Problem definition: We consider a platform’s problem of how to staff its operations given the possibilities of hiring employees and setting up a contractor marketplace. We aim to understand the operational difference between these two work arrangement models. Methodology/results: We consider a model where demand is not only stochastic but also evolving over time, which we capture via a state of the world that determines the demand distribution. In the case of employees, the platform controls the number of employee hours it uses for serving demand, whereas in the case of contractors, it sets the wage paid to them per utilized hour. We show that although the employee problem is equivalent to a standard newsvendor, the contractor one corresponds to an unusual version of the newsvendor model where utilization is the control variable. Managerial implications: This distinction makes the contractor model more flexible, allowing us to prove that it performs significantly better, especially if the order of magnitude of demand is unknown. Meanwhile, hybrid solutions that combine both employees and contractors have complex optimal solutions and offer relatively limited benefits relative to a contractor marketplace. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: This research was partially supported by the National Natural Science Foundation of China [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0029 .
问题定义：我们考虑的是一个平台在雇佣员工和建立承包商市场的可能性下如何为其运营配备人员的问题。我们的目标是了解这两种工作安排模式之间的运作差异。方法/结果：我们考虑的模型是，需求不仅是随机的，而且随着时间的推移而不断变化，我们通过决定需求分布的世界状态来捕捉这种变化。对于雇员，平台控制用于满足需求的雇员小时数，而对于承包商，平台设定支付给他们的每小时工资。我们的研究表明，虽然雇员问题等同于标准的新闻供应商问题，但承包商问题则对应于新闻供应商模型的一个不同寻常的版本，即利用率是控制变量。对管理的影响：这种区别使承包商模型更加灵活，使我们能够证明它的性能明显更好，尤其是在需求数量级未知的情况下。同时，结合了雇员和承包商的混合解决方案具有复杂的最优解，与承包商市场相比，其优势相对有限。历史：本文已被 "制造与服务运营管理运营前沿"（Manufacturing & Service Operations Management Frontiers in Operations Initiative）收录。资助：本研究得到国家自然科学基金[批准号：71821002]的部分资助。补充材料：在线附录见 https://doi.org/10.1287/msom.2023.0029 。
{"title":"Frontiers in Operations: Employees vs. Contractors: An Operational Perspective","authors":"Ilan Lobel, Sébastien Martin, Haotian Song","doi":"10.1287/msom.2023.0029","DOIUrl":"https://doi.org/10.1287/msom.2023.0029","url":null,"abstract":"Problem definition: We consider a platform’s problem of how to staff its operations given the possibilities of hiring employees and setting up a contractor marketplace. We aim to understand the operational difference between these two work arrangement models. Methodology/results: We consider a model where demand is not only stochastic but also evolving over time, which we capture via a state of the world that determines the demand distribution. In the case of employees, the platform controls the number of employee hours it uses for serving demand, whereas in the case of contractors, it sets the wage paid to them per utilized hour. We show that although the employee problem is equivalent to a standard newsvendor, the contractor one corresponds to an unusual version of the newsvendor model where utilization is the control variable. Managerial implications: This distinction makes the contractor model more flexible, allowing us to prove that it performs significantly better, especially if the order of magnitude of demand is unknown. Meanwhile, hybrid solutions that combine both employees and contractors have complex optimal solutions and offer relatively limited benefits relative to a contractor marketplace. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: This research was partially supported by the National Natural Science Foundation of China [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0029 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"22 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140963386","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}
Problem definition: In practice, consumers are directly or indirectly connected with branded manufacturers through intermediaries across industries. In this paper, we explore the effects of different selling formats on product quality and price depending on consumer valuations in a market. We employ a distribution family to comprehensively capture the heterogeneity of consumer valuations. Motivated by realistic phenomena, consumer valuations are used to investigate strategic decisions under different selling formats that are not trivial to analyze. Methodology/results: We develop game-theoretical models to examine the equilibrium decisions of stakeholders. The impact of consumer valuations is investigated and validated using sensitivity analysis, and the results are connected to practice. First, we find that agency selling induces a premium quality and maximizes the channel profit; remarkably, a nonmonotonic (approximate U-shaped) relationship exists between the agency fee and consumer valuations. A higher consumer surplus can be achieved in an agency selling channel compared with a reselling channel, particularly when targeting a mass of high-end consumers. Second, by examining distinct consumer valuations, maintaining top-notch quality and the highest price in an agency selling channel is not universally viable under some conditions. Third, in the case of production-level competition, an agency selling format tends to cause product quality to vary noticeably. Moreover, in the hybrid selling channel, in contrast to agency selling, the high-type manufacturer reduces both quality and price, which bolsters the overall profits of the channel and the consumer surplus. Managerial implications: Branded manufacturers can efficiently respond to individualized consumer needs in a centralized distribution channel. In contrast, for selling basic products, the reselling channel could contribute to achieving economies of scale and offering competitive prices. In the agency selling channel, standardized pricing determined by branded manufacturers can create a consistent perception of product quality throughout the distribution network. Funding: The research of L. Hsiao was supported by Ministry of Science and Technology (now National Science and Technology Council), Grant/Award Number: MOST 110-2410-H-005-016-MY3. The research of Y.-J. Chen was supported by the HK RGC General Research Fund [GRF 16500821, GRF 16501722, and HKUST C6020-21GF]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0470 .
