编者按

在本系列文章中，我们对顶刊《Manufacturing & Service Operations Management》于2026年5月份在线发布的文章中进行了精选（共8篇），并总结其基本信息，旨在帮助读者快速洞察行业最新动态。本月M&SOM发文聚焦服务运营中的技术创新、收益管理、零售决策、医疗管理、可持续运营及不确定环境下的库存决策等领域，研究涵盖客户对服务聊天机器人的行为障碍与AI情绪劳动、联合品类优化与营销组合分配、医院利用率的效率区间、竞争下的企业漂绿策略、数据驱动定价等前沿问题，方法涉及在线实验与实地实验、多项Logit模型与近似算法、因果森林、数据驱动优化、Voronoi分区与次梯度算法、分布鲁棒优化等多种先进运营管理研究方法。

文章1

● 题目：Why Are Customers Averse to Service Chatbots?

客户为何厌恶服务聊天机器人？

● 原文链接：https://doi.org/10.1287/msom.2024.1141

● 作者：Evgeny Kagan , Maqbool Dada , Brett Hathaway

● 发布时间：2026-5-5

● 摘要：

Problem definition: Despite rapid advances in artificial intelligence, the adoption and effective use of customer service chatbots remain slow relative to their capabilities. This paper explores the behavioral reasons for these adoption hurdles. Methodology/results: We use incentivized online experiments to study chatbot uptake. The results of these experiments are threefold. First, people respond positively to improvements in chatbot performance; however, the chatbot channel is used less frequently than expected-time minimization would predict. A key driver of this underutilization is reluctance to engage with a gatekeeper process (i.e., a process with an imperfect initial service stage and possible transfer to a second expert service stage—a behavior that we term gatekeeper aversion). Second, we find that gatekeeper aversion can be further amplified by an additional hurdle—algorithm aversion. Third, we find that chatbot adoption decreases when stakes are higher and when the human/algorithmic nature of the server is manipulated with more realism. Managerial implications: We use an illustrative case to show how the behaviors identified in our experiments affect optimal technology investment and staffing levels and how failing to anticipate these behaviors can lead to suboptimal decisions and higher realized costs. More broadly, our results suggest that adding a chatbot to a service system requires rethinking the entire service process, including technology investment, staffing, and queueing policies.

问题定义： 尽管人工智能技术飞速进步，客服聊天机器人的采用和有效使用相较于其能力而言仍显缓慢。本文探究了造成这些采纳障碍的行为原因。

方法/结果： 我们采用有激励的在线实验来研究聊天机器人的使用情况。实验结果有三点。第一，人们对聊天机器人性能的改善反应积极；然而，聊天机器人渠道的使用频率低于以期望时间最小化为目标的预测。导致这种利用不足的一个关键驱动因素是，用户不愿经历“守门人”流程（即一个初始服务阶段不完美、且可能转至第二阶段专家服务的流程——我们将这种行为称为“守门人厌恶”）。第二，我们发现“守门人厌恶”还可能被另一个障碍——算法厌恶——进一步放大。第三，我们发现，当所涉利害关系更高，以及当服务提供者的人类/算法属性以更为真实的方式呈现时，聊天机器人的采用率会下降。

管理启示： 我们通过一个示例案例说明，实验中识别的这些行为如何影响最优技术投资和人员配置水平，以及未能预见这些行为会如何导致次优决策和更高的实际成本。更广泛而言，我们的结果表明，在服务系统中引入聊天机器人，需要重新思考整个服务流程，包括技术投资、人员配置和排队策略。

