In the paper “The Multinomial Logit Model with Sequential Offerings: Algorithmic Frameworks for Product Recommendation Displays,” we consider a sequential assortment problem that has applications ranging from appointment scheduling in hospitals, restaurants, and fitness centers to product recommendation in e-commerce settings. Our main contribution comes in the form of a strongly polynomial-time approximation scheme for the most general form of the problem. We also conduct an extensive case study in which we fit our sequential model to historical search data from Expedia’s hotel booking platform. We observe substantial gains in fitting accuracy when our model is benchmarked against other well-known choice models designed for the setting at hand.
{"title":"Technical Note - The Multinomial Logit Model with Sequential Offerings: Algorithmic Frameworks for Product Recommendation Displays","authors":"Jacob B. Feldman, D. Segev","doi":"10.1287/opre.2021.2218","DOIUrl":"https://doi.org/10.1287/opre.2021.2218","url":null,"abstract":"In the paper “The Multinomial Logit Model with Sequential Offerings: Algorithmic Frameworks for Product Recommendation Displays,” we consider a sequential assortment problem that has applications ranging from appointment scheduling in hospitals, restaurants, and fitness centers to product recommendation in e-commerce settings. Our main contribution comes in the form of a strongly polynomial-time approximation scheme for the most general form of the problem. We also conduct an extensive case study in which we fit our sequential model to historical search data from Expedia’s hotel booking platform. We observe substantial gains in fitting accuracy when our model is benchmarked against other well-known choice models designed for the setting at hand.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"39 1","pages":"2162-2184"},"PeriodicalIF":0.0,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90373328","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}
Simulation Solution Screening Using Functional Properties Simulation models today give rise to problems with large numbers of simulated scenarios or solutions—more than can be simulated exhaustively. Fortunately, users of these models may be able to verify or infer properties, such as convexity, of a performance measure of interest when viewed as a function over the space of solutions. In “Plausible Screening Using Functional Properties for Simulations with Large Solution Spaces,” Eckman, Plumlee and Nelson introduce a framework in which such properties are exploited to avoid simulating solutions with unacceptable performances. Their methods solve optimization problems that measure how well the result of a limited simulation experiment agrees with the claim that a solution is acceptable. These methods deliver desirable statistical guarantees of confidence and consistency. Numerical experiments illustrate how functional properties coupled with small simulation experiments can avoid many simulations for simulation-optimization problems.
{"title":"Plausible Screening Using Functional Properties for Simulations with Large Solution Spaces","authors":"David J. Eckman, M. Plumlee, B. Nelson","doi":"10.1287/opre.2021.2206","DOIUrl":"https://doi.org/10.1287/opre.2021.2206","url":null,"abstract":"Simulation Solution Screening Using Functional Properties Simulation models today give rise to problems with large numbers of simulated scenarios or solutions—more than can be simulated exhaustively. Fortunately, users of these models may be able to verify or infer properties, such as convexity, of a performance measure of interest when viewed as a function over the space of solutions. In “Plausible Screening Using Functional Properties for Simulations with Large Solution Spaces,” Eckman, Plumlee and Nelson introduce a framework in which such properties are exploited to avoid simulating solutions with unacceptable performances. Their methods solve optimization problems that measure how well the result of a limited simulation experiment agrees with the claim that a solution is acceptable. These methods deliver desirable statistical guarantees of confidence and consistency. Numerical experiments illustrate how functional properties coupled with small simulation experiments can avoid many simulations for simulation-optimization problems.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"45 1","pages":"3473-3489"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88781759","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}
Predicting Shortages in a Supply Chain Using Simulation A supply chain shortage is a serious problem that can lead to assembly plant shutdowns. However, predicting such shortages using simulation poses a challenge, because the information necessary to initialize such a supply chain simulation is often only partially observable in many real-world applications. In “Simulation-Based Prediction,” Lim and Glynn investigate this problem. They formulate such a prediction problem as the problem of computing the conditional expectation of the quantity of interest, given the observed state of the system. Simulation can be easily applied to computing such a conditional expectation when the simulation state is fully observed in the real system. Lim and Glynn propose a new simulation methodology appropriate to the many settings in which the observed current state does not fully determine the simulation’s initial state. With the use of such methods, simulation has the potential to more accurately predict upcoming supply chain bottlenecks and to enhance predictions in the many other problem settings where simulation is commonly used.