问题定义：在实践中，消费者通过跨行业的中间商直接或间接地与品牌制造商建立联系。在本文中，我们探讨了不同销售形式对产品质量和价格的影响，这取决于市场中消费者的评价。我们采用分布族来全面捕捉消费者估值的异质性。在现实现象的激励下，我们利用消费者估值来研究不同销售形式下的战略决策，而这些战略决策的分析并不简单。方法/结果：我们建立了博弈论模型来研究利益相关者的均衡决策。通过敏感性分析研究和验证了消费者估值的影响，并将结果与实践相结合。首先，我们发现代理销售会诱导溢价质量，并使渠道利润最大化；值得注意的是，代理费和消费者估值之间存在非单调（近似 U 型）关系。与转售渠道相比，代理销售渠道可以获得更高的消费者剩余，尤其是在面向大批高端消费者时。其次，通过研究不同的消费者价值，在某些条件下，在代理销售渠道中保持一流的质量和最高的价格并不是普遍可行的。第三，在生产层面竞争的情况下，代理销售形式往往会造成产品质量的明显差异。此外，在混合销售渠道中，与代理销售不同，高类型制造商会同时降低质量和价格，从而提高渠道的整体利润和消费者剩余。管理意义：品牌制造商可以在集中的销售渠道中有效地满足消费者的个性化需求。相反，对于基本产品的销售，转售渠道有助于实现规模经济，并提供有竞争力的价格。在代理销售渠道中，由品牌制造商确定的标准化定价可以在整个分销网络中形成一致的产品质量观念。资金来源L. Hsiao 的研究得到了科技部（现为国家科学技术委员会）的支持，拨款/奖励编号为Most 110-2410-H-005-016-MY3。陈永杰的研究得到香港研资局一般研究基金[GRF 16500821, GRF 16501722, and HKUST C6020-21GF]的资助。补充材料：在线附录见 https://doi.org/10.1287/msom.2022.0470 。
{"title":"The Effects of Selling Formats and Upstream Competition on Product Pricing and Quality Design","authors":"L. Hsiao, Xin Ma, Ying‐Ju Chen","doi":"10.1287/msom.2022.0470","DOIUrl":"https://doi.org/10.1287/msom.2022.0470","url":null,"abstract":"Problem definition: In practice, consumers are directly or indirectly connected with branded manufacturers through intermediaries across industries. In this paper, we explore the effects of different selling formats on product quality and price depending on consumer valuations in a market. We employ a distribution family to comprehensively capture the heterogeneity of consumer valuations. Motivated by realistic phenomena, consumer valuations are used to investigate strategic decisions under different selling formats that are not trivial to analyze. Methodology/results: We develop game-theoretical models to examine the equilibrium decisions of stakeholders. The impact of consumer valuations is investigated and validated using sensitivity analysis, and the results are connected to practice. First, we find that agency selling induces a premium quality and maximizes the channel profit; remarkably, a nonmonotonic (approximate U-shaped) relationship exists between the agency fee and consumer valuations. A higher consumer surplus can be achieved in an agency selling channel compared with a reselling channel, particularly when targeting a mass of high-end consumers. Second, by examining distinct consumer valuations, maintaining top-notch quality and the highest price in an agency selling channel is not universally viable under some conditions. Third, in the case of production-level competition, an agency selling format tends to cause product quality to vary noticeably. Moreover, in the hybrid selling channel, in contrast to agency selling, the high-type manufacturer reduces both quality and price, which bolsters the overall profits of the channel and the consumer surplus. Managerial implications: Branded manufacturers can efficiently respond to individualized consumer needs in a centralized distribution channel. In contrast, for selling basic products, the reselling channel could contribute to achieving economies of scale and offering competitive prices. In the agency selling channel, standardized pricing determined by branded manufacturers can create a consistent perception of product quality throughout the distribution network. Funding: The research of L. Hsiao was supported by Ministry of Science and Technology (now National Science and Technology Council), Grant/Award Number: MOST 110-2410-H-005-016-MY3. The research of Y.-J. Chen was supported by the HK RGC General Research Fund [GRF 16500821, GRF 16501722, and HKUST C6020-21GF]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0470 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"59 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140979110","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}
Pub Date : 2024-05-01DOI: 10.1287/msom.2024.ack.v26.n3
{"title":"Acknowledgments to Editors and Reviewers (2023)","authors":"","doi":"10.1287/msom.2024.ack.v26.n3","DOIUrl":"https://doi.org/10.1287/msom.2024.ack.v26.n3","url":null,"abstract":"","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141030292","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}
Problem definition: Content promotion policies are crucial for online content platforms to improve content consumption and user engagement. However, traditional promotion policies generally neglect the diffusion effect within a crowd of users. In this paper, we study the candidate generation and promotion optimization (CGPO) problem for an online content platform, emphasizing the incorporation of the diffusion effect. Methodology/results: We propose a diffusion model that incorporates platform promotion decisions to characterize the adoption process of online content. Based on this diffusion model, we formulate the CGPO problem as a mixed-integer program with nonconvex and nonlinear constraints, which is proved to be NP-hard. Additionally, we investigate methods for estimating the diffusion model parameters using available online platform data and introduce novel double ordinary least squares (D-OLS) estimators. We prove the submodularity of the objective function for the CGPO problem, which enables us to find an efficient [Formula: see text]-approximation greedy solution. Furthermore, we demonstrate that the D-OLS estimators are consistent and have smaller asymptotic