文章2

● 题目：Artificial Intelligence, Emotional Labor, and Service Operations

人工智能、情绪劳动与服务运营

● 原文链接：https://doi.org/10.1287/msom.2024.1459

● 作者：Zheng Fang, Yuqian Chang , Xueming Luo , Qingsheng Wu , Jaakko Aspara

● 发布时间：2026-5-7

● 摘要：

Problem definition: Emotional labor is increasingly demanded in service operations, placing tremendous psychological strain on human employees and posing challenges to scalability and sustainability. Our study scrutinizes whether artificial intelligence (AI) service bots may tackle these challenges by examining how and when AI’s engagement in emotional labor enhances economic performance in service operations. Methodology/results: We provide causal evidence from a pair of randomized field experiments conducted in partnership with a firm for loan collection service. Results suggest that, compared with human employees, undisclosed AI service agents display the required emotions (both positive and negative) more accurately. However, AI’s advantage of higher emotion display accuracy does not always guarantee better economic performance. For AI to collect more payments from borrowers than human workers, the displayed emotion must be contextually appropriate. Specifically, AI substantially outperforms human workers in debt collection by 49%–94% when the emotion display instructions are suitable for the collection task (i.e., displaying positive emotions to borrowers with minor delinquency but negative emotions to borrowers with repeated delays). However, when the displayed emotions are unsuitable, AI backfires and performs worse than human employees because of its unwavering adherence to inappropriate emotional display instructions. Further, AI’s performance advantages over human agents are amplified when the suitable emotions involve negative (versus positive) valence. We also leverage the machine learning method causal forest to explore heterogeneous treatment effects across customer segments. Managerial implications: Our research suggests that operations managers should deploy AI to reduce frontline employee emotional burnout, develop explicit emotional labor guidelines for AI, and use negative-emotion AI strategically to boost compliance and efficiency. It is also important to identify emotion-intense operational tasks, target AI when it has clear advantages, and set up continuous monitoring and quality control for AI emotional performance.

问题定义： 服务运营中对情绪劳动的需求日益增长，这给人类员工带来了巨大的心理压力，并对可扩展性和可持续性构成了挑战。我们的研究通过考察人工智能服务机器人在情绪劳动中的参与如何以及何时能提升服务运营的经济绩效，来审视AI是否能应对这些挑战。

方法/结果： 我们通过与一家贷款催收公司合作开展的两项随机实地实验，提供了因果证据。结果表明，与人类员工相比，未披露身份的AI服务代理能够更准确地展现所需的情绪（无论是积极还是消极）。然而，AI在情绪展现准确性方面的优势并非总能保证更好的经济绩效。AI要想比人类员工从借款人那里催收到更多款项，所展现的情绪必须与情境相适宜。具体而言，当情绪展现指令与催收任务匹配时（即对轻微逾期的借款人展现积极情绪，而对屡次拖延的借款人展现消极情绪），AI的债务催收绩效显著优于人类员工，幅度达49%至94%。然而，当所展现的情绪不适宜时，由于AI会不折不扣地执行不恰当的情绪展现指令，其效果适得其反，表现逊于人类员工。此外，当适宜的情绪为消极（相较于积极）效价时，AI相较于人类代理的绩效优势会进一步放大。我们还利用机器学习方法因果森林，探究了不同客户群体间处理效应的异质性。

管理启示： 我们的研究表明，运营管理者应部署AI以减少一线员工的情绪耗竭，为AI制定明确的情绪劳动指南，并策略性地使用展现消极情绪的AI来提升合规性和效率。同样重要的是，要识别情绪密集型的运营任务，在AI具有明显优势时加以部署，并对AI的情绪表现建立持续监控和质量控制机制。

文章3

● 题目：Joint Assortment Optimization and Discrete Marketing Mix Allocation

联合品类优化与离散营销组合分配

● 原文链接：https://doi.org/10.1287/msom.2024.1406

● 作者：Shuai Li , Zikun Ye, Xin Chen , Weijun Xie

● 发布时间：2026-5-11

● 摘要：

Problem definition: Assortment selection and marketing mix allocation are critical decisions for retailers, directly influencing consumer choices. In this paper, we propose a multinomial logit (MNL) choice model in which consumer utility is influenced by marketing decisions such as advertising and promotions: a model widely utilized in empirical marketing literature. We then study the joint assortment and marketing mix allocation problem subject to either cardinality constraints or knapsack constraints on marketing mix allocations. Methodologies/results: We prove that the problem under cardinality constraints is already strongly NP-hard and does not admit constant ratio approximation. For the model with cardinality constraints, we provide an optimal ratio approximation algorithm and polynomial-time algorithms for special cases. With a constant number of marketing mix decisions, the problem can be solved using a linear program of polynomial size. Under knapsack constraints, we also provide an optimal ratio approximation algorithm and a fully polynomial-time approximation scheme (FPTAS) for special cases. With a constant number of marketing mixes, the problem admits an optimal polynomial-time approximation scheme (PTAS). Computational experiments with real-world NielsenIQ retail data show significant 2.05% revenue increases using our method over a two-stage “assortment-then-marketing mix allocation” heuristic approach. A complementary experiment using online transaction data from JD.com is also conducted to demonstrate the applicability of our method in online settings. Managerial implications: Our comprehensive numerical experiments across various scenarios demonstrate that neglecting the impact of marketing decisions in assortment selection can lead to a significant decline in profitability. This finding demonstrates the importance of jointly optimizing assortment and marketing mix allocation, particularly when the number of marketing mix decisions significantly exceeds the number of products and when resources for marketing mix allocation are limited.