{"title":"Simulation-Based Prediction","authors":"Eunji Lim, P. Glynn","doi":"10.1287/opre.2021.2229","DOIUrl":"https://doi.org/10.1287/opre.2021.2229","url":null,"abstract":"Predicting Shortages in a Supply Chain Using Simulation A supply chain shortage is a serious problem that can lead to assembly plant shutdowns. However, predicting such shortages using simulation poses a challenge, because the information necessary to initialize such a supply chain simulation is often only partially observable in many real-world applications. In “Simulation-Based Prediction,” Lim and Glynn investigate this problem. They formulate such a prediction problem as the problem of computing the conditional expectation of the quantity of interest, given the observed state of the system. Simulation can be easily applied to computing such a conditional expectation when the simulation state is fully observed in the real system. Lim and Glynn propose a new simulation methodology appropriate to the many settings in which the observed current state does not fully determine the simulation’s initial state. With the use of such methods, simulation has the potential to more accurately predict upcoming supply chain bottlenecks and to enhance predictions in the many other problem settings where simulation is commonly used.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"347 1","pages":"47-60"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77321625","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}
This study proposes a risk-adjusted version of bed occupancy rates (BORs) that can be used for patient admission control and bed capacity planning in hospitals. Simulations indicate a potential increase of 11% in elective patient admissions by planning using the proposed bed shortage index (BSI) as opposed to the traditional BOR. The BSI is also illustrated for purposes of bed capacity allocation between departments and across acute and nonacute hospitals.
{"title":"The Analytics of Bed Shortages: Coherent Metric, Prediction, and Optimization","authors":"Jingui Xie, G. Loke, Melvyn Sim, S. Lam","doi":"10.1287/opre.2021.2231","DOIUrl":"https://doi.org/10.1287/opre.2021.2231","url":null,"abstract":"This study proposes a risk-adjusted version of bed occupancy rates (BORs) that can be used for patient admission control and bed capacity planning in hospitals. Simulations indicate a potential increase of 11% in elective patient admissions by planning using the proposed bed shortage index (BSI) as opposed to the traditional BOR. The BSI is also illustrated for purposes of bed capacity allocation between departments and across acute and nonacute hospitals.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"1 1","pages":"23-46"},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78430697","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}
Title: Constant Regret Resolving Heuristics for Price-Based Revenue Management Network revenue management (NRM) and its corresponding pricing question is one of the most fundamental problems in operations management. To alleviate the curse of dimensionality and the prohibitive cost of computing an exact solution using dynamic programming, computationally efficient resolving algorithms are proposed. The state-of-the-art analysis of the resolving heuristic establishes a logarithmic additive regret for price-based NRM problems. In “Constant Regret Resolving Heuristics for Price-Based Revenue Management,” Y. Wang and H. Wang from the University of Florida and Georgia Institute of Technology, respectively, significantly advance the state-of-the-art analysis by showing a constant regret for resolving heuristics. Their theoretical advance is made possible by a novel, direct analysis of the exact DP solution.
{"title":"Constant Regret Resolving Heuristics for Price-Based Revenue Management","authors":"Yining Wang, He Wang","doi":"10.1287/opre.2021.2219","DOIUrl":"https://doi.org/10.1287/opre.2021.2219","url":null,"abstract":"Title: Constant Regret Resolving Heuristics for Price-Based Revenue Management Network revenue management (NRM) and its corresponding pricing question is one of the most fundamental problems in operations management. To alleviate the curse of dimensionality and the prohibitive cost of computing an exact solution using dynamic programming, computationally efficient resolving algorithms are proposed. The state-of-the-art analysis of the resolving heuristic establishes a logarithmic additive regret for price-based NRM problems. In “Constant Regret Resolving Heuristics for Price-Based Revenue Management,” Y. Wang and H. Wang from the University of Florida and Georgia Institute of Technology, respectively, significantly advance the state-of-the-art analysis by showing a constant regret for resolving heuristics. Their theoretical advance is made possible by a novel, direct analysis of the exact DP solution.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"10 1","pages":"3538-3557"},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74974298","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}
Grid energy storage plays a key role in making carbon-free, renewable energy production a reality. Yet, when it comes to maximizing profit, owners of storage assets still struggle with coordinating their trading activities across time because of the complex nature of multisettlement electricity markets. In “Coordination of Multimarket Bidding of Grid-Energy Storage,” Nils Löhndorf and David Wozabal propose a multistage stochastic programming model for market-oriented optimization of energy storage. To calculate lower and upper bounds on optimal values, they develop novel methods for scenario-tree generation and information relaxation. They show that a coordinated policy that reserves capacity for the short-term markets is optimal and that the gap to a sequential policy increases with short-term price volatility and market liquidity. The authors find that coordination is beneficial for all considered asset types and that flexible storages with high price impact benefit most. Their findings inform storage owners which markets contribute most value, how to organize trading across time, and how to calculate optimal bidding strategies.