variances than traditional ordinary least squares estimators. By utilizing real data from a large-scale video-sharing platform, we show that our diffusion model effectively characterizes the adoption process of online content. Compared with the policy implemented on the platform, our proposed promotion policy increases total adoptions by 49.90%. Managerial implications: Our research highlights the essential role of diffusion in online content and provides actionable insights for online content platforms to optimize their content promotion policies by leveraging our diffusion model. Funding: R. Zhang is grateful for the financial support from the Hong Kong Research Grants Council General Research Fund [Grants 14502722 and 14504123] and the National Natural Science Foundation of China [Grant 72293560/72293565]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0172 .
{"title":"Content Promotion for Online Content Platforms with the Diffusion Effect","authors":"Yunduan Lin, Mengxin Wang, Heng Zhang, Renyu Zhang, Zuo-Jun Max Shen","doi":"10.1287/msom.2022.0172","DOIUrl":"https://doi.org/10.1287/msom.2022.0172","url":null,"abstract":"Problem definition: Content promotion policies are crucial for online content platforms to improve content consumption and user engagement. However, traditional promotion policies generally neglect the diffusion effect within a crowd of users. In this paper, we study the candidate generation and promotion optimization (CGPO) problem for an online content platform, emphasizing the incorporation of the diffusion effect. Methodology/results: We propose a diffusion model that incorporates platform promotion decisions to characterize the adoption process of online content. Based on this diffusion model, we formulate the CGPO problem as a mixed-integer program with nonconvex and nonlinear constraints, which is proved to be NP-hard. Additionally, we investigate methods for estimating the diffusion model parameters using available online platform data and introduce novel double ordinary least squares (D-OLS) estimators. We prove the submodularity of the objective function for the CGPO problem, which enables us to find an efficient [Formula: see text]-approximation greedy solution. Furthermore, we demonstrate that the D-OLS estimators are consistent and have smaller asymptotic variances than traditional ordinary least squares estimators. By utilizing real data from a large-scale video-sharing platform, we show that our diffusion model effectively characterizes the adoption process of online content. Compared with the policy implemented on the platform, our proposed promotion policy increases total adoptions by 49.90%. Managerial implications: Our research highlights the essential role of diffusion in online content and provides actionable insights for online content platforms to optimize their content promotion policies by leveraging our diffusion model. Funding: R. Zhang is grateful for the financial support from the Hong Kong Research Grants Council General Research Fund [Grants 14502722 and 14504123] and the National Natural Science Foundation of China [Grant 72293560/72293565]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0172 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"3 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140247806","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}
Problem definition: We study the operations of electric vehicles (EVs) providing frequency regulation services to the electric grid in vehicle-to-grid (V2G) systems. In particular, individually owned EVs collaboratively bid in the regulation market, coordinated by a platform that operates the network of charging equipment. We study how the platform determines optimal pricing incentives for drivers to plug in their EVs, accounting for heterogeneous driving schedules. Methodology/results: We model the platform’s pricing optimization problem as a bilevel program: At the upper level, the platform determines hourly rebates for EV owners to plug in their EVs and capacity bids in the regulation market; at the lower level, individual travelers optimize their travel and charging schedules in response to pricing incentives. To account for uncertainties and heterogeneity in regulation market prices and travel patterns, we adopt distributionally robust optimization techniques to formulate the problem as a mixed-integer second-order cone program. We conduct a computational study based on the California Household Travel Survey data set and actual frequency regulation prices. Our results show that the ability to offer time-varying rebates and install workplace chargers can significantly improve the V2G platform’s expected profits. Managerial implications: As EV adoption progresses past the nascent stage, V2G business models become more viable. Successful implementation of V2G provides economic incentive for switching to EVs, potentially helps sustain adoption growth, and complements the growth of renewable power by helping stabilize the grid. Our findings shed light on the design of driver incentives for V2G systems. Funding: R. Tang acknowledges support from the National Natural Science Foundation of China [Grant 72201206]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0133 .