问题定义： 品类选择与营销组合分配是直接影响消费者选择的零售商关键决策。本文提出一个多项Logit（MNL）选择模型，其中消费者效用受到广告、促销等营销决策的影响——这一模型在实证营销文献中被广泛使用。进而，我们研究了在营销组合分配具有基数约束或背包约束条件下的联合品类与营销组合分配问题。

方法/结果： 我们证明了，在基数约束下该问题已具有强NP难性质，且不存在常数比近似算法。对于具有基数约束的模型，我们提供了一个最优比近似算法以及适用于特殊情形的多项式时间算法。当营销组合决策数量为常数时，该问题可通过一个多项式规模的线性规划求解。在背包约束下，我们同样提供了最优比近似算法以及特殊情形的完全多项式时间近似方案（FPTAS）。当营销组合数量为常数时，该问题存在一个最优的多项式时间近似方案（PTAS）。基于NielsenIQ真实零售数据的计算实验表明，与“先品类后营销组合分配”的两阶段启发式方法相比，我们的方法使收入显著提高了2.05%。我们还利用京东的在线交易数据进行了补充实验，以证明该方法在线上场景的适用性。

管理启示： 我们跨多种场景的全面数值实验表明，在品类选择中忽视营销决策的影响会导致盈利能力的显著下降。这一发现表明，联合优化品类与营销组合分配至关重要，尤其当营销组合决策数量显著超过产品数量，且用于营销组合分配的资源有限时更是如此。

文章4

● 题目：Utilization: Is More Always Better? Zone of Efficiency, Complexity, and Costs of Care

利用率：越高越好吗？效率区间、复杂性与医疗成本

● 原文链接：https://doi.org/10.1287/msom.2023.0702

● 作者：Sriram Thirumalai , Sarv Devaraj

● 发布时间：2026-5-11

● 摘要：

Problem definition: Faced with the growing need to improve access to care, hospitals have been operating at capacity in recent times. We argue, however, that the pursuit of utilization beyond a threshold might come at a cost. We propose that there exists a zone of efficiency within which the costs of care reach the minimum and the pursuit of utilization outside of this zone may be counterproductive. Our second objective is to explicitly incorporate the complexity of care requirements in understanding the relationship between utilization and costs of care. We propose that complexity exacerbates the demands on resources during care delivery and shifts the zone of efficient utilization lower. Methodology/results: Using patient-level data from over 325,338 inpatient discharges in 156 hospitals, we empirically test these propositions. Our findings indicate that the marginal cost curve is nonlinear, revealing a zone of efficiency between 75%–85% utilization, within which costs per patient day are minimized. Departing from this zone (either above or below it) incurs substantial cost penalties. The results also reveal heterogeneity in the cost implications of utilization across departments with varied complexity regimes. Managerial implications: The study findings carry key implications for managers in improving access to care in an efficient manner. Our study informs decision making in this regard by highlighting that more (utilization) is not always better. Furthermore, a one-size-fits-all policy across all departments may be counterproductive. Whereas managers may pursue high utilization in some departments, it may not be prudent in others.

问题定义： 面对日益增长的改善医疗服务可及性的需求，近年来医院一直以满负荷运转。然而，我们认为，追求超过某一阈值的利用率可能付出代价。我们提出存在一个效率区间，在此区间内医疗成本达到最低，而超出此区间追求利用率可能适得其反。我们的第二个目标是将医疗需求的复杂性明确纳入利用率与医疗成本关系的考量中。我们提出，复杂性在医疗服务提供过程中加剧了对资源的需求，并使高效利用率的区间下移。

方法/结果： 利用来自156家医院的325,338例住院出院患者数据，我们对这些命题进行了实证检验。研究结果表明，边际成本曲线呈非线性，揭示出在75%–85%利用率之间存在一个效率区间，在该区间内每患者日成本最小化。偏离此区间（无论高于或低于该区间）都会导致显著的成本惩罚。结果还揭示了在不同复杂性程度的科室之间，利用率对成本影响的异质性。

