Gerrymandering has been a fundamental issue in American democracy for more than two centuries, with significant implications for electoral representation. Traditional optimization models for political districting primarily model nonpolitical fairness metrics such as the compactness of districts. In “Multiobjective Optimization for Politically Fair Districting: A Scalable Multilevel Approach,” Swamy, King, and Jacobson propose optimization models that explicitly incorporate political fairness objectives using political data from past elections. These objectives model fundamental fairness principles such as vote-seat proportionality (efficiency gap), partisan (a)symmetry, and competitiveness. They propose a solution strategy, called the multilevel algorithm, that solves large instances of the problem using a series of matching-based graph contractions. A case study on congressional districting in Wisconsin demonstrates that district plans balance the interests of the voters and the political parties.
{"title":"Multiobjective Optimization for Politically Fair Districting: A Scalable Multilevel Approach","authors":"Rahul Swamy, D. King, S. Jacobson","doi":"10.1287/opre.2022.2311","DOIUrl":"https://doi.org/10.1287/opre.2022.2311","url":null,"abstract":"Gerrymandering has been a fundamental issue in American democracy for more than two centuries, with significant implications for electoral representation. Traditional optimization models for political districting primarily model nonpolitical fairness metrics such as the compactness of districts. In “Multiobjective Optimization for Politically Fair Districting: A Scalable Multilevel Approach,” Swamy, King, and Jacobson propose optimization models that explicitly incorporate political fairness objectives using political data from past elections. These objectives model fundamental fairness principles such as vote-seat proportionality (efficiency gap), partisan (a)symmetry, and competitiveness. They propose a solution strategy, called the multilevel algorithm, that solves large instances of the problem using a series of matching-based graph contractions. A case study on congressional districting in Wisconsin demonstrates that district plans balance the interests of the voters and the political parties.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"32 1","pages":"536-562"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76126535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online multiproduct sellers increasingly use interactive selling strategies to customize their offers to individual buyers. For example, a seller may adjust the prices of products dynamically based on user interaction and offer discounts for buying bundles of products. What selling strategy should such a seller use to maximize profit? In “Sequential Mechanisms with ex Post Individual Rationality,” I. Ashlagi, C. Daskalakis, and N. Haghpanah provide a recursive characterization of the optimal selling strategy. This characterization is used to identify conditions under which the ability to bundle products is less profitable for the seller than the ability to adjust prices dynamically.
{"title":"Sequential Mechanisms with Ex Post Individual Rationality","authors":"I. Ashlagi, C. Daskalakis, Nima Haghpanah","doi":"10.1287/opre.2022.2332","DOIUrl":"https://doi.org/10.1287/opre.2022.2332","url":null,"abstract":"Online multiproduct sellers increasingly use interactive selling strategies to customize their offers to individual buyers. For example, a seller may adjust the prices of products dynamically based on user interaction and offer discounts for buying bundles of products. What selling strategy should such a seller use to maximize profit? In “Sequential Mechanisms with ex Post Individual Rationality,” I. Ashlagi, C. Daskalakis, and N. Haghpanah provide a recursive characterization of the optimal selling strategy. This characterization is used to identify conditions under which the ability to bundle products is less profitable for the seller than the ability to adjust prices dynamically.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"255 1","pages":"245-258"},"PeriodicalIF":0.0,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76150219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Feizi, Anita Carson, Jillian A. Berry Jaeker, W. Baker
Trade-offs Caused by Batching Inpatient Admissions from Emergency Departments In “To Batch or Not to Batch? Impact of Admission Batching on Emergency Department Boarding Time and Physician Productivity,” Feizi and coauthors tackle the important problem of identifying causes of emergency department (ED) boarding with the goal of identifying a managerial lever to reduce it. They investigate the impact of batching admissions of ED patients. The authors empirically show that batching occurs frequently at the end of shifts when physicians wrap up their tasks. Interestingly, Feizi et al. find a trade-off. Batching improves individual physician productivity, which explains its prevalence. However, it increases boarding times, an outcome that negatively impacts patients and the hospital. A counterfactual analysis comparing empirical results to theoretical queuing models finds that eliminating batching reduces boarding times by 15%. The paper highlights that boarding can be reduced by physicians completing admissions work as it occurs rather than delaying to the end of shift.