{"title":"The Value of Coordination in Multimarket Bidding of Grid Energy Storage","authors":"N. Löhndorf, D. Wozabal","doi":"10.1287/opre.2021.2247","DOIUrl":"https://doi.org/10.1287/opre.2021.2247","url":null,"abstract":"Grid energy storage plays a key role in making carbon-free, renewable energy production a reality. Yet, when it comes to maximizing profit, owners of storage assets still struggle with coordinating their trading activities across time because of the complex nature of multisettlement electricity markets. In “Coordination of Multimarket Bidding of Grid-Energy Storage,” Nils Löhndorf and David Wozabal propose a multistage stochastic programming model for market-oriented optimization of energy storage. To calculate lower and upper bounds on optimal values, they develop novel methods for scenario-tree generation and information relaxation. They show that a coordinated policy that reserves capacity for the short-term markets is optimal and that the gap to a sequential policy increases with short-term price volatility and market liquidity. The authors find that coordination is beneficial for all considered asset types and that flexible storages with high price impact benefit most. Their findings inform storage owners which markets contribute most value, how to organize trading across time, and how to calculate optimal bidding strategies.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"49 1","pages":"1-22"},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75260197","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}
Chandrasekhar Manchiraju, Milind Dawande, G. Janakiraman
Dynamic pricing is a well-known revenue management technique used by firms to maximize their revenues. In industries such as fashion retail and airlines, it is observed in practice that firms vary the prices of their products, through time, only among certain pre-fixed discrete price points; for example, in fashion retail, products are first sold at a base price and then later at, say, 10%, 20%, 30% discounted prices. In such discrete-price practices, when the number of products offered by a firm is large, it is mathematically challenging to determine the optimal pricing decisions. In “Multiproduct Pricing with Discrete Price Sets,” Manchiraju, Dawande, and Janakiraman design pricing algorithms that are fast and result in near-optimal revenues.
{"title":"Multiproduct Pricing with Discrete Price Sets","authors":"Chandrasekhar Manchiraju, Milind Dawande, G. Janakiraman","doi":"10.1287/opre.2021.2222","DOIUrl":"https://doi.org/10.1287/opre.2021.2222","url":null,"abstract":"Dynamic pricing is a well-known revenue management technique used by firms to maximize their revenues. In industries such as fashion retail and airlines, it is observed in practice that firms vary the prices of their products, through time, only among certain pre-fixed discrete price points; for example, in fashion retail, products are first sold at a base price and then later at, say, 10%, 20%, 30% discounted prices. In such discrete-price practices, when the number of products offered by a firm is large, it is mathematically challenging to determine the optimal pricing decisions. In “Multiproduct Pricing with Discrete Price Sets,” Manchiraju, Dawande, and Janakiraman design pricing algorithms that are fast and result in near-optimal revenues.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"64 1","pages":"2185-2193"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86215369","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}
Srikanth Jagabathula, Dmitry Mitrofanov, Gustavo J. Vulcano
A Framework to Run Personalized Promotions The availability of individual-level transaction data allows retailers to implement personalized operational decisions. Although such decisions have been around for several years now in online platforms, recent technological developments open new opportunities to extend similar practices to bricks-and-mortar settings (e.g., by using electronic price tags to show different prices to different customers or by using beacon-based technology to send promotion offers to targeted customers). In “Personalized Retail Promotions through a DAG-Based Representation of Customer Preferences,” Jagabathula, Mitrofanov, and Vulcano propose a back-to-back procedure for running customized promotions in retail operations contexts, from the construction of a nonparametric choice model where customer preferences are represented by directed acyclic graphs to the formulation of the promotion optimization problem. The empirical validation of their proposal on real supermarket data shows the promising performance of their approach over state-of-the-art benchmarks.