{"title":"Collaborative Vehicle-to-Grid Operations in Frequency Regulation Markets","authors":"Ho‐Yin Mak, Runyu Tang","doi":"10.1287/msom.2022.0133","DOIUrl":"https://doi.org/10.1287/msom.2022.0133","url":null,"abstract":"Problem definition: We study the operations of electric vehicles (EVs) providing frequency regulation services to the electric grid in vehicle-to-grid (V2G) systems. In particular, individually owned EVs collaboratively bid in the regulation market, coordinated by a platform that operates the network of charging equipment. We study how the platform determines optimal pricing incentives for drivers to plug in their EVs, accounting for heterogeneous driving schedules. Methodology/results: We model the platform’s pricing optimization problem as a bilevel program: At the upper level, the platform determines hourly rebates for EV owners to plug in their EVs and capacity bids in the regulation market; at the lower level, individual travelers optimize their travel and charging schedules in response to pricing incentives. To account for uncertainties and heterogeneity in regulation market prices and travel patterns, we adopt distributionally robust optimization techniques to formulate the problem as a mixed-integer second-order cone program. We conduct a computational study based on the California Household Travel Survey data set and actual frequency regulation prices. Our results show that the ability to offer time-varying rebates and install workplace chargers can significantly improve the V2G platform’s expected profits. Managerial implications: As EV adoption progresses past the nascent stage, V2G business models become more viable. Successful implementation of V2G provides economic incentive for switching to EVs, potentially helps sustain adoption growth, and complements the growth of renewable power by helping stabilize the grid. Our findings shed light on the design of driver incentives for V2G systems. Funding: R. Tang acknowledges support from the National Natural Science Foundation of China [Grant 72201206]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0133 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"120 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140250480","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}
Xiaoqiang Cai, Daniel Zhuoyu Long, Gen Yu, Lianmin Zhang
Problem definition: We consider a multiportfolio optimization problem in which nonlinear market impact costs result in a strong dependency of one account’s performance on the trading activities of the other accounts. Methodology/results: We develop a novel target-oriented model that jointly optimizes the rebalancing trades and the split of market impact costs. The key advantages of our proposed model include the consideration of clients’ targets on investment returns and the incorporation of distributional uncertainty. The former helps fund managers to circumvent the difficulty in identifying clients’ utility functions or risk parameters, whereas the latter addresses a practical challenge that the probability distribution of risky asset returns cannot be fully observed. Specifically, to evaluate the quality of multiple portfolios’ investment payoffs in achieving targets, we propose a new class of performance measures, called fairness-aware multiparticipant satisficing (FMS) criteria. These criteria can be extended to encompass distributional uncertainty and have the salient feature of addressing the fairness issue with the collective satisficing level as determined by the least satisfied participant. We find that, structurally, the FMS criteria have a dual connection with a set of risk measures. For multiportfolio optimization, we consider the FMS criterion with conditional value-at-risk being the underlying risk measure to further account for the magnitude of shortfalls against targets. The resulting problem, although nonconvex, can be solved efficiently by solving an equivalent converging sequence of tractable subproblems. Managerial implications: For the multiportfolio optimization problem, the numerical study shows that our approach outperforms utility-based models in achieving targets and in out-of-sample performance. More generally, the proposed FMS criteria provide a new decision framework for operational problems in which the decision makers are target-oriented rather than being utility maximizers and issues of fairness and ambiguity should be considered. Funding: This work was supported by the Hong Kong Research Grants Council [Grants 14210821, 16204521], Leading Talent Program of Guangdong Province [Grant 2016LJ06D703], and the National Natural Science Foundation of China [Grants 71971187, 72171156, 72231002, 72331009]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0363 .