{"title":"To Batch or Not to Batch? Impact of Admission Batching on Emergency Department Boarding Time and Physician Productivity","authors":"A. Feizi, Anita Carson, Jillian A. Berry Jaeker, W. Baker","doi":"10.1287/opre.2022.2335","DOIUrl":"https://doi.org/10.1287/opre.2022.2335","url":null,"abstract":"Trade-offs Caused by Batching Inpatient Admissions from Emergency Departments In “To Batch or Not to Batch? Impact of Admission Batching on Emergency Department Boarding Time and Physician Productivity,” Feizi and coauthors tackle the important problem of identifying causes of emergency department (ED) boarding with the goal of identifying a managerial lever to reduce it. They investigate the impact of batching admissions of ED patients. The authors empirically show that batching occurs frequently at the end of shifts when physicians wrap up their tasks. Interestingly, Feizi et al. find a trade-off. Batching improves individual physician productivity, which explains its prevalence. However, it increases boarding times, an outcome that negatively impacts patients and the hospital. A counterfactual analysis comparing empirical results to theoretical queuing models finds that eliminating batching reduces boarding times by 15%. The paper highlights that boarding can be reduced by physicians completing admissions work as it occurs rather than delaying to the end of shift.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"192 1","pages":"939-957"},"PeriodicalIF":0.0,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76950378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A Novel and Promising Approximation for Network Revenue Management In “Product-Based Approximate Linear Programs for Network Revenue Management,” Zhang, Samiedaluie, and Zhang propose a novel separable piecewise linear (SPL) approximation for the network revenue management problem. The coefficients of the proposed SPL approximation can be interpreted as each product’s revenue contribution to the value of each resource in a given period, which provides more granular information compared with the existing resource-based SPL approximation in the literature. The new approximation provides more flexibility for policy construction. Furthermore, the new approximation opens the opportunity to derive a set of valid inequalities to further improve the computational performance and achieve additional gains in the expected revenue. Computational experiments with instances of various network structures and parameters demonstrate its efficacy: the new approximation leads to bid-price policies generating higher expected revenues and demonstrates better performance in terms of both computational efficiency and numerical stability.
{"title":"Technical Note - Product-Based Approximate Linear Programs for Network Revenue Management","authors":"Rui Zhang, S. Samiedaluie, Dan Zhang","doi":"10.1287/opre.2022.2354","DOIUrl":"https://doi.org/10.1287/opre.2022.2354","url":null,"abstract":"A Novel and Promising Approximation for Network Revenue Management In “Product-Based Approximate Linear Programs for Network Revenue Management,” Zhang, Samiedaluie, and Zhang propose a novel separable piecewise linear (SPL) approximation for the network revenue management problem. The coefficients of the proposed SPL approximation can be interpreted as each product’s revenue contribution to the value of each resource in a given period, which provides more granular information compared with the existing resource-based SPL approximation in the literature. The new approximation provides more flexibility for policy construction. Furthermore, the new approximation opens the opportunity to derive a set of valid inequalities to further improve the computational performance and achieve additional gains in the expected revenue. Computational experiments with instances of various network structures and parameters demonstrate its efficacy: the new approximation leads to bid-price policies generating higher expected revenues and demonstrates better performance in terms of both computational efficiency and numerical stability.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"1 1","pages":"2837-2850"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76626386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accelerated Algorithms for Ranking Assigning ranking scores to items based on observed comparison data (e.g., paired comparisons, choice, and full ranking outcomes) has been of continued interest in a wide range of applications, including information search, aggregation of social opinions, electronic commerce, online gaming platforms, and more recently, evaluation of machine learning algorithms. The key problem is to compute ranking scores, which are of interest for quantifying the strength of skills, relevancies, or preferences, and prediction of ranking outcomes. One of the most popular statistical models of ranking outcomes is the Bradley–Terry model for paired comparisons and its extensions to choice and full ranking outcomes. In “Accelerated MM Algorithms for Inference of Ranking Scores from Comparison Data,” M. Vojnovic, S.-Y. Yun, and K. Zhou show that a popular MM algorithm for inference of ranking scores for generalized Bradley–Terry ranking models suffers a slow convergence issue, and they propose a new accelerated algorithm that resolves this shortcoming and can yield substantial convergence speedups.
