Problem definition: We study the dynamic fulfillment problem in e-commerce, in which incoming (multi-item) customer orders must be immediately dispatched to (a combination of) fulfillment centers that have the required inventory. Methodology/results: A prevailing approach to this problem, pioneered by Jasin and Sinha in 2015 , has been to write a “deterministic” linear program that dictates, for each item in an incoming multi-item order from a particular region, how frequently it should be dispatched to each fulfillment center (FC). However, dispatching items in a way that satisfies these frequency constraints, without splitting the order across too many FCs, is challenging. Jasin and Sinha in 2015 identified this as a correlated rounding problem and proposed an intricate rounding scheme that they proved was suboptimal by a factor of at most [Formula: see text] on a q-item order. This paper provides, to our knowledge, the first substantially improved scheme for this correlated rounding problem, which is suboptimal by a factor of at most [Formula: see text]. We provide another scheme for sparse networks, which is suboptimal by a factor of at most d if each item is stored in at most d FCs. We show both of these guarantees to be tight in terms of the dependence on q or d. Our schemes are simple and fast, based on an intuitive idea; items wait for FCs to “open” at random times but observe them on “dilated” time scales. This also implies a new randomized rounding method for the classical Set Cover problem, which could be of general interest. Managerial implications: We numerically test our new rounding schemes under the same realistic setups as Jasin and Sinha and find that they improve runtimes, shorten code, and robustly improve performance. Our code is made publicly available online. History: This paper was selected for Fast Track in the M&SOM Journal from the 2022 MSOM Supply Chain Management SIG Conference. Funding: This research was partially funded by a grant from Amazon.com Inc., which was awarded through collaboration with the Columbia Center of AI Technology (CAIT).
{"title":"Order-Optimal Correlated Rounding for Fulfilling Multi-Item E-Commerce Orders","authors":"Will Ma","doi":"10.1287/msom.2023.1219","DOIUrl":"https://doi.org/10.1287/msom.2023.1219","url":null,"abstract":"Problem definition: We study the dynamic fulfillment problem in e-commerce, in which incoming (multi-item) customer orders must be immediately dispatched to (a combination of) fulfillment centers that have the required inventory. Methodology/results: A prevailing approach to this problem, pioneered by Jasin and Sinha in 2015 , has been to write a “deterministic” linear program that dictates, for each item in an incoming multi-item order from a particular region, how frequently it should be dispatched to each fulfillment center (FC). However, dispatching items in a way that satisfies these frequency constraints, without splitting the order across too many FCs, is challenging. Jasin and Sinha in 2015 identified this as a correlated rounding problem and proposed an intricate rounding scheme that they proved was suboptimal by a factor of at most [Formula: see text] on a q-item order. This paper provides, to our knowledge, the first substantially improved scheme for this correlated rounding problem, which is suboptimal by a factor of at most [Formula: see text]. We provide another scheme for sparse networks, which is suboptimal by a factor of at most d if each item is stored in at most d FCs. We show both of these guarantees to be tight in terms of the dependence on q or d. Our schemes are simple and fast, based on an intuitive idea; items wait for FCs to “open” at random times but observe them on “dilated” time scales. This also implies a new randomized rounding method for the classical Set Cover problem, which could be of general interest. Managerial implications: We numerically test our new rounding schemes under the same realistic setups as Jasin and Sinha and find that they improve runtimes, shorten code, and robustly improve performance. Our code is made publicly available online. History: This paper was selected for Fast Track in the M&SOM Journal from the 2022 MSOM Supply Chain Management SIG Conference. Funding: This research was partially funded by a grant from Amazon.com Inc., which was awarded through collaboration with the Columbia Center of AI Technology (CAIT).","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135060669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emma Gibson, Sarang Deo, Jónas Oddur Jónasson, Mphatso Kachule, Kara Palamountain
Problem definition: Healthcare systems in resource-limited settings rely on diagnostic networks in which medical samples (e.g., blood, sputum) and results need to be transported between geographically dispersed healthcare facilities and centralized laboratories. Academic/practical relevance: Existing sample transportation (ST) systems typically operate fixed schedules, which do not account for demand variability and lead to unnecessary transportation visits as well as delays. Methodology: We design an optimized sample transportation (OST) system that comprises two components: (i) a new approach for timely collection of information on transportation demand (samples and results) using low-cost technology based on feature phones, and (ii) an optimization-based solution approach to the problem of routing and scheduling courier trips in a multistage transportation system. Results: Our solution approach performs well in a range of numerical experiments. Furthermore, we implement OST in collaboration with Riders For Health, who operate the national ST system in Malawi. Based on analysis of field data describing over 20,000 samples and results transported during July–October 2019, we show that the implementation of OST routes reduced average ST delays in three districts of Malawi by approximately 25%. In addition, the proportion of unnecessary trips by ST couriers decreased by 55%. Managerial implications: Our approach for improving ST operations is feasible and effective in Malawi and can be applied to other resource-limited settings, particularly in sub-Saharan Africa. History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition. Funding: This work was supported by Bill and Melinda Gates Foundation [Grant OPP1182217] and by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health [Grant U54EB027049]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1182 .