{"title":"Multiportfolio Optimization: A Fairness-Aware Target-Oriented Model","authors":"Xiaoqiang Cai, Daniel Zhuoyu Long, Gen Yu, Lianmin Zhang","doi":"10.1287/msom.2021.0363","DOIUrl":"https://doi.org/10.1287/msom.2021.0363","url":null,"abstract":"Problem definition: We consider a multiportfolio optimization problem in which nonlinear market impact costs result in a strong dependency of one account’s performance on the trading activities of the other accounts. Methodology/results: We develop a novel target-oriented model that jointly optimizes the rebalancing trades and the split of market impact costs. The key advantages of our proposed model include the consideration of clients’ targets on investment returns and the incorporation of distributional uncertainty. The former helps fund managers to circumvent the difficulty in identifying clients’ utility functions or risk parameters, whereas the latter addresses a practical challenge that the probability distribution of risky asset returns cannot be fully observed. Specifically, to evaluate the quality of multiple portfolios’ investment payoffs in achieving targets, we propose a new class of performance measures, called fairness-aware multiparticipant satisficing (FMS) criteria. These criteria can be extended to encompass distributional uncertainty and have the salient feature of addressing the fairness issue with the collective satisficing level as determined by the least satisfied participant. We find that, structurally, the FMS criteria have a dual connection with a set of risk measures. For multiportfolio optimization, we consider the FMS criterion with conditional value-at-risk being the underlying risk measure to further account for the magnitude of shortfalls against targets. The resulting problem, although nonconvex, can be solved efficiently by solving an equivalent converging sequence of tractable subproblems. Managerial implications: For the multiportfolio optimization problem, the numerical study shows that our approach outperforms utility-based models in achieving targets and in out-of-sample performance. More generally, the proposed FMS criteria provide a new decision framework for operational problems in which the decision makers are target-oriented rather than being utility maximizers and issues of fairness and ambiguity should be considered. Funding: This work was supported by the Hong Kong Research Grants Council [Grants 14210821, 16204521], Leading Talent Program of Guangdong Province [Grant 2016LJ06D703], and the National Natural Science Foundation of China [Grants 71971187, 72171156, 72231002, 72331009]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0363 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"116 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140079505","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}
Problem definition: We study a robust screening problem where a seller attempts to sell a product to a buyer knowing only the moment and support information of the buyer’s valuation distribution. The objective is to maximize the competitive ratio relative to an optimal hindsight policy equipped with full valuation information. Methodology/results: We formulate the robust screening problem as a linear programming problem, which can be solved efficiently if the support of the buyer’s valuation is finite. When the support of the buyer’s valuation is continuous and the seller knows the mean and the upper and lower bounds of the support for the buyer’s valuation, we show that the optimal payment is a piecewise polynomial function of the valuation with a degree of at most two. Moreover, we derive the closed-form competitive ratio corresponding to the optimal mechanism. The optimal mechanism can be implemented by a randomized pricing mechanism whose price density function is a piecewise inverse function adjusted by a constant. When the mean and variance are known to the seller, we propose a feasible piecewise polynomial approximation of the optimal payment function with a degree of at most three. We also demonstrate that the optimal competitive ratio exhibits a logarithmic decay with respect to the coefficient of variation of the buyer’s valuation distribution. Managerial implications: Our general framework provides an approach to investigating the value of moment information in the robust screening problem. We establish that even a loose upper bound of support or a large variance can guarantee a good competitive ratio. Funding: The research of S. Liu is partly supported by the National Natural Science Foundation of China [Grant NSFC-72072117]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0072 .