{"title":"Personalized Retail Promotions Through a Directed Acyclic Graph-Based Representation of Customer Preferences","authors":"Srikanth Jagabathula, Dmitry Mitrofanov, Gustavo J. Vulcano","doi":"10.1287/opre.2021.2108","DOIUrl":"https://doi.org/10.1287/opre.2021.2108","url":null,"abstract":"A Framework to Run Personalized Promotions The availability of individual-level transaction data allows retailers to implement personalized operational decisions. Although such decisions have been around for several years now in online platforms, recent technological developments open new opportunities to extend similar practices to bricks-and-mortar settings (e.g., by using electronic price tags to show different prices to different customers or by using beacon-based technology to send promotion offers to targeted customers). In “Personalized Retail Promotions through a DAG-Based Representation of Customer Preferences,” Jagabathula, Mitrofanov, and Vulcano propose a back-to-back procedure for running customized promotions in retail operations contexts, from the construction of a nonparametric choice model where customer preferences are represented by directed acyclic graphs to the formulation of the promotion optimization problem. The empirical validation of their proposal on real supermarket data shows the promising performance of their approach over state-of-the-art benchmarks.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"16 1","pages":"641-665"},"PeriodicalIF":0.0,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81513370","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}
Applications of mixed integer programming can be found in many industries, such as transportation, healthcare, energy, and finance, and their economic impact is significant. It is also well known that mixed integer programs (MIPs) can be very difficult to solve. Their challenge continues to stimulate research in the design and implementation of efficient and effective techniques that can better solve them. In this study, we introduce a novel and powerful approach for solving certain classes of mixed integer programs (MIPs): decomposition branching. Two seminal and widely used techniques for solving MIPs, branch-and-bound and decomposition, form its foundation. Computational experiments with instances of a weighted set covering problem and a regionalized p-median facility location problem with assignment range constraints demonstrate its efficacy: it explores far fewer nodes and can be orders of magnitude faster than a commercial solver and an automatic Dantzig-Wolfe approach.
{"title":"Decomposition Branching for Mixed Integer Programming","authors":"Barış Yıldız, N. Boland, M. Savelsbergh","doi":"10.1287/opre.2021.2210","DOIUrl":"https://doi.org/10.1287/opre.2021.2210","url":null,"abstract":"Applications of mixed integer programming can be found in many industries, such as transportation, healthcare, energy, and finance, and their economic impact is significant. It is also well known that mixed integer programs (MIPs) can be very difficult to solve. Their challenge continues to stimulate research in the design and implementation of efficient and effective techniques that can better solve them. In this study, we introduce a novel and powerful approach for solving certain classes of mixed integer programs (MIPs): decomposition branching. Two seminal and widely used techniques for solving MIPs, branch-and-bound and decomposition, form its foundation. Computational experiments with instances of a weighted set covering problem and a regionalized p-median facility location problem with assignment range constraints demonstrate its efficacy: it explores far fewer nodes and can be orders of magnitude faster than a commercial solver and an automatic Dantzig-Wolfe approach.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"8 1","pages":"1854-1872"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78875072","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}
Since the seminal work of Gale and Shapley, stable assignments have received widespread attention for their mathematical elegance and broad applicability. However, in applications such as the school choice problem, in which public schools are often perceived as commodities and only students’ welfare matters, enforcing stability implies a loss of efficiency for the students. In “Legal assignments and fast EADAM with consent via classical theory of stable matchings,” Faenza and Zhang study two extensions of the traditional model—legal assignments and efficiency adjusted deferred acceptance mechanism (EADAM)—that strive to regain this loss in efficiency. The authors establish a tight connection between legal and stable assignments, which allows them to use critical structural tools of stable matchings, such as the concept of rotations, to design provably fast algorithms for (1) optimizing linear functions over the set of legal assignments and (2) finding the outcome of EADAM. These algorithmic results greatly improve the applicability of both extensions as witnessed by a complexity analysis and experimental results.
{"title":"Legal Assignments and Fast EADAM with Consent via Classic Theory of Stable Matchings","authors":"Yuri Faenza, Xuan Zhang","doi":"10.1287/opre.2021.2199","DOIUrl":"https://doi.org/10.1287/opre.2021.2199","url":null,"abstract":"Since the seminal work of Gale and Shapley, stable assignments have received widespread attention for their mathematical elegance and broad applicability. However, in applications such as the school choice problem, in which public schools are often perceived as commodities and only students’ welfare matters, enforcing stability implies a loss of efficiency for the students. In “Legal assignments and fast EADAM with consent via classical theory of stable matchings,” Faenza and Zhang study two extensions of the traditional model—legal assignments and efficiency adjusted deferred acceptance mechanism (EADAM)—that strive to regain this loss in efficiency. The authors establish a tight connection between legal and stable assignments, which allows them to use critical structural tools of stable matchings, such as the concept of rotations, to design provably fast algorithms for (1) optimizing linear functions over the set of legal assignments and (2) finding the outcome of EADAM. These algorithmic results greatly improve the applicability of both extensions as witnessed by a complexity analysis and experimental results.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"19 1","pages":"1873-1890"},"PeriodicalIF":0.0,"publicationDate":"2022-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82143596","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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