Adaptive Patient Flow Management Appointment scheduling has significant clinical, operational, and economical impact on healthcare systems. An informed scheduling strategy that can effectively match patient demand and service capacity dynamically is vital for the business of medical providers, quality of care, and patient satisfaction. By regulating patient flow via an appointment system, healthcare providers can mitigate arrival process variability and improve operational performance. The simultaneous consideration of appointment day (interday scheduling) and time of day (intraday scheduling) in dynamic scheduling decisions is an important theoretical and practical problem that has remained open because of its stochastic nature, complex structure, and large dimensionality. Zacharias et al. (2022) fill this critical gap in the literature. They introduce a novel dynamic programming framework, designed with the intention of bridging two independently established streams of literature, and to leverage their latest advances in tackling the joint problem. They advance the theory of the field to provide a rigorous and practically implantable solution.
{"title":"Dynamic Interday and Intraday Scheduling","authors":"Christos Zacharias, Nan Liu, Mehmet A. Begen","doi":"10.1287/opre.2022.2342","DOIUrl":"https://doi.org/10.1287/opre.2022.2342","url":null,"abstract":"Adaptive Patient Flow Management Appointment scheduling has significant clinical, operational, and economical impact on healthcare systems. An informed scheduling strategy that can effectively match patient demand and service capacity dynamically is vital for the business of medical providers, quality of care, and patient satisfaction. By regulating patient flow via an appointment system, healthcare providers can mitigate arrival process variability and improve operational performance. The simultaneous consideration of appointment day (interday scheduling) and time of day (intraday scheduling) in dynamic scheduling decisions is an important theoretical and practical problem that has remained open because of its stochastic nature, complex structure, and large dimensionality. Zacharias et al. (2022) fill this critical gap in the literature. They introduce a novel dynamic programming framework, designed with the intention of bridging two independently established streams of literature, and to leverage their latest advances in tackling the joint problem. They advance the theory of the field to provide a rigorous and practically implantable solution.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"33 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84620052","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}
Ranking and selection (R&S) procedures in simulation optimization simulate every feasible solution to provide global statistical error control, often selecting a single solution in finite time that is optimal or near-optimal with high probability. By exploiting parallel computing advancements, large-scale problems with hundreds of thousands and even millions of feasible solutions are suitable for R&S. Naively parallelizing existing R&S methods originally designed for a serial computing setting is generally ineffective, however, as many of these conventional methods uphold family-wise error guarantees that suffer from multiplicity and require pairwise comparisons that present a computational bottleneck. Parallel adaptive survivor selection (PASS) is a new framework specifically designed for large-scale parallel R&S. By comparing systems to an adaptive “standard” that is learned as the algorithm progresses, PASS eliminates inferior solutions with false elimination rate control and with computationally efficient aggregate comparisons rather than pairwise comparisons. PASS satisfies desirable theoretical properties and performs effectively on realistic problems.
{"title":"Parallel Adaptive Survivor Selection","authors":"Linda Pei, Barry L. Nelson, Susan R. Hunter","doi":"10.1287/opre.2022.2343","DOIUrl":"https://doi.org/10.1287/opre.2022.2343","url":null,"abstract":"Ranking and selection (R&S) procedures in simulation optimization simulate every feasible solution to provide global statistical error control, often selecting a single solution in finite time that is optimal or near-optimal with high probability. By exploiting parallel computing advancements, large-scale problems with hundreds of thousands and even millions of feasible solutions are suitable for R&S. Naively parallelizing existing R&S methods originally designed for a serial computing setting is generally ineffective, however, as many of these conventional methods uphold family-wise error guarantees that suffer from multiplicity and require pairwise comparisons that present a computational bottleneck. Parallel adaptive survivor selection (PASS) is a new framework specifically designed for large-scale parallel R&S. By comparing systems to an adaptive “standard” that is learned as the algorithm progresses, PASS eliminates inferior solutions with false elimination rate control and with computationally efficient aggregate comparisons rather than pairwise comparisons. PASS satisfies desirable theoretical properties and performs effectively on realistic problems.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"79 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89771827","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}
Ranking algorithms play a crucial role in information technologies and numerical analysis due to their efficiency in high dimensions and wide range of possible applications, including internet ranking, scientometrics, and systemic risk in finance (SinkRank and DebtRank). The traditional approach to internet ranking goes back to the seminal work of Sergey Brin and Larry Page, who developed the initial method PageRank (PR) in order to rank websites for search engine results based on linear algebra rules. But how robust is this method in times of rapid internet growth? Recent works have studied robust reformulations of the PageRank model for the case when links in the network structure may vary; that is, some links may appear or disappear, influencing the transportation matrix defined by the network structure. In this article, the authors make a further step forward, allowing the network to vary not only in links but also in the number of nodes. The authors focus on growing network structures and develop methods for ranking of networks uncertain both in size and in structure.