问题定义：我们研究的是一个稳健筛选问题，即卖方在只知道买方估值分布的时刻和支持信息的情况下，试图向买方出售产品。我们的目标是，相对于具备完整估值信息的最优后见之明政策，最大化竞争比率。方法/结果：我们将稳健筛选问题表述为一个线性规划问题，如果买方估值的支持度是有限的，这个问题就可以有效解决。当买方估值的支持是连续的，且卖方知道买方估值的平均值和支持的上下限时，我们证明最优付款是估值的一次多项式函数，度数最多为 2。此外，我们还推导出了与最优机制相对应的闭式竞争比率。最优机制可以通过随机定价机制来实现，该机制的价格密度函数是一个由常数调整的片断反函数。当卖方已知均值和方差时，我们提出了一个可行的片断多项式近似最优支付函数，度数最多为三。我们还证明，最优竞争比率与买方估值分布的变异系数呈对数衰减关系。管理意义：我们的一般框架为研究稳健筛选问题中时刻信息的价值提供了一种方法。我们发现，即使是宽松的支持上限或较大的方差也能保证良好的竞争比率。资助：S. Liu 的研究得到了国家自然科学基金[NSFC-72072117]的部分资助。补充材料：在线附录见 https://doi.org/10.1287/msom.2023.0072 。
{"title":"Minimax Regret Robust Screening with Moment Information","authors":"Shixin Wang, Shaoxuan Liu, Jiawei Zhang","doi":"10.1287/msom.2023.0072","DOIUrl":"https://doi.org/10.1287/msom.2023.0072","url":null,"abstract":"Problem definition: We study a robust screening problem where a seller attempts to sell a product to a buyer knowing only the moment and support information of the buyer’s valuation distribution. The objective is to maximize the competitive ratio relative to an optimal hindsight policy equipped with full valuation information. Methodology/results: We formulate the robust screening problem as a linear programming problem, which can be solved efficiently if the support of the buyer’s valuation is finite. When the support of the buyer’s valuation is continuous and the seller knows the mean and the upper and lower bounds of the support for the buyer’s valuation, we show that the optimal payment is a piecewise polynomial function of the valuation with a degree of at most two. Moreover, we derive the closed-form competitive ratio corresponding to the optimal mechanism. The optimal mechanism can be implemented by a randomized pricing mechanism whose price density function is a piecewise inverse function adjusted by a constant. When the mean and variance are known to the seller, we propose a feasible piecewise polynomial approximation of the optimal payment function with a degree of at most three. We also demonstrate that the optimal competitive ratio exhibits a logarithmic decay with respect to the coefficient of variation of the buyer’s valuation distribution. Managerial implications: Our general framework provides an approach to investigating the value of moment information in the robust screening problem. We establish that even a loose upper bound of support or a large variance can guarantee a good competitive ratio. Funding: The research of S. Liu is partly supported by the National Natural Science Foundation of China [Grant NSFC-72072117]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0072 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140438961","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}
Problem definition: We study stochastic periodic-review inventory systems with lost sales, where the decision maker has no access to the true demand distribution a priori and can only observe historical sales data (referred to as censored demand) and feature information about the demand. In an inventory system, excess demand is unobservable because of inventory constraints, and sales data alone cannot fully recover the true demand. Meanwhile, feature information about the demand is abundant to assist inventory decisions. We incorporate features for inventory systems with censored demand. Methodology/results: We propose two feature-based inventory algorithms called the feature-based adaptive inventory algorithm and the dynamic shrinkage algorithm. Both algorithms are based on the stochastic gradient descent method. We measure the performance of the proposed algorithms through the average expected regret in finite periods: that is, the difference between the cost of our algorithms and that of a clairvoyant optimal policy with access to information, which is acting optimally. We show that the average expected cost incurred under both algorithms converges to the clairvoyant optimal cost at the rate of [Formula: see text] for the perishable inventory case and [Formula: see text] for the nonperishable inventory case. The feature-based adaptive inventory algorithm results in high volatility in the stochastic gradients, which hampers the initial performance of regret. The dynamic shrinkage algorithm uses a shrinkage parameter to adjust the gradients, which significantly improves the initial performance. Managerial implications: This paper considers feature information. The idea of dynamic shrinkage for the stochastic gradient descent method builds on a fundamental insight known as the bias-variance trade-off. Our research shows the importance of incorporating the bias-variance in a dynamic environment for inventory systems with feature information. Funding: W. T. Huh acknowledges support from the NSERC Discovery Grants [Grant RGPIN 2020-04213] and the Canada Research Chair Program. The work of Y. Rong was supported by the National Natural Science Foundation of China [Grants 72025201, 72331006, and 72221001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0135 .