{"title":"Accelerated MM Algorithms for Inference of Ranking Scores from Comparison Data","authors":"M. Vojnović, Se-Young Yun, Kaifang Zhou","doi":"10.1287/opre.2022.2264","DOIUrl":"https://doi.org/10.1287/opre.2022.2264","url":null,"abstract":"Accelerated Algorithms for Ranking Assigning ranking scores to items based on observed comparison data (e.g., paired comparisons, choice, and full ranking outcomes) has been of continued interest in a wide range of applications, including information search, aggregation of social opinions, electronic commerce, online gaming platforms, and more recently, evaluation of machine learning algorithms. The key problem is to compute ranking scores, which are of interest for quantifying the strength of skills, relevancies, or preferences, and prediction of ranking outcomes. One of the most popular statistical models of ranking outcomes is the Bradley–Terry model for paired comparisons and its extensions to choice and full ranking outcomes. In “Accelerated MM Algorithms for Inference of Ranking Scores from Comparison Data,” M. Vojnovic, S.-Y. Yun, and K. Zhou show that a popular MM algorithm for inference of ranking scores for generalized Bradley–Terry ranking models suffers a slow convergence issue, and they propose a new accelerated algorithm that resolves this shortcoming and can yield substantial convergence speedups.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"13 1","pages":"1318-1342"},"PeriodicalIF":0.0,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84800480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A General Elliptical Potential Lemma In sequential learning and decision-making problems, the elliptical potential lemma is a key technique to quantify the decrease in the uncertainty of the model as more observations are obtained. However, it requires the observation noise and prior distribution of the unknown parameters to be Gaussian. In “The Elliptical Potential Lemma for General Distributions with an Application to Linear Thompson Sampling,” N. Hamidi and M. Bayati introduce a general version of the elliptical potential lemma that relaxes the Gaussian assumption. They also apply their general lemma to prove a minimax optimal Bayesian regret bound for the well-known Thompson sampling algorithm in stochastic linear bandits with changing action sets where prior and noise distributions are general.
{"title":"Technical Note - The Elliptical Potential Lemma for General Distributions with an Application to Linear Thompson Sampling","authors":"N. Hamidi, M. Bayati","doi":"10.1287/opre.2022.2274","DOIUrl":"https://doi.org/10.1287/opre.2022.2274","url":null,"abstract":"A General Elliptical Potential Lemma In sequential learning and decision-making problems, the elliptical potential lemma is a key technique to quantify the decrease in the uncertainty of the model as more observations are obtained. However, it requires the observation noise and prior distribution of the unknown parameters to be Gaussian. In “The Elliptical Potential Lemma for General Distributions with an Application to Linear Thompson Sampling,” N. Hamidi and M. Bayati introduce a general version of the elliptical potential lemma that relaxes the Gaussian assumption. They also apply their general lemma to prove a minimax optimal Bayesian regret bound for the well-known Thompson sampling algorithm in stochastic linear bandits with changing action sets where prior and noise distributions are general.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"1 1","pages":"1434-1439"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83026901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In many service systems, customers from different classes may have very different resource requirements. These differences include not only the duration of the service but also the units of resource required. This is especially prominent in healthcare settings, where patients with different severity levels can require a different level of medical attention/supervision translating into varying demands of nurse staffing capacity. Motivated by these applications, in “Managing Queues with Different Resource Requirements,” Zychlinski, Chan, and Dong study the optimal scheduling policy of a multiserver queue in which customers from different classes may require different units of servers. When customers have heterogeneous resource requirements, in addition to the cost of waiting and resource requirement, we also need to take policy-induced idleness into account. A good policy needs to carefully balance the myopic cost reduction rate and the more forward-looking system throughput. An index-based policy, referred to as the idle-avoid [Formula: see text] rule, is developed to balance these factors. Theoretical analysis along with numerical experiments provide support for good and robust performance of the proposed policy.
{"title":"Managing Queues with Different Resource Requirements","authors":"Noa Zychlinski, Carri W. Chan, Jing Dong","doi":"10.1287/opre.2022.2284","DOIUrl":"https://doi.org/10.1287/opre.2022.2284","url":null,"abstract":"In many service systems, customers from different classes may have very different resource requirements. These differences include not only the duration of the service but also the units of resource required. This is especially prominent in healthcare settings, where patients with different severity levels can require a different level of medical attention/supervision translating into varying demands of nurse staffing capacity. Motivated by these applications, in “Managing Queues with Different Resource Requirements,” Zychlinski, Chan, and Dong study the optimal scheduling policy of a multiserver queue in which customers from different classes may require different units of servers. When customers have heterogeneous resource requirements, in addition to the cost of waiting and resource requirement, we also need to take policy-induced idleness into account. A good policy needs to carefully balance the myopic cost reduction rate and the more forward-looking system throughput. An index-based policy, referred to as the idle-avoid [Formula: see text] rule, is developed to balance these factors. Theoretical analysis along with numerical experiments provide support for good and robust performance of the proposed policy.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"99 1","pages":"1387-1413"},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84011480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
What is the revenue prediction of a seller in selling digital goods when the seller does not have full information about the extent of externality of users? In “Technical Note—Revenue Volatility Under Uncertain Network Effects,” Baron, Hu, and Malekian address this question by characterizing how the seller’s revenue prediction depends on the underlying network structure and the available information to the seller.