管理启示： 研究结果对管理者以高效方式改善医疗服务可及性具有重要启示。我们的研究通过强调“更多（利用率）并非总是更好”这一观点，为相关决策提供信息。此外，在所有科室推行一刀切的政策可能适得其反。管理者或许可以在某些科室追求高利用率，但在其他科室这样做可能并不明智。

文章5

● 题目：Greenwashing Under Competition

竞争下的漂绿行为

● 原文链接：https://doi.org/10.1287/msom.2025.0420

● 作者：Liqun Wei , Soraya Fatehi , Anyan Qi , Jianxiong Zhang

● 发布时间：2026-5-18

● 摘要：

Problem definition: Growing consumer awareness of corporate social responsibility (CSR) has motivated firms to invest in CSR initiatives to gain a competitive edge. However, a phenomenon known as greenwashing has emerged, whereby firms exploit observable CSR activities and advertising solely as a marketing tactic. Methodology/results: We develop a game-theoretic model with two types of firms: a socially responsible firm that intrinsically values CSR and a profit-maximizing firm that may engage in action-based or message-based greenwashing by investing in observable CSR activities or CSR advertising, respectively. Consumers are socially minded but face limited information about the firms’ actual CSR type, inferring the type through observable CSR investment and advertising. We examine how the advertising influence and competition intensity affect equilibrium strategies and social welfare. Our findings show that when advertising influence is high, the profit-maximizing firm engages in greenwashing. When it is moderate, the socially responsible firm overinvests in CSR to deter imitation. When it is low, the two firm types naturally separate. Notably, under high transparency, stronger advertising influence can increase both overall CSR activity and social welfare. Moreover, when transparency is low, the profit-maximizing firm strongly prefers greenwashing; in such settings, if competition intensity is low, greenwashing persists, whereas if competition intensity is high, the socially responsible firm responds by overinvesting to prevent greenwashing. Managerial implications: Greenwashing produces both negative and positive outcomes. Although it erodes consumer surplus, it can spur higher CSR activity in a competitive environment, enhancing social welfare. These results suggest that governments and nongovernmental organizations should carefully design CSR transparency measures; their effect on total CSR investment and social welfare can vary depending on the levels of advertising influence and competitive intensity.

问题定义： 消费者对企业社会责任（CSR）意识的日益增强，促使企业投资于CSR活动以获取竞争优势。然而，一种被称为“漂绿”的现象已然出现，即企业将可观察的CSR活动和广告纯粹作为一种营销策略加以利用。

方法/结果： 我们构建了一个博弈论模型，包含两类企业：内在重视CSR的社会责任型企业，以及可能通过投资于可观察的CSR活动或CSR广告而分别从事行动式或信息式漂绿的利润最大化型企业。消费者具有社会意识，但对企业的实际CSR类型信息有限，会通过观察到的CSR投资和广告来推断其类型。我们考察了广告影响力和竞争强度如何影响均衡策略和社会福利。研究发现，当广告影响力高时，利润最大化型企业会进行漂绿。当广告影响力适中时，社会责任型企业会过度投资于CSR以阻止模仿。当广告影响力低时，两类企业自然分离。值得注意的是，在透明度高的条件下，更强的广告影响力能够增加总体CSR活动和社会福利。此外，当透明度低时，利润最大化型企业强烈偏好漂绿；在此情境下，若竞争强度低，漂绿行为将持续；若竞争强度高，社会责任型企业则会通过过度投资来防止漂绿。

管理启示： 漂绿会产生消极和积极两种结果。尽管它侵蚀了消费者剩余，但可在竞争环境中激励更高的CSR活动，从而提升社会福利。这些结果表明，政府和非政府组织应精心设计CSR透明度措施；这些措施对CSR总投资和社会福利的影响会因广告影响力和竞争强度的不同而有所差异。

文章6

● 题目：Data-Driven Pricing for Availability-Based Upgrades Under a Multiple Binary Choice Model with Copula

基于Copula多元二元选择模型的可用性升级数据驱动定价

● 原文链接：https://doi.org/10.1287/msom.2025.0328

● 作者：Övünç Yılmaz , Farbod Ekbatani , Zifeng Zhao , Ruxian Wang , Andrew Vakhutinsky