{"title":"Information Retrieval Under Network Uncertainty: Robust Internet Ranking","authors":"Anna Timonina-Farkas, Ralf W. Seifert","doi":"10.1287/opre.2022.2298","DOIUrl":"https://doi.org/10.1287/opre.2022.2298","url":null,"abstract":"Ranking algorithms play a crucial role in information technologies and numerical analysis due to their efficiency in high dimensions and wide range of possible applications, including internet ranking, scientometrics, and systemic risk in finance (SinkRank and DebtRank). The traditional approach to internet ranking goes back to the seminal work of Sergey Brin and Larry Page, who developed the initial method PageRank (PR) in order to rank websites for search engine results based on linear algebra rules. But how robust is this method in times of rapid internet growth? Recent works have studied robust reformulations of the PageRank model for the case when links in the network structure may vary; that is, some links may appear or disappear, influencing the transportation matrix defined by the network structure. In this article, the authors make a further step forward, allowing the network to vary not only in links but also in the number of nodes. The authors focus on growing network structures and develop methods for ranking of networks uncertain both in size and in structure.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"34 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77498619","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}
The paper by Han, Bandi, and Nohadani on “On Finite Adaptability in Two-Stage Distributionally Robust Optimization” studies finite adaptability with the goal to construct interpretable and easily implementable policies in the context of two-stage distributionally robust optimization problems. To achieve this, the set of uncertainty realizations needs to be partitioned. The authors show that an optimal partitioning can be accomplished via “translated orthants.” They then propose a nondecreasing orthant partitioning and binary approximation to obtain the corresponding “orthant-based policies” from a mixed-integer optimization problem of a moderate size. For these policies, they provide provable performance bounds, generalizing the existing bounds in the literature. For more general settings, they also propose optimization formulations to obtain posterior lower bounds that can serve to evaluate performance. Two numerical experiments support these findings. A joint inventory-routing problem highlights the practical applicability for large-sized instances with mixed-integer recourse. A case study from a pharmacy retailer demonstrates that the orthant-based policies are less sensitive to cost parameters than optimal solutions, enabling these policies to outperform comparable methods when the realized cost ratio deviates from its nominal value.
Han、Bandi和Nohadani在“on Finite adaptive in Two-Stage distribution鲁棒优化”一文中研究了有限适应性，目的是在两阶段分布鲁棒优化问题中构建可解释且易于实现的策略。要实现这一点，需要对不确定性实现集进行划分。作者证明了一个最优的划分可以通过“翻译正交”来完成。然后，他们提出了一个非递减正交分区和二元逼近，从一个中等规模的混合整数优化问题中获得相应的“基于正交的策略”。对于这些策略，他们提供了可证明的性能界限，推广了文献中现有的界限。对于更一般的设置，他们还提出了优化公式，以获得可用于评估性能的后验下界。两个数值实验支持这些发现。联合库存路由问题突出了具有混合整数追索权的大型实例的实际适用性。一个来自药房零售商的案例研究表明，与最优解决方案相比，基于orthant的策略对成本参数的敏感性较低，因此当实现的成本比率偏离其标称值时，这些策略的性能优于可比方法。
{"title":"On Finite Adaptability in Two-Stage Distributionally Robust Optimization","authors":"Eojin Han, Chaithanya Bandi, O. Nohadani","doi":"10.1287/opre.2022.2273","DOIUrl":"https://doi.org/10.1287/opre.2022.2273","url":null,"abstract":"The paper by Han, Bandi, and Nohadani on “On Finite Adaptability in Two-Stage Distributionally Robust Optimization” studies finite adaptability with the goal to construct interpretable and easily implementable policies in the context of two-stage distributionally robust optimization problems. To achieve this, the set of uncertainty realizations needs to be partitioned. The authors show that an optimal partitioning can be accomplished via “translated orthants.” They then propose a nondecreasing orthant partitioning and binary approximation to obtain the corresponding “orthant-based policies” from a mixed-integer optimization problem of a moderate size. For these policies, they provide provable performance bounds, generalizing the existing bounds in the literature. For more general settings, they also propose optimization formulations to obtain posterior lower bounds that can serve to evaluate performance. Two numerical experiments support these findings. A joint inventory-routing problem highlights the practical applicability for large-sized instances with mixed-integer recourse. A case study from a pharmacy retailer demonstrates that the orthant-based policies are less sensitive to cost parameters than optimal solutions, enabling these policies to outperform comparable methods when the realized cost ratio deviates from its nominal value.