{"title":"Redesigning Sample Transportation in Malawi Through Improved Data Sharing and Daily Route Optimization","authors":"Emma Gibson, Sarang Deo, Jónas Oddur Jónasson, Mphatso Kachule, Kara Palamountain","doi":"10.1287/msom.2022.1182","DOIUrl":"https://doi.org/10.1287/msom.2022.1182","url":null,"abstract":"Problem definition: Healthcare systems in resource-limited settings rely on diagnostic networks in which medical samples (e.g., blood, sputum) and results need to be transported between geographically dispersed healthcare facilities and centralized laboratories. Academic/practical relevance: Existing sample transportation (ST) systems typically operate fixed schedules, which do not account for demand variability and lead to unnecessary transportation visits as well as delays. Methodology: We design an optimized sample transportation (OST) system that comprises two components: (i) a new approach for timely collection of information on transportation demand (samples and results) using low-cost technology based on feature phones, and (ii) an optimization-based solution approach to the problem of routing and scheduling courier trips in a multistage transportation system. Results: Our solution approach performs well in a range of numerical experiments. Furthermore, we implement OST in collaboration with Riders For Health, who operate the national ST system in Malawi. Based on analysis of field data describing over 20,000 samples and results transported during July–October 2019, we show that the implementation of OST routes reduced average ST delays in three districts of Malawi by approximately 25%. In addition, the proportion of unnecessary trips by ST couriers decreased by 55%. Managerial implications: Our approach for improving ST operations is feasible and effective in Malawi and can be applied to other resource-limited settings, particularly in sub-Saharan Africa. History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition. Funding: This work was supported by Bill and Melinda Gates Foundation [Grant OPP1182217] and by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health [Grant U54EB027049]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1182 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136011726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: With the rapid growth of the gig economy, on-demand staffing platforms have emerged to help companies manage their temporary workforce. This emerging business-to-business context motivates us to study a new form of supply chain coordination problem. We consider a staffing platform managing an on-demand workforce to serve multiple firms facing stochastic labor demand. Before demand realization, each individual firm can hire permanent employees, whereas the platform determines a compensation rate for potential on-demand workers. After knowing the realized demand, firms in need can request on-demand workers from the platform, and then, the platform operator allocates the available on-demand workforce among the firms. We explore how to maximize and distribute the benefits of an on-demand workforce through coordinating self-interested parties in the staffing system. Methodology/results: We combine game theory and online optimization techniques to address the challenges in incentivizing and coordinating the online workforce. We propose a novel and easily implementable fill rate-based allocation and coordination mechanism that enables the on-demand workforce to be shared optimally when individual firms and the platform operator make decisions in their own interest. We also show that the proposed mechanism can be adapted to the cases when contract terms need to be identical to all firms and when actual demand is unverifiable. Managerial implications: The proposed contract mechanism is in line with the performance-based contracting commonly used in on-demand staffing services. Our results suggest that under an appropriately designed performance-based mechanism, individual firms and the platform operator can share the maximum benefits of on-demand staffing. Funding: This work was supported by the National Natural Science Foundation of China [Grant 71871097] and the Fundamental Research Funds for the Central Universities, China. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0327 .