● 发布时间：2026-5-25

● 摘要：

Problem definition: Intense competition in the travel industry has increasingly shifted focus toward ancillary services, particularly seat upgrades in airlines and room upgrades in hotels. In response to this trend, several innovative solutions have emerged, among which Nor1’s eStandby Upgrade program stands out by offering discounted, availability-based room upgrades. Revenue management for these upgrades is complex because customers may request multiple upgrades, whereas hotels allocate them based on availability and typically grant at most one upgrade per customer. Methodology/results: Partnering with Oracle, which acquired Nor1, we develop a state-of-the-art framework for prediction, pricing, and allocation to maximize total revenue from eStandby upgrades. We first model customer decision making using a novel copula-based multivariate choice model that captures complex dependencies among multiple decisions made by the same customer. Next, we develop efficient pricing and allocation algorithms to address the challenges associated with offering multiple availability-based upgrades and tracking customer requests. Managerial implications: Validated with real-world data and data-driven numerical experiments, our choice model for upgrade requests and algorithms for pricing and allocation demonstrate significant revenue potential by capturing dependencies across customers’ multiple decisions.

问题定义： 旅游行业的激烈竞争使关注点日益转向辅助服务，尤其是航空公司的座位升级和酒店的客房升级。为应对这一趋势，出现了若干创新解决方案，其中Nor1公司的eStandby升级计划通过提供折扣化的、基于可用性的客房升级而脱颖而出。此类升级的收益管理较为复杂，因为顾客可能请求多项升级，而酒店则根据可用性进行分配，且通常每位顾客最多只获准一项升级。

方法/结果： 我们与收购了Nor1的甲骨文公司合作，开发了一套先进的预测、定价和分配框架，以最大化eStandby升级的总收入。首先，我们采用一种新颖的基于Copula的多元选择模型对顾客决策进行建模，该模型能捕捉同一顾客多项决策之间复杂的依赖关系。随后，我们开发了高效的定价与分配算法，以应对提供多项基于可用性的升级以及追踪顾客请求所带来的挑战。

管理启示： 经真实数据和数据驱动的数值实验验证，我们的升级请求选择模型以及定价与分配算法，通过捕捉顾客多项决策间的依赖关系，展现出显著的增收潜力。

文章7

● 题目：Equitable Delivery Zoning for Last-Mile Logistics: A Framework Validated with Implementation

最后一公里物流的公平配送分区：一个经实施验证的框架

● 原文链接：https://doi.org/10.1287/msom.2024.1221

● 作者：John Gunnar Carlsson , Stanley Frederick W. T. Lim , Sheng Liu , Han Yu

● 发布时间：2026-5-26

● 摘要：

Problem definition: Parcel logistics companies use zoning systems to manage last-mile delivery operations. This practice divides a service area into zones, each served by its own station and drivers. Designing an optimal zoning policy is challenging because the practical service area often includes many customer locations, and vehicle routing problems (VRPs) should be incorporated as a subroutine. Existing methods have limitations in modeling practical fleets with diverse vehicle types and broader routing objectives. Methodology/results: We collaborated with a delivery company to develop a novel data-driven zoning method that minimizes the maximum work span of delivery stations. We define the work span of a station as the duration between the start time of sorting the first parcel and the return time of the last driver upon finishing all assigned delivery tasks. Our method iteratively solves VRPs using observed demand data and partitions the region with additively weighted Voronoi diagrams. We leverage the primal-dual properties and develop a subgradient algorithm with established convergence conditions. Our numerical analyses show that this approach not only reduces the station-level maximum average work span by 20.5% and the average delivery time per driver by 17%, but it also reduces their standard deviations by approximately 25% and 19%, respectively. When tested in actual field conditions, we continue to observe reductions in the work span of the stations and the delivery time of the drivers. Managerial implications: Our approach reduces delivery lead times, better distributes workload among drivers, and limits long working hours, creating a win–win outcome for both the company and its drivers. Besides improving service quality and driver well-being, we estimate annual savings of nearly half a million dollars simply by readjusting the boundaries of service zones. The proposed framework can also be applied in other spatial service settings to achieve equitable distribution of workload among resources.