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"4 9 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74126932","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}
Despite the successful applications of decision diagrams (DDs) to solve various classes of integer programs in the literature, the question of which mixed-integer structures admit a DD representation remains open. The present work addresses this question by developing both necessary and sufficient conditions for a mixed-integer program to be DD-representable through identification of certain rectangular formations in the underlying sets. This so-called rectangularization framework is applicable to all bounded mixed-integer linear programs, providing a notable extension of the DD domain to continuous problems. As an application, the paper uses the developed methods to solve stochastic unit commitment problems in energy systems. Computational experiments conducted on benchmark instances show that the DD approach uniformly and significantly outperforms the existing solution methods and modern solvers. The proposed methodology opens new pathways to solving challenging mixed-integer programs in energy systems more efficiently.
{"title":"On the Structure of Decision Diagram–Representable Mixed-Integer Programs with Application to Unit Commitment","authors":"Hosseinali Salemi, D. Davarnia","doi":"10.1287/opre.2022.2353","DOIUrl":"https://doi.org/10.1287/opre.2022.2353","url":null,"abstract":"Despite the successful applications of decision diagrams (DDs) to solve various classes of integer programs in the literature, the question of which mixed-integer structures admit a DD representation remains open. The present work addresses this question by developing both necessary and sufficient conditions for a mixed-integer program to be DD-representable through identification of certain rectangular formations in the underlying sets. This so-called rectangularization framework is applicable to all bounded mixed-integer linear programs, providing a notable extension of the DD domain to continuous problems. As an application, the paper uses the developed methods to solve stochastic unit commitment problems in energy systems. Computational experiments conducted on benchmark instances show that the DD approach uniformly and significantly outperforms the existing solution methods and modern solvers. The proposed methodology opens new pathways to solving challenging mixed-integer programs in energy systems more efficiently.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"2011 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82580386","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}
The problem of simulating graphs (networks) subject to constraints has been studied extensively across several areas. Applications of this problem include modeling inter-bank financial networks, predator-prey ecological graphs, contingency tables, and even studying larger networks such as the Internet. In “Maximum Entropy Distributions with Applications to Graph Simulation,” P. Glasserman and E. Lelo de Larrea study the more general problem of sampling uniformly from product sets under linear constraints, which includes simulating bipartite, directed, and undirected graphs with given degree sequences. For this purpose, they consider two suitable probability distributions: one that maximizes the entropy of the system, and another that maximizes the minimum probability of hitting the desired target set. Although apparently different, the authors provide conditions under which both distributions coincide. In addition, they propose a simple sequential algorithm to sample medium-sized graphs with fixed degrees.