{"title":"Maximizing the Benefits of an On-Demand Workforce: Fill Rate-Based Allocation and Coordination Mechanisms","authors":"Tao Lu, Zhichao Zheng, Yuanguang Zhong","doi":"10.1287/msom.2021.0327","DOIUrl":"https://doi.org/10.1287/msom.2021.0327","url":null,"abstract":"Problem definition: With the rapid growth of the gig economy, on-demand staffing platforms have emerged to help companies manage their temporary workforce. This emerging business-to-business context motivates us to study a new form of supply chain coordination problem. We consider a staffing platform managing an on-demand workforce to serve multiple firms facing stochastic labor demand. Before demand realization, each individual firm can hire permanent employees, whereas the platform determines a compensation rate for potential on-demand workers. After knowing the realized demand, firms in need can request on-demand workers from the platform, and then, the platform operator allocates the available on-demand workforce among the firms. We explore how to maximize and distribute the benefits of an on-demand workforce through coordinating self-interested parties in the staffing system. Methodology/results: We combine game theory and online optimization techniques to address the challenges in incentivizing and coordinating the online workforce. We propose a novel and easily implementable fill rate-based allocation and coordination mechanism that enables the on-demand workforce to be shared optimally when individual firms and the platform operator make decisions in their own interest. We also show that the proposed mechanism can be adapted to the cases when contract terms need to be identical to all firms and when actual demand is unverifiable. Managerial implications: The proposed contract mechanism is in line with the performance-based contracting commonly used in on-demand staffing services. Our results suggest that under an appropriately designed performance-based mechanism, individual firms and the platform operator can share the maximum benefits of on-demand staffing. Funding: This work was supported by the National Natural Science Foundation of China [Grant 71871097] and the Fundamental Research Funds for the Central Universities, China. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0327 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135571020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ernan Haruvy, Meisam Hejazi Nia, Özalp Özer, A. Serdar Şimşek
Problem definition: Dynamic forecasting models in auctions have fallen short on two dimensions: (i) the lack of an equilibrium model for final bids and (ii) the lack of a winner’s curse (i.e., a tendency to overpay conditional on winning the auction) adjustment to allow bidders to account for a common value component in the auction item. In this paper, we develop a methodology to accurately predict equilibrium stage bids from the initial bidding dynamics and quantify the impact of the winner’s curse. This methodology allows us to conduct policy simulations to optimize auction design parameters. Methodology/Results: Dynamic auctions typically have a stage of high exploratory activity, followed by an inactivity period, and then an equilibrium stage of last-minute bids with sharp jumps. With a Kalman filter approach, we use exploratory stage bids to predict an auction item’s valuation distribution. We feed this prediction into an equilibrium model and apply item-specific adjustments for winner’s curse, bidder heterogeneity, and inactivity period. We use the resulting equilibrium model to predict the equilibrium stage bids. Our methodology improves the forecast of equilibrium stage bids by 11.33%, on average, compared with a state-of-the-art benchmark. This improvement is even higher (18.99%) for common value auctions. We also find that (i) significantly more (respectively, fewer) bidders internalize the winner’s curse in common value (respectively, private value) auctions; (ii) bidders in common value auctions decrease their bids by 6.03% because of the winner’s curse; and (iii) the inactivity period has a lesser impact on the equilibrium stage bids in private value auctions. Managerial implications: Our proposed methodology is intended to facilitate the need in academia and practice for real-time bid predictions that encompass different levels of the common value component in auctions. Using our methodology, auction platforms can support their choice of minimum bid increment policies and decide how to allocate resources across different auctions to mitigate the adverse effects of the winner’s curse. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1165 .
