that are insensitive to model error, can sometimes but not always deliver a decision that has a larger out-of-sample expected reward than the sample average approximation (SAA). “A Data Driven Approach to Beating SAA out of Sample” by Jun-ya Gotoh, Michael Kim, and Andrew Lim shows that if worst case (DRO) solutions fail at this task, then the solution of a best case distributionally optimistic optimization problem will do the job. As good as this sounds, there is a catch: whereas an optimistic decision might beat SAA, the improvement is very modest and comes at the cost of being much more sensitive to model misspecification than both the SAA and the DRO decisions. Moreover, it is easy to make a mistake: it can be difficult to determine with a modestly sized data set whether the best or worst case solution will have the higher expected reward than SAA. In summary, data driven optimization is a trade-off between maximizing the expected reward and controlling the sensitivity of this expectation to model misspecification. When both are considered, a little bit of pessimism goes a long way.
数据驱动的优化涉及找到一个决策，使用数据，也许是一个模型，当它应用于一个新的看不见的数据点时表现良好。数据驱动的优化是具有挑战性的，因为数据是有限的，或者模型是错误的，或者应用决策的环境与收集训练数据的环境不同。分布式鲁棒优化(DRO)是一种用于寻找对模型误差不敏感的决策的最坏情况优化方法，有时(但并不总是)可以提供比样本平均近似(SAA)具有更大样本外预期奖励的决策。由Jun-ya Gotoh, Michael Kim和Andrew Lim撰写的“打败样本外SAA的数据驱动方法”表明，如果最坏情况(DRO)解决方案在这项任务中失败，那么最佳情况下分布乐观优化问题的解决方案将完成这项任务。尽管这听起来很好，但有一个问题:尽管一个乐观的决定可能会击败SAA，但这种改进是非常温和的，而且代价是对模型错误规范的敏感程度要比SAA和DRO的决定高得多。此外，很容易犯错误:很难用中等规模的数据集确定最佳或最坏情况解决方案是否比SAA具有更高的预期回报。总之，数据驱动的优化是在最大化预期奖励和控制该期望对模型错误规范的敏感性之间的权衡。当考虑到这两点时，一点点悲观主义就会走得很远。
{"title":"A Data-Driven Approach to Beating SAA Out of Sample","authors":"Jun-ya Gotoh, Michael Jong Kim, Andrew E. B. Lim","doi":"10.1287/opre.2021.0393","DOIUrl":"https://doi.org/10.1287/opre.2021.0393","url":null,"abstract":"A Little Pessimism Goes a Long Way Data-driven optimization is concerned with finding a decision, using data and perhaps a model, that performs well when it is applied on a new unseen data point. Data-driven optimization is challenging because data are limited or the model is wrong or the environment in which the decision is being applied is different from the one in which the training data were collected. Distributionally robust optimization (DRO), a worst case optimization method for finding decisions that are insensitive to model error, can sometimes but not always deliver a decision that has a larger out-of-sample expected reward than the sample average approximation (SAA). “A Data Driven Approach to Beating SAA out of Sample” by Jun-ya Gotoh, Michael Kim, and Andrew Lim shows that if worst case (DRO) solutions fail at this task, then the solution of a best case distributionally optimistic optimization problem will do the job. As good as this sounds, there is a catch: whereas an optimistic decision might beat SAA, the improvement is very modest and comes at the cost of being much more sensitive to model misspecification than both the SAA and the DRO decisions. Moreover, it is easy to make a mistake: it can be difficult to determine with a modestly sized data set whether the best or worst case solution will have the higher expected reward than SAA. In summary, data driven optimization is a trade-off between maximizing the expected reward and controlling the sensitivity of this expectation to model misspecification. When both are considered, a little bit of pessimism goes a long way.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82901563","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}
M. Zhalechian, Esmaeil Keyvanshokooh, Cong Shi, M. P. Van Oyen
A Data-Driven Approach to Improve Care Unit Placements in Hospitals The choice of care unit upon hospital admission is a challenging task because of the wide variety of patient characteristics, uncertain needs of patients, and limited number of beds in intensive and intermediate care units. These decisions require carefully weighing the benefits of improved health outcomes against the opportunity cost of reserving higher level care beds for potentially more complex patients arriving in the future. In “Data-Driven Hospital Admission Control: A Learning Approach,” Zhalechian, Keyvanshokooh, Shi, and Van Oyen introduce a data-driven algorithm to address this challenging task. By focusing on reducing the readmission risk of patients, the algorithm is designed to (i) adaptively learn the readmission risk of patients through batch learning with delayed feedback and (ii) determine the best care unit placement for a patient based on the observed information and occupancy levels to minimize total readmission risk. The algorithm is supported by a performance guarantee, and its effectiveness is showcased using real-world hospital system data.