模拟受约束的图(网络)的问题已经在多个领域得到了广泛的研究。这个问题的应用包括银行间金融网络的建模，捕食者-猎物生态图，列联表，甚至研究更大的网络，如互联网。在“最大熵分布及其在图模拟中的应用”一文中，P. Glasserman和E. Lelo de Larrea研究了在线性约束下从积集中均匀抽样的更一般的问题，其中包括模拟具有给定度序列的二部图、有向图和无向图。为此，他们考虑了两种合适的概率分布:一种是最大化系统的熵，另一种是最大化达到预期目标集的最小概率。尽管明显不同，但作者提供了两种分布一致的条件。此外，他们提出了一种简单的顺序算法来采样固定度的中等大小的图。
{"title":"Maximum Entropy Distributions with Applications to Graph Simulation","authors":"P. Glasserman, Enrique Lelo de Larrea","doi":"10.1287/opre.2022.2323","DOIUrl":"https://doi.org/10.1287/opre.2022.2323","url":null,"abstract":"The problem of simulating graphs (networks) subject to constraints has been studied extensively across several areas. Applications of this problem include modeling inter-bank financial networks, predator-prey ecological graphs, contingency tables, and even studying larger networks such as the Internet. In “Maximum Entropy Distributions with Applications to Graph Simulation,” P. Glasserman and E. Lelo de Larrea study the more general problem of sampling uniformly from product sets under linear constraints, which includes simulating bipartite, directed, and undirected graphs with given degree sequences. For this purpose, they consider two suitable probability distributions: one that maximizes the entropy of the system, and another that maximizes the minimum probability of hitting the desired target set. Although apparently different, the authors provide conditions under which both distributions coincide. In addition, they propose a simple sequential algorithm to sample medium-sized graphs with fixed degrees.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"PP 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84353206","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}
On online platforms, goods, services, and content providers, also known as agents, introduce adverse events. The frequency of these events depends on each agent’s effort level. In “Efficient Resource Allocation Contracts to Reduce Adverse Events,” Liang, Sun, Tang, and Zhang study continuous-time dynamic contracts that utilize resource allocation and monetary transfers to induce agents to exert effort and reduce the arrival rate of adverse events. They devise an iterative algorithm that characterizes and calculates such contracts and specify the profit-maximizing contract for the platform, also known as the principal. In contrast to the single-agent case, in which efficiency is not achievable, they show that efficient and incentive-compatible contracts, which allocate all resources and induce agents to exert constant effort, generally exist with two or more agents. Additionally, they also provide efficient and incentive-compatible dynamic contracts that can be expressed in closed form and are therefore easy to understand and implement in practice.
{"title":"Efficient Resource Allocation Contracts to Reduce Adverse Events","authors":"Yong Liang, Peng Sun, Runyu Tang, Chong Zhang","doi":"10.1287/opre.2022.2322","DOIUrl":"https://doi.org/10.1287/opre.2022.2322","url":null,"abstract":"On online platforms, goods, services, and content providers, also known as agents, introduce adverse events. The frequency of these events depends on each agent’s effort level. In “Efficient Resource Allocation Contracts to Reduce Adverse Events,” Liang, Sun, Tang, and Zhang study continuous-time dynamic contracts that utilize resource allocation and monetary transfers to induce agents to exert effort and reduce the arrival rate of adverse events. They devise an iterative algorithm that characterizes and calculates such contracts and specify the profit-maximizing contract for the platform, also known as the principal. In contrast to the single-agent case, in which efficiency is not achievable, they show that efficient and incentive-compatible contracts, which allocate all resources and induce agents to exert constant effort, generally exist with two or more agents. Additionally, they also provide efficient and incentive-compatible dynamic contracts that can be expressed in closed form and are therefore easy to understand and implement in practice.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"19 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85666979","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}
I. Aravena, D. Molzahn, Shixu Zhang, C. Petra, Frank E. Curtis, Shenyinying Tu, Andreas Wächter, Ermin Wei, Elizabeth Wong, A. Gholami, Kaizhao Sun, X. Sun, S. Elbert, Jesse T. Holzer, A. Veeramany
Intro to the ARPA-E Grid Optimization Competition In “Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition,” we review the state of the art in practical algorithms for scheduling power-systems operations in the short term and the results of the recent competition organized by the U.S. Advanced Research Projects Agency–Energy. We explain the mixed-integer nonlinear formulation used in the competition for nonspecialists in electrical engineering, the context and organization of the competition, and the performance of competitors. We find that the collective approaches and results of competitors provide support for efforts to move nonlinear optimization techniques into industrial applications, as they have proven to be a robust and efficient alternative to current linear approximation techniques.