{"title":"The Winner’s Curse in Dynamic Forecasting of Auction Data: Empirical Evidence from eBay","authors":"Ernan Haruvy, Meisam Hejazi Nia, Özalp Özer, A. Serdar Şimşek","doi":"10.1287/msom.2022.1165","DOIUrl":"https://doi.org/10.1287/msom.2022.1165","url":null,"abstract":"Problem definition: Dynamic forecasting models in auctions have fallen short on two dimensions: (i) the lack of an equilibrium model for final bids and (ii) the lack of a winner’s curse (i.e., a tendency to overpay conditional on winning the auction) adjustment to allow bidders to account for a common value component in the auction item. In this paper, we develop a methodology to accurately predict equilibrium stage bids from the initial bidding dynamics and quantify the impact of the winner’s curse. This methodology allows us to conduct policy simulations to optimize auction design parameters. Methodology/Results: Dynamic auctions typically have a stage of high exploratory activity, followed by an inactivity period, and then an equilibrium stage of last-minute bids with sharp jumps. With a Kalman filter approach, we use exploratory stage bids to predict an auction item’s valuation distribution. We feed this prediction into an equilibrium model and apply item-specific adjustments for winner’s curse, bidder heterogeneity, and inactivity period. We use the resulting equilibrium model to predict the equilibrium stage bids. Our methodology improves the forecast of equilibrium stage bids by 11.33%, on average, compared with a state-of-the-art benchmark. This improvement is even higher (18.99%) for common value auctions. We also find that (i) significantly more (respectively, fewer) bidders internalize the winner’s curse in common value (respectively, private value) auctions; (ii) bidders in common value auctions decrease their bids by 6.03% because of the winner’s curse; and (iii) the inactivity period has a lesser impact on the equilibrium stage bids in private value auctions. Managerial implications: Our proposed methodology is intended to facilitate the need in academia and practice for real-time bid predictions that encompass different levels of the common value component in auctions. Using our methodology, auction platforms can support their choice of minimum bid increment policies and decide how to allocate resources across different auctions to mitigate the adverse effects of the winner’s curse. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1165 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135449545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We consider a revenue management problem that arises from the selling of high-speed train tickets in China. Compared with traditional network revenue management problems, the new feature of our problem is the assign-to-seat restriction. That is, each request, if accepted, must be assigned instantly to a single seat throughout the whole journey, and later adjustment is not allowed. When making decisions, the seller needs to track not only the total seat capacity available, but also the status of each seat. Methodology/results: We build a modified network revenue management model for this problem. First, we study a static problem in which all requests are given. Although the problem is NP-hard in general, we identify conditions for solvability in polynomial time and propose efficient approximation algorithms for general cases. We then introduce a bid-price control policy based on a novel maximal sequence principle. This policy accommodates nonlinearity in bid prices and, as a result, yields a more accurate approximation of the value function than a traditional bid-price control policy does. Finally, we combine a dynamic view of the maximal sequence with the static solution of a primal problem to propose a “re-solving a dynamic primal” policy that can achieve uniformly bounded revenue loss under mild assumptions. Numerical experiments using both synthetic and real data document the advantage of our proposed policies on resource-allocation efficiency. Managerial implications: The results of this study reveal connections between our problem and traditional network revenue management problems. Particularly, we demonstrate that by adaptively using our proposed methods, the impact of the assign-to-seat restriction becomes limited both in theory and practice. Funding: S. Liu’s research is partly supported by the National Natural Science Foundation of China (NSFC) [Grant NSFC-72072117]. Z. Wang’s research is partly supported by the NSFC [Grant NSFC-72150002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1188 .
{"title":"Assign-to-Seat: Dynamic Capacity Control for Selling High-Speed Train Tickets","authors":"Feng Zhu, Shaoxuan Liu, Rowan Wang, Zizhuo Wang","doi":"10.1287/msom.2023.1188","DOIUrl":"https://doi.org/10.1287/msom.2023.1188","url":null,"abstract":"Problem definition: We consider a revenue management problem that arises from the selling of high-speed train tickets in China. Compared with traditional network revenue management problems, the new feature of our problem is the assign-to-seat restriction. That is, each request, if accepted, must be assigned instantly to a single seat throughout the whole journey, and later adjustment is not allowed. When making decisions, the seller needs to track not only the total seat capacity available, but also the status of each seat. Methodology/results: We build a modified network revenue management model for this problem. First, we study a static problem in which all requests are given. Although the problem is NP-hard in general, we identify conditions for solvability in polynomial time and propose efficient approximation algorithms for general cases. We then introduce a bid-price control policy based