Murray Lei, Sheng Liu, Stefanus Jasin, A. Vakhutinsky
In “Joint Inventory and Pricing for a One-Warehouse Multistore Problem: Spiraling Phenomena, Near Optimal Policies, and the Value of Dynamic Pricing,” Lei, Liu, Jasin, and Vakhutinsky consider a joint inventory and pricing problem with one warehouse and multiple stores with lost sales. The retailer makes a one-time decision on the amount of inventory to be placed at the warehouse at the beginning of the selling season, followed by periodic joint replenishment and pricing decisions for each store throughout the season. The authors first analyze the performance of two popular and simple heuristic policies that directly implement the solution of a deterministic approximation of the original stochastic problem. They show that simple reoptimization of the deterministic approximation may worsen the performance by causing a “spiraling up” movement in expected lost sales quantity. The authors further propose two improved heuristic policies with provably near-optimal performance. In particular, the first policy achieves the best possible performance among all policies that rely on static pricing, and the second policy outperforms the first one because of its use of carefully designed dynamic pricing scheme.
{"title":"Joint Inventory and Pricing for a One-Warehouse Multistore Problem: Spiraling Phenomena, Near Optimal Policies, and the Value of Dynamic Pricing","authors":"Murray Lei, Sheng Liu, Stefanus Jasin, A. Vakhutinsky","doi":"10.1287/opre.2022.2389","DOIUrl":"https://doi.org/10.1287/opre.2022.2389","url":null,"abstract":"In “Joint Inventory and Pricing for a One-Warehouse Multistore Problem: Spiraling Phenomena, Near Optimal Policies, and the Value of Dynamic Pricing,” Lei, Liu, Jasin, and Vakhutinsky consider a joint inventory and pricing problem with one warehouse and multiple stores with lost sales. The retailer makes a one-time decision on the amount of inventory to be placed at the warehouse at the beginning of the selling season, followed by periodic joint replenishment and pricing decisions for each store throughout the season. The authors first analyze the performance of two popular and simple heuristic policies that directly implement the solution of a deterministic approximation of the original stochastic problem. They show that simple reoptimization of the deterministic approximation may worsen the performance by causing a “spiraling up” movement in expected lost sales quantity. The authors further propose two improved heuristic policies with provably near-optimal performance. In particular, the first policy achieves the best possible performance among all policies that rely on static pricing, and the second policy outperforms the first one because of its use of carefully designed dynamic pricing scheme.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"13 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83552945","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 “Identifying Merger Opportunities: The Case of Air Traffic Control,” N. Adler, O. Olesen, and N. Volta propose a model to identify an optimal horizontal merger configuration at the level of an industry or firm with multiple branches. Assuming that each firm operates within a catchment area or owns part of a network, we extend the model to consider feasible mergers that cover a contiguous area, should network effects be a consideration. An application to the European air traffic control system suggests that four contiguous air navigation service providers should replace the current 29 providers and the nine functional airspace blocks proposed in the Single European Skies initiative. The technological developments in air traffic management in which regulators on both sides of the Atlantic have invested heavily, namely SESAR and NextGen, are unlikely to be used without a concomitant reduction in operating costs through economies of scale. We find that the politically oriented solution may save around one third of current costs, but an optimal solution will save closer to 46%.
在“识别合并机会:空中交通管制的案例”中，N. Adler, O. Olesen和N. Volta提出了一个模型来识别具有多个分支的行业或公司层面的最佳横向合并配置。假设每个公司都在一个集水区经营或拥有一部分网络，我们扩展模型，考虑覆盖连续区域的可行合并，如果考虑到网络效应。欧洲空中交通管制系统的一项申请表明，四个连续的空中导航服务提供商应该取代目前的29个提供商和欧洲单一天空倡议中提出的9个功能空域块。大西洋两岸的监管机构在空中交通管理方面投入了大量资金的技术发展，即SESAR和NextGen，如果没有规模经济带来的运营成本降低，就不太可能得到应用。我们发现，以政治为导向的解决方案可能会节省约三分之一的当前成本，但最优解决方案将节省近46%。
{"title":"Identifying Merger Opportunities: The Case of Air Traffic Control","authors":"N. Adler, O. Olesen, N. Volta","doi":"10.1287/opre.2022.2348","DOIUrl":"https://doi.org/10.1287/opre.2022.2348","url":null,"abstract":"In “Identifying Merger Opportunities: The Case of Air Traffic Control,” N. Adler, O. Olesen, and N. Volta propose a model to identify an optimal horizontal merger configuration at the level of an industry or firm with multiple branches. Assuming that each firm operates within a catchment area or owns part of a network, we extend the model to consider feasible mergers that cover a contiguous area, should network effects be a consideration. An application to the European air traffic control system suggests that four contiguous air navigation service providers should replace the current 29 providers and the nine functional airspace blocks proposed in the Single European Skies initiative. The technological developments in air traffic management in which regulators on both sides of the Atlantic have invested heavily, namely SESAR and NextGen, are unlikely to be used without a concomitant reduction in operating costs through economies of scale. We find that the politically oriented solution may save around one third of current costs, but an optimal solution will save closer to 46%.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"9 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87269992","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}
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}
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