{"title":"Data-Driven Hospital Admission Control: A Learning Approach","authors":"M. Zhalechian, Esmaeil Keyvanshokooh, Cong Shi, M. P. Van Oyen","doi":"10.1287/opre.2020.0481","DOIUrl":"https://doi.org/10.1287/opre.2020.0481","url":null,"abstract":"A Data-Driven Approach to Improve Care Unit Placements in Hospitals The choice of care unit upon hospital admission is a challenging task because of the wide variety of patient characteristics, uncertain needs of patients, and limited number of beds in intensive and intermediate care units. These decisions require carefully weighing the benefits of improved health outcomes against the opportunity cost of reserving higher level care beds for potentially more complex patients arriving in the future. In “Data-Driven Hospital Admission Control: A Learning Approach,” Zhalechian, Keyvanshokooh, Shi, and Van Oyen introduce a data-driven algorithm to address this challenging task. By focusing on reducing the readmission risk of patients, the algorithm is designed to (i) adaptively learn the readmission risk of patients through batch learning with delayed feedback and (ii) determine the best care unit placement for a patient based on the observed information and occupancy levels to minimize total readmission risk. The algorithm is supported by a performance guarantee, and its effectiveness is showcased using real-world hospital system data.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"95 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73755800","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}
An unbiased forecast of profit is important in most business environments. Typically, forecasts are generated from data. However, in “Technical Note—Data-Driven Profit Estimation Error in the newsvendor model,” Siegel and Wagner identify a strictly positive bias in a natural estimation of expected profit in a data-driven newsvendor model, where managers will expect more profit than will actually be realized, on average. This bias can reach significant proportions (in some cases 50%+) of the true expected profit and could therefore have undesired and damaging effects in the real world. Siegel and Wagner then design a data-driven adjustment that results in an unbiased estimator of expected profit, so that managers may have an accurate forecast of future profit that is free of systematic bias.
{"title":"Technical Note—Data-Driven Profit Estimation Error in the Newsvendor Model","authors":"A. Siegel, Michael R. Wagner","doi":"10.1287/opre.2023.0070","DOIUrl":"https://doi.org/10.1287/opre.2023.0070","url":null,"abstract":"An unbiased forecast of profit is important in most business environments. Typically, forecasts are generated from data. However, in “Technical Note—Data-Driven Profit Estimation Error in the newsvendor model,” Siegel and Wagner identify a strictly positive bias in a natural estimation of expected profit in a data-driven newsvendor model, where managers will expect more profit than will actually be realized, on average. This bias can reach significant proportions (in some cases 50%+) of the true expected profit and could therefore have undesired and damaging effects in the real world. Siegel and Wagner then design a data-driven adjustment that results in an unbiased estimator of expected profit, so that managers may have an accurate forecast of future profit that is free of systematic bias.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"46 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80789494","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}
Efficient Learning Algorithms for Dynamic Inventory Allocation in Multiwarehouse Multistore Systems with Censored Demand Motivated by collaboration with a prominent fast-fashion retailer in Europe, the researchers focus their attention on the one-warehouse multistore (OWMS) inventory control problem, specifically addressing scenarios in which the demand distribution is unknown a priori. The OWMS problem revolves around a central warehouse that receives initial replenishments and subsequently distributes inventory to multiple stores within a finite time horizon. The objective lies in minimizing the total expected cost. To overcome the hurdles posed by the unknown demand distribution, the researchers propose a primal-dual algorithm that continuously learns from demand observations and dynamically adjusts inventory control decisions in real time. Thorough theoretical analysis and empirical evaluations highlight the promising performance of this approach, offering valuable insights for efficient inventory allocation within the ever-evolving retail industry.
{"title":"Inventory Control and Learning for One-Warehouse Multistore System with Censored Demand","authors":"Recep Yusuf Bekci, M. Gümüş, Sentao Miao","doi":"10.1287/opre.2021.0694","DOIUrl":"https://doi.org/10.1287/opre.2021.0694","url":null,"abstract":"Efficient Learning Algorithms for Dynamic Inventory Allocation in Multiwarehouse Multistore Systems with Censored Demand Motivated by collaboration with a prominent fast-fashion retailer in Europe, the researchers focus their attention on the one-warehouse multistore (OWMS) inventory control problem, specifically addressing scenarios in which the demand distribution is unknown a priori. The OWMS problem revolves around a central warehouse that receives initial replenishments and subsequently distributes inventory to multiple stores within a finite time horizon. The objective lies in minimizing the total expected cost. To overcome the hurdles posed by the unknown demand distribution, the researchers propose a primal-dual algorithm that continuously learns from demand observations and dynamically adjusts inventory control decisions in real time. Thorough theoretical analysis and empirical evaluations highlight the promising performance of this approach, offering valuable insights for efficient inventory allocation within the ever-evolving retail industry.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"21 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86952131","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}
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}
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