{"title":"Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition","authors":"I. Aravena, D. Molzahn, Shixu Zhang, C. Petra, Frank E. Curtis, Shenyinying Tu, Andreas Wächter, Ermin Wei, Elizabeth Wong, A. Gholami, Kaizhao Sun, X. Sun, S. Elbert, Jesse T. Holzer, A. Veeramany","doi":"10.1287/opre.2022.0315","DOIUrl":"https://doi.org/10.1287/opre.2022.0315","url":null,"abstract":"Intro to the ARPA-E Grid Optimization Competition In “Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition,” we review the state of the art in practical algorithms for scheduling power-systems operations in the short term and the results of the recent competition organized by the U.S. Advanced Research Projects Agency–Energy. We explain the mixed-integer nonlinear formulation used in the competition for nonspecialists in electrical engineering, the context and organization of the competition, and the performance of competitors. We find that the collective approaches and results of competitors provide support for efforts to move nonlinear optimization techniques into industrial applications, as they have proven to be a robust and efficient alternative to current linear approximation techniques.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"77 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84964993","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 many real-life situations, the inventory record may not match the actual stock perfectly. This can happen due to distortion of inventory data, such as transaction errors, misplaced inventories, and spoilage. In these cases, because the decision maker only has incomplete information about the inventory levels, many well-known inventory policies are not even admissible, and our understanding of the optimal policies, even their existence, is very limited. In “Average Cost Optimality in Partially Observable Lost-Sales Inventory Systems,” Bai et al. consider the classical lost-sales inventory model, in which the inventory level is only observed when it becomes zero. They formulate the cost-minimization problem as a partially observable Markov decision process. By exploiting the vanishing discount factor approach, they provide a way to verify the existence of optimal policies under the average cost criterion. The key step in their analysis is the construction of a valid policy, which, in a certain sense, copies the actions of another policy for the process starting from another initial state.
{"title":"Technical Note—Average Cost Optimality in Partially Observable Lost-Sales Inventory Systems","authors":"Xingyu Bai, X. Chen, A. Stolyar","doi":"10.1287/opre.2022.2305","DOIUrl":"https://doi.org/10.1287/opre.2022.2305","url":null,"abstract":"In many real-life situations, the inventory record may not match the actual stock perfectly. This can happen due to distortion of inventory data, such as transaction errors, misplaced inventories, and spoilage. In these cases, because the decision maker only has incomplete information about the inventory levels, many well-known inventory policies are not even admissible, and our understanding of the optimal policies, even their existence, is very limited. In “Average Cost Optimality in Partially Observable Lost-Sales Inventory Systems,” Bai et al. consider the classical lost-sales inventory model, in which the inventory level is only observed when it becomes zero. They formulate the cost-minimization problem as a partially observable Markov decision process. By exploiting the vanishing discount factor approach, they provide a way to verify the existence of optimal policies under the average cost criterion. The key step in their analysis is the construction of a valid policy, which, in a certain sense, copies the actions of another policy for the process starting from another initial state.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"50 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85089379","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 “Dual Approach for Two-Stage Robust Nonlinear Optimization,” de Ruiter, Zhen, and den Hertog study adjustable robust minimization problems where the objective or constraints depend in a convex way on the adjustable variables. They reformulate the original adjustable robust nonlinear problem with a polyhedral uncertainty set into an equivalent adjustable robust linear problem, for which all existing approaches for adjustable robust linear problems can be used. The reformulation is obtained by first dualizing over the adjustable variables and then over the uncertain parameters. The polyhedral structure of the uncertainty set then appears in the linear constraints of the dualized problem, and the nonlinear functions of the adjustable variables in the original problem appear in the uncertainty set of the dualized problem. The authors show how to recover linear decision rules to the original primal problem and how to generate bounds on its optimal objective value.
{"title":"Dual Approach for Two-Stage Robust Nonlinear Optimization","authors":"F.J.C.T. de Ruiter, Jianzhe Zhen, D. den Hertog","doi":"10.1287/opre.2022.2289","DOIUrl":"https://doi.org/10.1287/opre.2022.2289","url":null,"abstract":"In “Dual Approach for Two-Stage Robust Nonlinear Optimization,” de Ruiter, Zhen, and den Hertog study adjustable robust minimization problems where the objective or constraints depend in a convex way on the adjustable variables. They reformulate the original adjustable robust nonlinear problem with a polyhedral uncertainty set into an equivalent adjustable robust linear problem, for which all existing approaches for adjustable robust linear problems can be used. The reformulation is obtained by first dualizing over the adjustable variables and then over the uncertain parameters. The polyhedral structure of the uncertainty set then appears in the linear constraints of the dualized problem, and the nonlinear functions of the adjustable variables in the original problem appear in the uncertainty set of the dualized problem. The authors show how to recover linear decision rules to the original primal problem and how to generate bounds on its optimal objective value.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89256328","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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