问题定义：我们研究的是具有销售损失的随机定期回顾库存系统，在这种系统中，决策者无法事先获得真实的需求分布，只能观察历史销售数据（称为删减需求）和需求特征信息。在库存系统中，由于库存限制，超额需求是不可观测的，仅凭销售数据无法完全恢复真实需求。同时，关于需求的特征信息非常丰富，可以帮助库存决策。我们为有删减需求的库存系统加入了特征信息。方法/结果：我们提出了两种基于特征的库存算法，分别称为基于特征的自适应库存算法和动态收缩算法。这两种算法都基于随机梯度下降法。我们通过有限期间内的平均预期遗憾来衡量所提算法的性能：即我们算法的成本与获取信息的 "千里眼 "最优策略的成本之间的差额。我们的研究表明，在两种算法下产生的平均预期成本，在易腐烂库存情况下以[公式：见正文]的速度收敛到千里眼最优成本，在非易腐烂库存情况下以[公式：见正文]的速度收敛到千里眼最优成本。基于特征的自适应库存算法会导致随机梯度的高波动性，从而影响后悔的初始性能。动态收缩算法使用收缩参数来调整梯度，从而显著改善了初始性能。管理意义：本文考虑了特征信息。随机梯度下降法的动态收缩思想建立在一个称为偏差-方差权衡的基本见解之上。我们的研究表明，将偏差-方差纳入动态环境对具有特征信息的库存系统非常重要。资助：W. T. Huh 感谢国家科学和技术研究中心（NSERC）发现基金[Grant RGPIN 2020-04213] 和加拿大研究教席计划（Canada Research Chair Program）的支持。Y. Rong 的工作得到了国家自然科学基金[Grants 72025201, 72331006, and 72221001]的支持。补充材料：在线附录见 https://doi.org/10.1287/msom.2021.0135 。
{"title":"Feature-Based Inventory Control with Censored Demand","authors":"Jingying Ding, W. T. Huh, Ying Rong","doi":"10.1287/msom.2021.0135","DOIUrl":"https://doi.org/10.1287/msom.2021.0135","url":null,"abstract":"Problem definition: We study stochastic periodic-review inventory systems with lost sales, where the decision maker has no access to the true demand distribution a priori and can only observe historical sales data (referred to as censored demand) and feature information about the demand. In an inventory system, excess demand is unobservable because of inventory constraints, and sales data alone cannot fully recover the true demand. Meanwhile, feature information about the demand is abundant to assist inventory decisions. We incorporate features for inventory systems with censored demand. Methodology/results: We propose two feature-based inventory algorithms called the feature-based adaptive inventory algorithm and the dynamic shrinkage algorithm. Both algorithms are based on the stochastic gradient descent method. We measure the performance of the proposed algorithms through the average expected regret in finite periods: that is, the difference between the cost of our algorithms and that of a clairvoyant optimal policy with access to information, which is acting optimally. We show that the average expected cost incurred under both algorithms converges to the clairvoyant optimal cost at the rate of [Formula: see text] for the perishable inventory case and [Formula: see text] for the nonperishable inventory case. The feature-based adaptive inventory algorithm results in high volatility in the stochastic gradients, which hampers the initial performance of regret. The dynamic shrinkage algorithm uses a shrinkage parameter to adjust the gradients, which significantly improves the initial performance. Managerial implications: This paper considers feature information. The idea of dynamic shrinkage for the stochastic gradient descent method builds on a fundamental insight known as the bias-variance trade-off. Our research shows the importance of incorporating the bias-variance in a dynamic environment for inventory systems with feature information. Funding: W. T. Huh acknowledges support from the NSERC Discovery Grants [Grant RGPIN 2020-04213] and the Canada Research Chair Program. The work of Y. Rong was supported by the National Natural Science Foundation of China [Grants 72025201, 72331006, and 72221001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0135 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"12 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140441010","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}
Herbie Huang, Nur Sunar, Jayashankar M. Swaminathan, Rahul Roy
Problem definition: Customer reviews are essential to online marketplaces. However, reviews typically vary; ratings of a product or service are rarely the same. In many service marketplaces, including the ones for solar panel installations, supply-side participants are active. That is, a seller must make a proposal to serve each customer. In such marketplaces, it is not clear how (or if) the dispersion in customer reviews affects the seller activity level and number of matches in the marketplace. Our paper examines this by considering both ratings and text reviews. To our knowledge, this is the first paper that empirically studies how the review dispersion affects a seller’s activity level and the number of matches in an online marketplace with active sellers. Distinct from literature, we examine the relationship between the review dispersion and supply-side activities in an online service marketplace. Methodology/results: We collaborated with one of the largest online solar marketplaces in the United States that connects potential solar panel adopters with installers. We obtained a unique data set from the marketplace for 2013 − 2018. We complement this with public data sets. Our analysis uses traditional econometrics methods, a clustering method, and the deep-learning-based natural-language-processing model BERT developed by Google AI. We find that the dispersion in customer reviews has a significant and inverted U-shaped relationship with an installer’s marketplace activity level. Intuitively, a marketplace operator would favor having more sellers with perfect ratings. In contrast, we identify a significant and inverted U-shaped relationship between the market-level review dispersion and transactions. Managerial implications: Our paper provides key insights to marketplace operators and sellers. We find that in contrast to general belief, an operator can improve its market transactions by keeping/promoting sellers with low ratings or avoiding (negative) review filtering. Furthermore, sellers’ implementation of “rating gating” to avoid negative reviews may backfire for them by reducing their matches. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0104 .