问题定义： 包裹物流公司采用分区系统来管理最后一公里配送作业。该做法将服务区域划分为若干分区，每个分区由其自有站点和司机提供服务。设计最优分区策略颇具挑战，因为实际服务区域通常包含众多客户位置，且需将车辆路径问题（VRP）作为子程序纳入考虑。现有方法在建模包含多种车辆类型和更广泛路径目标的实际车队方面存在局限。

方法/结果： 我们与一家快递公司合作，开发了一种新颖的数据驱动分区方法，旨在最小化各配送站点的最大工作时长。我们将站点工作时长定义为从分拣第一件包裹开始，到最后一名司机完成所有指派配送任务并返回的时间间隔。该方法利用观察到的需求数据迭代求解VRP，并通过加法加权Voronoi图对区域进行划分。我们借助原始-对偶性质，开发了一种具有已确立收敛条件的次梯度算法。数值分析表明，该方法不仅将站点级最大平均工作时长降低了20.5%，司机平均配送时间减少了17%，还使其标准差分别降低了约25%和19%。在实际现场条件测试中，我们持续观察到站点工作时长和司机配送时间的减少。

管理启示： 我们的方法缩短了配送交付时间，更均衡地在司机间分配工作负荷，并限制了过长的工作时长，为公司和司机创造了双赢。除提升服务质量和司机福祉外，我们估计仅通过重新调整服务区域边界，每年即可节省近50万美元。所提出的框架还可应用于其他空间服务场景，以实现工作负荷在资源间的公平分配。

文章8

● 题目：Newsvendor Under Ambiguity and Misspecification

模糊性与模型误设下的报童问题

● 原文链接：https://doi.org/10.1287/msom.2025.0197

● 作者：Feng Liu , Zhi Chen , Ruodu Wang , Shuming Wang

● 发布时间：2026-5-27

● 摘要：

Problem definition: We consider a newsvendor problem with unknown demand distribution, where we distinguish ambiguity under which the newsvendor does not differentiate demand distributions of common characteristics (e.g., mean and variance) and misspecification under which such characteristics might be misspecified (because of, e.g., estimation error and/or distribution shift). Methodology/results: The newsvendor hedges against ambiguity and misspecification by maximizing the worst-case expected profit regularized by a distribution’s distance to an ambiguity set of distributions with some specified characteristics. Focusing on the popular mean-variance ambiguity set and optimal-transport cost for the misspecification, we show that the decision criterion of misspecification aversion possesses insightful interpretations as distributional transforms. We derive the closed-form optimal order quantity that generalizes the solution of the seminal Scarf model under only ambiguity aversion. We establish finite-sample performance guarantees that consist of two parts: an in-sample optimal value and an out-of-sample effect of misspecification, which can be further decoupled into estimation error and distribution shift. We also extend the framework to multiple products, distributional characteristics specified via optimal transport, and misspecification measured by total variation distance and derive analytical optimal solutions. Managerial implications: The closed-form solution highlights the impact of misspecification aversion; the optimal order quantity under misspecification aversion can decrease as the price or variance increases, reversing the monotonicity of that under only ambiguity aversion. Hence, ambiguity and misspecification, as different layers of distributional uncertainty, can result in distinct operational consequences. The finite-sample performance guarantee theoretically justifies the need to incorporate misspecification aversion in a nonstationary environment, as demonstrated in our experiments with real-world data.

问题定义： 我们考虑一个需求分布未知的报童问题，其中我们区分了模糊性（报童无法区分具有共同特征（如均值和方差）的需求分布）和模型误设（由于估计误差和/或分布偏移等原因，这些特征可能被错误设定）。

方法/结果： 报童通过最大化最坏情况下的期望利润来对冲模糊性和误设，该利润经分布到某个具有指定特征的模糊集的距离正则化。针对常用的均值-方差模糊集和用于误设的最优传输成本，我们证明了误设厌恶的决策准则具有深刻的分布变换解释。我们推导出了闭合形式的最优订货量，它推广了仅存在模糊性厌恶下经典的Scarf模型解。我们建立了有限样本下的性能保证，该保证由两部分组成：样本内最优值以及误设的样本外效应，后者可进一步分解为估计误差和分布偏移。我们还将该框架扩展到多产品、通过最优传输指定的分布特征以及用全变差距离度量误设的情形，并推导了解析最优解。

管理启示： 闭合形式解凸显了误设厌恶的影响；在误设厌恶下，最优订货量可能随价格或方差的上升而下降，这与仅在模糊性厌恶下的单调性相反。因此，模糊性和误设作为分布不确定性的不同层面，可能导致截然不同的运营后果。有限样本性能保证从理论上证明，在非平稳环境中纳入误设厌恶的必要性，我们使用真实数据的实验也证实了这一点。