on a novel maximal sequence principle. This policy accommodates nonlinearity in bid prices and, as a result, yields a more accurate approximation of the value function than a traditional bid-price control policy does. Finally, we combine a dynamic view of the maximal sequence with the static solution of a primal problem to propose a “re-solving a dynamic primal” policy that can achieve uniformly bounded revenue loss under mild assumptions. Numerical experiments using both synthetic and real data document the advantage of our proposed policies on resource-allocation efficiency. Managerial implications: The results of this study reveal connections between our problem and traditional network revenue management problems. Particularly, we demonstrate that by adaptively using our proposed methods, the impact of the assign-to-seat restriction becomes limited both in theory and practice. Funding: S. Liu’s research is partly supported by the National Natural Science Foundation of China (NSFC) [Grant NSFC-72072117]. Z. Wang’s research is partly supported by the NSFC [Grant NSFC-72150002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1188 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135801478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We ask whether and how a charitable organization’s front-line staff members can be effectively positioned to encourage donors to donate more (in compliance with the eligibility rules) during their in-person interactions. Academic/practical relevance: Specifically, we consider how charitable organizations can use microlevel data on worker-donor interactions to improve donation outcomes, via understanding of workers’ experiences and donors’ characteristics. Methodology: Using a unique data set at the worker-donor interaction level, we analyze the role of nurses’ experiences in driving charitable productivity and explore the downstream effects of the donation volume outcome. Results: We find that the effect of the charitable worker on charitable productivity strongly depends on the worker’s experiences that entail sharing knowledge about a donor’s donation options, rather than the worker’s experiences that are primarily focused on collecting donations. Moreover, worker experience can encourage donors that have lower self-efficacy over performing their donation to choose higher donation volumes. A worker’s experience with donors with lower self-efficacy furthermore benefits charitable productivity when interacting with those donors. Higher donations induced by an experienced worker from the previous session are correlated with higher donation volumes in the focal session if the donor returns to donate. Managerial implications: When taking the insights on staff-donor interactions into account, improved matching between workers and donors can provide economically significant benefits for the blood bank. Understanding worker experience in the staff-donor interactions and leveraging big data in staffing decisions can help charitable organizations improve their productivity simply from the personnel end. Funding: W. Lin acknowledges the support of the Marshall Fellowship at the USC Marshall School of Business. S. F. Lu acknowledges the support of the Gerald Lyles Rising Star fund at Purdue. T. Sun acknowledges the support of an Adobe Data Science Award and an iORB grant at the USC Marshall School of Business. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1198 .
问题定义:我们的问题是，慈善机构的一线工作人员在与慈善机构的面对面互动中，能否以及如何有效地定位，鼓励捐赠者(符合资格规则)更多地捐赠。学术/实践相关性:具体而言，我们考虑慈善组织如何通过了解工作人员的经历和捐助者的特点，利用工作人员-捐助者互动的微观数据来改善捐赠结果。研究方法:利用工作者-捐赠者互动层面的独特数据集，我们分析了护士经验在推动慈善生产力方面的作用，并探讨了捐赠量结果的下游效应。结果:我们发现，慈善工作者对慈善生产力的影响在很大程度上取决于工作者的经验，这需要分享有关捐赠者捐赠选择的知识，而不是工作者的经验，主要集中在收集捐赠。此外，工作经验可以鼓励自我效能感较低的捐赠者选择更高的捐赠量。工作人员与自我效能较低的捐赠者打交道的经历进一步有利于与这些捐赠者互动时的慈善生产力。如果捐赠者再次捐赠，由前一届有经验的工作人员诱导的更高捐赠与焦点会议中更高的捐赠量相关。管理意义:当考虑到工作人员与献血者互动的见解时，改善工作人员和献血者之间的匹配可以为血库提供显著的经济效益。了解员工与捐赠者互动中的员工体验，并在人员配置决策中利用大数据，可以帮助慈善组织从人员方面提高生产力。资助:林伟感谢南加州大学马歇尔商学院马歇尔奖学金的支持。S. F. Lu感谢普渡大学Gerald Lyles新星基金的支持。T. Sun感谢Adobe数据科学奖和USC马歇尔商学院iORB资助的支持。补充材料:在线附录可在https://doi.org/10.1287/msom.2023.1198上获得。
{"title":"Worker Experience and Donor Heterogeneity: The Impact of Charitable Workers on Donors’ Blood Donation Decisions","authors":"Wilson Lin, Susan Feng Lu, Tianshu Sun","doi":"10.1287/msom.2023.1198","DOIUrl":"https://doi.org/10.1287/msom.2023.1198","url":null,"abstract":"Problem definition: We ask whether and how a charitable organization’s front-line staff members can be effectively positioned to encourage donors to donate more (in compliance with the eligibility rules) during their in-person interactions. Academic/practical relevance: Specifically, we consider how charitable organizations can use microlevel data on worker-donor interactions to improve donation outcomes, via understanding of workers’ experiences and donors’ characteristics. Methodology: Using a unique data set at the worker-donor interaction level, we analyze the role of nurses’ experiences in driving charitable productivity and explore the downstream effects of the donation volume outcome. Results: We find that the effect of the charitable worker on charitable productivity strongly depends on the worker’s experiences that entail sharing knowledge about a donor’s donation options, rather than the worker’s experiences that are primarily focused on collecting donations. Moreover, worker experience can encourage donors that have lower self-efficacy over performing their donation to choose higher donation volumes. A worker’s experience with donors with lower self-efficacy furthermore benefits charitable productivity when interacting with those donors. Higher donations induced by an experienced worker from the previous session are correlated with higher donation volumes in the focal session if the donor returns to donate. Managerial implications: When taking the insights on staff-donor interactions into account, improved matching between workers and donors can provide economically significant benefits for the blood bank. Understanding worker experience in the staff-donor interactions and leveraging big data in staffing decisions can help charitable organizations improve their productivity simply from the personnel end. Funding: W. Lin acknowledges the support of the Marshall Fellowship at the USC Marshall School of Business. S. F. Lu acknowledges the support of the Gerald Lyles Rising Star fund at Purdue. T. Sun acknowledges the support of an Adobe Data Science Award and an iORB grant at the USC Marshall School of Business. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1198 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135801487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Early research has documented significant growth in ride-hailing services worldwide and allied benefits. However, growing evidence of their negative externalities is leading to significant policy scrutiny. Despite demonstrated socioeconomic benefits and consumer surplus worth billions of dollars, cities are choosing to curb these services in a bid to mitigate first order urban mobility problems. Existing studies on the congestion effects of ride-hailing are limited, report mixed evidence, and exclusively focus on the United States, where the supply consists primarily of part-time drivers. Methodology/results: We study how the absence of ride-hailing services affects congestion levels in three major cities in India, a market where most ride-hailing drivers participate full time. Using rich real-time traffic and route trajectory data from Google Maps, we show that in, all three cities, periods of ride-hailing unavailability due to driver strikes see a discernible drop in travel time. The effects are largest for the most congested regions during the busiest hours, which see 10.1%–14.8% reduction in travel times. Additionally, we provide suggestive evidence for some of the mechanisms behind the observed effects, including deadheading elimination, substitution with public transit, and opening up of shorter alternative routes. Managerial implications: These results suggest that despite their paltry modal share, ride-hailing vehicles are substituting more sustainable means of transport and are contributing significantly to congestion in the cities studied. The reported effect sizes quantify the maximum travel time gains that can be expected on curbing them. Funding: This work was supported by the Srini Raju Center for Information Technology and the Networked Economy at Indian School of Business. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1158 .
{"title":"The Impact of Ride-Hailing Services on Congestion: Evidence from Indian Cities","authors":"Saharsh Agarwal, Deepa Mani, Rahul Telang","doi":"10.1287/msom.2022.1158","DOIUrl":"https://doi.org/10.1287/msom.2022.1158","url":null,"abstract":"Problem definition: Early research has documented significant growth in ride-hailing services worldwide and allied benefits. However, growing evidence of their negative externalities is leading to significant policy scrutiny. Despite demonstrated socioeconomic benefits and consumer surplus worth billions of dollars, cities are choosing to curb these services in a bid to mitigate first order urban mobility problems. Existing studies on the congestion effects of ride-hailing are limited, report mixed evidence, and exclusively focus on the United States, where the supply consists primarily of part-time drivers. Methodology/results: We study how the absence of ride-hailing services affects congestion levels in three major cities in India, a market where most ride-hailing drivers participate full time. Using rich real-time traffic and route trajectory data from Google Maps, we show that in, all three cities, periods of ride-hailing unavailability due to driver strikes see a discernible drop in travel time. The effects are largest for the most congested regions during the busiest hours, which see 10.1%–14.8% reduction in travel times. Additionally, we provide suggestive evidence for some of the mechanisms behind the observed effects, including deadheading elimination, substitution with public transit, and opening up of shorter alternative routes. Managerial implications: These results suggest that despite their paltry modal share, ride-hailing vehicles are substituting more sustainable means of transport and are contributing significantly to congestion in the cities studied. The reported effect sizes quantify the maximum travel time gains that can be expected on curbing them. Funding: This work was supported by the Srini Raju Center for Information Technology and the Networked Economy at Indian School of Business. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1158 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135315089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We study scheduling multi-class impatient customers in parallel server queueing systems. At the time of arrival, customers are identified as being one of many classes, and the class represents the service and patience time distributions as well as cost characteristics. From the system’s perspective, customers of the same class at time of arrival get differentiated on their residual patience time as they wait in queue. We leverage this property and propose two novel and easy-to-implement multi-class scheduling policies. Academic/practical relevance: Scheduling multi-class impatient customers is an important and challenging topic, especially when customers’ patience times are nonexponential. In these