{"title":"Do Noisy Customer Reviews Discourage Platform Sellers? Empirical Analysis of an Online Solar Marketplace","authors":"Herbie Huang, Nur Sunar, Jayashankar M. Swaminathan, Rahul Roy","doi":"10.1287/msom.2021.0104","DOIUrl":"https://doi.org/10.1287/msom.2021.0104","url":null,"abstract":"Problem definition: Customer reviews are essential to online marketplaces. However, reviews typically vary; ratings of a product or service are rarely the same. In many service marketplaces, including the ones for solar panel installations, supply-side participants are active. That is, a seller must make a proposal to serve each customer. In such marketplaces, it is not clear how (or if) the dispersion in customer reviews affects the seller activity level and number of matches in the marketplace. Our paper examines this by considering both ratings and text reviews. To our knowledge, this is the first paper that empirically studies how the review dispersion affects a seller’s activity level and the number of matches in an online marketplace with active sellers. Distinct from literature, we examine the relationship between the review dispersion and supply-side activities in an online service marketplace. Methodology/results: We collaborated with one of the largest online solar marketplaces in the United States that connects potential solar panel adopters with installers. We obtained a unique data set from the marketplace for 2013 − 2018. We complement this with public data sets. Our analysis uses traditional econometrics methods, a clustering method, and the deep-learning-based natural-language-processing model BERT developed by Google AI. We find that the dispersion in customer reviews has a significant and inverted U-shaped relationship with an installer’s marketplace activity level. Intuitively, a marketplace operator would favor having more sellers with perfect ratings. In contrast, we identify a significant and inverted U-shaped relationship between the market-level review dispersion and transactions. Managerial implications: Our paper provides key insights to marketplace operators and sellers. We find that in contrast to general belief, an operator can improve its market transactions by keeping/promoting sellers with low ratings or avoiding (negative) review filtering. Furthermore, sellers’ implementation of “rating gating” to avoid negative reviews may backfire for them by reducing their matches. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0104 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134205748","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}
Problem definition: We study a procurement problem, where the supplier holds superior cost information and can learn to improve efficiency over time. Despite its prevalence, the supply chain literature provides limited guidance on how to manage learning suppliers with evolving private information. Methodology/results: We use mechanism design. We show that supplier learning has both efficiency and agency effects, it can induce countervailing incentives, and the agency effect can overwhelm the efficiency effect. As a result, (i) supplier learning can hurt profits, (ii) information asymmetry can improve efficiency, (iii) production distortion can go upward, and (iv) ignoring the agency effect of learning can mislead contract design and inflict severe losses. Managerial implications: Our results suggest that previous studies may have overlooked the downside of learning and overestimated the harm of information asymmetry. Moreover, our results help explain when and why firms should overproduce output and disclose private information voluntarily. By highlighting the strategic role of supplier learning, this study sharpens our understanding of supply chain management. Funding: L. Gao is partly supported by the CoR research grant at University of California, Riverside. W. Zhang is partly supported by the National Natural Science Foundation of China [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0285 .
{"title":"The Strategic Role of Supplier Learning","authors":"Long Gao, M. E. Nikoofal, Wei Zhang","doi":"10.1287/msom.2021.0285","DOIUrl":"https://doi.org/10.1287/msom.2021.0285","url":null,"abstract":"Problem definition: We study a procurement problem, where the supplier holds superior cost information and can learn to improve efficiency over time. Despite its prevalence, the supply chain literature provides limited guidance on how to manage learning suppliers with evolving private information. Methodology/results: We use mechanism design. We show that supplier learning has both efficiency and agency effects, it can induce countervailing incentives, and the agency effect can overwhelm the efficiency effect. As a result, (i) supplier learning can hurt profits, (ii) information asymmetry can improve efficiency, (iii) production distortion can go upward, and (iv) ignoring the agency effect of learning can mislead contract design and inflict severe losses. Managerial implications: Our results suggest that previous studies may have overlooked the downside of learning and overestimated the harm of information asymmetry. Moreover, our results help explain when and why firms should overproduce output and disclose private information voluntarily. By highlighting the strategic role of supplier learning, this study sharpens our understanding of supply chain management. Funding: L. Gao is partly supported by the CoR research grant at University of California, Riverside. W. Zhang is partly supported by the National Natural Science Foundation of China [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0285 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116756553","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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