{"title":"Technical Note - Revenue Volatility Under Uncertain Network Effects","authors":"Opher Baron, Ming-Che Hu, Azarakhsh Malekian","doi":"10.1287/opre.2022.2302","DOIUrl":"https://doi.org/10.1287/opre.2022.2302","url":null,"abstract":"What is the revenue prediction of a seller in selling digital goods when the seller does not have full information about the extent of externality of users? In “Technical Note—Revenue Volatility Under Uncertain Network Effects,” Baron, Hu, and Malekian address this question by characterizing how the seller’s revenue prediction depends on the underlying network structure and the available information to the seller.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"17 1","pages":"2254-2263"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73204445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Integer packing problems have traditionally been some of the most fundamental and well-studied computational questions in discrete optimization. The paper by Aouad and Segev studies the incremental knapsack problem, where one wishes to sequentially pack items into a knapsack whose capacity expands over a finite planning horizon, with the objective of maximizing time-averaged profits. Although various approximation algorithms were developed under mitigating structural assumptions, obtaining nontrivial performance guarantees for this problem in its utmost generality has remained an open question thus far. The authors devise the first polynomial-time approximation scheme for general instances of the incremental knapsack problem, which is the strongest guarantee possible given existing hardness results. Their approach synthesizes various techniques related to approximate dynamic programming, including problem decompositions, counting arguments, and efficient rounding methods, which may be of broader interest.
{"title":"Technical Note - An Approximate Dynamic Programming Approach to the Incremental Knapsack Problem","authors":"A. Aouad, D. Segev","doi":"10.1287/opre.2022.2268","DOIUrl":"https://doi.org/10.1287/opre.2022.2268","url":null,"abstract":"Integer packing problems have traditionally been some of the most fundamental and well-studied computational questions in discrete optimization. The paper by Aouad and Segev studies the incremental knapsack problem, where one wishes to sequentially pack items into a knapsack whose capacity expands over a finite planning horizon, with the objective of maximizing time-averaged profits. Although various approximation algorithms were developed under mitigating structural assumptions, obtaining nontrivial performance guarantees for this problem in its utmost generality has remained an open question thus far. The authors devise the first polynomial-time approximation scheme for general instances of the incremental knapsack problem, which is the strongest guarantee possible given existing hardness results. Their approach synthesizes various techniques related to approximate dynamic programming, including problem decompositions, counting arguments, and efficient rounding methods, which may be of broader interest.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"36 1","pages":"1414-1433"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79099278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A Near-Optimal Patient Admission Policy in Postacute Care Motivated by applications in the postacute healthcare industry, in “Technical Note—A Near-Optimal Algorithm for Real-Time Order Acceptance: An Application in Postacute Healthcare Services,” Qu, Dawande, and Janakiraman study an infinite-horizon, stochastic optimization problem with a set of long-term capacity investment decisions and a sequence of real-time order acceptance/rejection decisions. To maximize the long-run average expected profit per period, the firm accepts/rejects stochastically arriving referrals in real time. Referrals differ in the revenue they offer to the firm, the resource requirements and the frequency of usage of the resources, and the stochastic duration of the episode. The authors develop a simple policy, derive a worst case guarantee on its optimality gap, and show that the policy is asymptotically optimal. They also illustrate an impressive numerical performance of the policy using public healthcare data.
{"title":"Technical Note - A Near-Optimal Algorithm for Real-Time Order Acceptance: An Application in Postacute Healthcare Services","authors":"Zihao Qu, Milind Dawande, G. Janakiraman","doi":"10.1287/opre.2022.2278","DOIUrl":"https://doi.org/10.1287/opre.2022.2278","url":null,"abstract":"A Near-Optimal Patient Admission Policy in Postacute Care Motivated by applications in the postacute healthcare industry, in “Technical Note—A Near-Optimal Algorithm for Real-Time Order Acceptance: An Application in Postacute Healthcare Services,” Qu, Dawande, and Janakiraman study an infinite-horizon, stochastic optimization problem with a set of long-term capacity investment decisions and a sequence of real-time order acceptance/rejection decisions. To maximize the long-run average expected profit per period, the firm accepts/rejects stochastically arriving referrals in real time. Referrals differ in the revenue they offer to the firm, the resource requirements and the frequency of usage of the resources, and the stochastic duration of the episode. The authors develop a simple policy, derive a worst case guarantee on its optimality gap, and show that the policy is asymptotically optimal. They also illustrate an impressive numerical performance of the policy using public healthcare data.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"67 1","pages":"2213-2225"},"PeriodicalIF":0.0,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83456358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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