contexts, even for customers of the same class, processing them under the first-come, first-served (FCFS) policy is suboptimal. This is because, at time of arrival, the system only knows the overall patience distribution from which a customer’s patience value is drawn, and as time elapses, the estimate of the customer’s residual patience time can be further updated. For nonexponential patience distributions, such an update indeed reveals additional information, and using this information to implement within-class prioritization can lead to additional benefits relative to the FCFS policy. Methodology: We use fluid approximations to analyze the multi-class scheduling problem with ideas borrowed from convex optimization. These approximations are known to perform well for large systems, and we use simulations to validate our proposed policies for small systems. Results: We propose a multi-class time-in-queue policy that prioritizes both across customer classes and within each class using a simple rule and further show that most of the gains of such a policy can be achieved by deviating from within-class FCFS for at most one customer class. In addition, for systems with exponential patience times, our policy reduces to a simple priority-based policy, which we prove is asymptotically optimal for Markovian systems with an optimality gap that does not grow with system scale. Managerial implications: Our work provides managers ways of improving quality of service to manage parallel server queueing systems. We propose easy-to-implement policies that perform well relative to reasonable benchmarks. Our work also adds to the academic literature on multi-class queueing systems by demonstrating the joint benefits of cross- and within-class prioritization. Funding: A. Bassamboo received financial support from the National Science Foundation [Grant CMMI 2006350]. C. (A.) Wu received financial support from the Hong Kong General Research Fund [Early Career Scheme, Project 26206419]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1190 .
{"title":"Optimally Scheduling Heterogeneous Impatient Customers","authors":"Achal Bassamboo, Ramandeep Randhawa, Chenguang (Allen) Wu","doi":"10.1287/msom.2023.1190","DOIUrl":"https://doi.org/10.1287/msom.2023.1190","url":null,"abstract":"Problem definition: We study scheduling multi-class impatient customers in parallel server queueing systems. At the time of arrival, customers are identified as being one of many classes, and the class represents the service and patience time distributions as well as cost characteristics. From the system’s perspective, customers of the same class at time of arrival get differentiated on their residual patience time as they wait in queue. We leverage this property and propose two novel and easy-to-implement multi-class scheduling policies. Academic/practical relevance: Scheduling multi-class impatient customers is an important and challenging topic, especially when customers’ patience times are nonexponential. In these contexts, even for customers of the same class, processing them under the first-come, first-served (FCFS) policy is suboptimal. This is because, at time of arrival, the system only knows the overall patience distribution from which a customer’s patience value is drawn, and as time elapses, the estimate of the customer’s residual patience time can be further updated. For nonexponential patience distributions, such an update indeed reveals additional information, and using this information to implement within-class prioritization can lead to additional benefits relative to the FCFS policy. Methodology: We use fluid approximations to analyze the multi-class scheduling problem with ideas borrowed from convex optimization. These approximations are known to perform well for large systems, and we use simulations to validate our proposed policies for small systems. Results: We propose a multi-class time-in-queue policy that prioritizes both across customer classes and within each class using a simple rule and further show that most of the gains of such a policy can be achieved by deviating from within-class FCFS for at most one customer class. In addition, for systems with exponential patience times, our policy reduces to a simple priority-based policy, which we prove is asymptotically optimal for Markovian systems with an optimality gap that does not grow with system scale. Managerial implications: Our work provides managers ways of improving quality of service to manage parallel server queueing systems. We propose easy-to-implement policies that perform well relative to reasonable benchmarks. Our work also adds to the academic literature on multi-class queueing systems by demonstrating the joint benefits of cross- and within-class prioritization. Funding: A. Bassamboo received financial support from the National Science Foundation [Grant CMMI 2006350]. C. (A.) Wu received financial support from the Hong Kong General Research Fund [Early Career Scheme, Project 26206419]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1190 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135449538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jan Vlachy, Turgay Ayer, Mehmet Ayvaci, Srinivasan Raghunathan
Problem definition: Under the prevailing fee-for-service (FFS) payments, hospitals receive a fixed payment, whereas physicians receive separate fees for each treatment or procedure performed for a given diagnosis. Under FFS, incentives of hospitals and physicians are misaligned, leading to large inefficiencies. Bundled payments (BP), an alternative to FFS unifying payments to the hospital and physicians, are expected to encourage care coordination and reduce ever increasing healthcare costs. However, as hospitals differ in their relationships with physicians in influencing care (level of physician integration), the expected effects of bundling in hospital systems with a varying level of physician integration remains unclear. Academic/practical relevance: There is a lack of both academic and practical understanding of hospitals’ and physicians’ bundling incentives. Our study builds on and expands the recent operations management literature on alternative payment models. Methodology: We formulate game-theoretic models to study (1) the impact of the level of integration between the hospital and physicians in the uptake of BP and (2) the consequences of bundling with respect to overall care quality and costs/savings across the spectrum of integration levels. Results: We find that (1) hospitals with low to moderate levels of physician integration have more incentives to bundle as compared with hospitals with high physician integration; (2) to engage physicians, hospitals need to financially incentivize them, a mechanism that was not available in traditional FFS-based payment models; (3) when feasible, BP is expected to reduce care intensity, and this reduction in care intensity is expected to result in quality improvement and cost savings in hospital systems with low to moderate level of physician integration; (4) however, when bundling happens in hospital systems with a relatively higher level of physician integration, BP may lead to underprovisioning of care and ultimately quality reduction; (5) in an environment where hospitals are also held accountable for quality, the incentives for bundling will be higher for involved parties, yet quality vulnerabilities due to bundling can be exacerbated. Managerial implications: Our findings have important managerial implications for policy makers, payers such as the Center for Medicare and Medicaid Services, and hospitals: (1) policy makers and payers should be aware of and account for potential negative effects of current BP design on a subset of hospital systems, including a possible quality reduction; (2) in deciding whether to enroll in BP, hospitals should consider their level of physician integration and possible implications for quality. Based on our findings, we expect that a widespread use of BP may trigger further market concentration via hospital mergers or service-line closures. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1187 .
{"title":"The Business of Healthcare: The Role of Physician Integration in Bundled Payments","authors":"Jan Vlachy, Turgay Ayer, Mehmet Ayvaci, Srinivasan Raghunathan","doi":"10.1287/msom.2023.1187","DOIUrl":"https://doi.org/10.1287/msom.2023.1187","url":null,"abstract":"Problem definition: Under the prevailing fee-for-service (FFS) payments, hospitals receive a fixed payment, whereas physicians receive separate fees for each treatment or procedure performed for a given diagnosis. Under FFS, incentives of hospitals and physicians are misaligned, leading to large inefficiencies. Bundled payments (BP), an alternative to FFS unifying payments to the hospital and physicians, are expected to encourage care coordination and reduce ever increasing healthcare costs. However, as hospitals differ in their relationships with physicians in influencing care (level of physician integration), the expected effects of bundling in hospital systems with a varying level of physician integration remains unclear. Academic/practical relevance: There is a lack of both academic and practical understanding of hospitals’ and physicians’ bundling incentives. Our study builds on and expands the recent operations management literature on alternative payment models. Methodology: We formulate game-theoretic models to study (1) the impact of the level of integration between the hospital and physicians in the uptake of BP and (2) the consequences of bundling with respect to overall care quality and costs/savings across the spectrum of integration levels. Results: We find that (1) hospitals with low to moderate levels of physician integration have more incentives to bundle as compared with hospitals with high physician integration; (2) to engage physicians, hospitals need to financially incentivize them, a mechanism that was not available in traditional FFS-based payment models; (3) when feasible, BP is expected to reduce care intensity, and this reduction in care intensity is expected to result in quality improvement and cost savings in hospital systems with low to moderate level of physician integration; (4) however, when bundling happens in hospital systems with a relatively higher level of physician integration, BP may lead to underprovisioning of care and ultimately quality reduction; (5) in an environment where hospitals are also held accountable for quality, the incentives for bundling will be higher for involved parties, yet quality vulnerabilities due to bundling can be exacerbated. Managerial implications: Our findings have important managerial implications for policy makers, payers such as the Center for Medicare and Medicaid Services, and hospitals: (1) policy makers and payers should be aware of and account for potential negative effects of current BP design on a subset of hospital systems, including a possible quality reduction; (2) in deciding whether to enroll in BP, hospitals should consider their level of physician integration and possible implications for quality. Based on our findings, we expect that a widespread use of BP may trigger further market concentration via hospital mergers or service-line closures. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1187 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135562991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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