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学者姓名：阳成虎
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Abstract ：
When dealing with short-life cycle products, small and medium-sized enterprises (SMEs) commonly confront challenges stemming from limited local censored demand data. This often leads to a lack of comprehensive understanding of demand distribution and can result in suboptimal order decisions. To address this issue, we introduce a data-driven newsvendor framework that combines a novel cost-driven data correction procedure with distributionally robust optimization (CDDC-DRO). With cost minimization objectives, the proposed procedure integrates local censored demand data and external demand information to adaptively generate highvalue improved censored datasets, while circumventing reliance on static correlations. Furthermore, we consider the granularities of external demand information and propose three DRO-based data correction strategies to effectively reduce demand censoring. Tests on both simulated and actual data indicate that the CDDCDRO procedure adaptively corrects censored data based on demand characteristics and cost structures, thereby eliminating significant errors induced by demand censoring and improving the precision and robustness of order decisions. The correction degree of the improved censored datasets dynamically depends on cost structure. A high degree of data correction is employed under high critical ratios, whereas a minimal correction degree is applied under low critical ratios. In response to the significant negative impacts of demand censoring, SMEs prefer to implement the DRO-based data correction strategy with finer-grained external demand information. This strategy enhances correction capabilities while minimizing variations in decision accuracy. Even when finergrained external demand information is unavailable, SMEs are able to make well-informed order decisions using the DRO-based data correction strategy with local censored demand data.
Keyword ：
Censored data correction Censored data correction Cost-driven Cost-driven Distributionally robust optimization Distributionally robust optimization Information fusion Information fusion
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| GB/T 7714 | Su, Xiaoli , Yuan, Zhe , Yang, Chenghu et al. Bridging uncertainty: A data-driven DRO approach for correcting censored demand in newsvendor problems [J]. | INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS , 2025 , 285 . |
| MLA | Su, Xiaoli et al. "Bridging uncertainty: A data-driven DRO approach for correcting censored demand in newsvendor problems" . | INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 285 (2025) . |
| APA | Su, Xiaoli , Yuan, Zhe , Yang, Chenghu , Sahin, Evren , Xiong, Jie . Bridging uncertainty: A data-driven DRO approach for correcting censored demand in newsvendor problems . | INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS , 2025 , 285 . |
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Objective: Diabetic retinopathy (DR) is one of the leading causes of vision loss and early diagnosis is crucial to prevent blindness. Diagnosing DR is time-consuming and labor-intensive, resulting in a high risk of misdiagnosis within resource-constrained clinical environment. Automated diagnostic models offer promising solutions for rapid DR identification, precise severity stratification, and streamlined reporting timelines. However, the clinical application may be significantly constrained when deal with data that is cross-center, cross-population, or class-imbalanced. Methods: We propose the Grade-Skewed Domain Adaptation Network with Coordinate and Category Attention (G2C-Net) model, which integrates domain adaptation, spatial channel attention mechanisms, and category attention to improve the diagnostic accuracy in cross-center, cross-population and Grade-Skewed (GS) situations. The G2C-Net model is trained and tested using two large datasets of retinal images graded for various stages of DR. The performance is evaluated in precision, sensitivity, F1-score, specificity, and accuracy metrics for each grade of DR images on both the source and target domains. Results: By comparing performance of DR grading among different models, the G2C-Net model achieves the best evaluation metrics on both the source domain and the target domain. Specifically, the G2C-Net model improves precision, sensitivity, F1-score, specificity and accuracy for DR grading by 23.7%, 21.1%, 24.3%, 6.0%, and 7.5% respectively on the source domain, and 37.9%, 30.7%, 31.1%, 11.9%, and 6.1% respectively on the target domain. Conclusion: The G2C-Net model significantly reduces class-imbalance-induced grading errors, addresses cross-domain distribution shifts in clinical data, and achieves superior performance in early diagnosis of DR.
Keyword ：
Deep learning Deep learning Diabetic retinopathy Diabetic retinopathy Domain adaptation Domain adaptation Grade skewed Grade skewed
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| GB/T 7714 | Lin, Binlong , Nie, Delong , Wu, Xiaoyuan et al. G2C-Net: Grade-Skewed domain adaptation network with coordinate and category attention for diabetic retinopathy grading [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2025 , 110 . |
| MLA | Lin, Binlong et al. "G2C-Net: Grade-Skewed domain adaptation network with coordinate and category attention for diabetic retinopathy grading" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 110 (2025) . |
| APA | Lin, Binlong , Nie, Delong , Wu, Xiaoyuan , Shen, Ximei , Yang, Chenghu . G2C-Net: Grade-Skewed domain adaptation network with coordinate and category attention for diabetic retinopathy grading . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2025 , 110 . |
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Reusing electric vehicles (EV) batteries that reach the end of their useful first life is an environmental and cost -competitive option; however, the process of recycling EV batteries is not yet mature. Due to complex electrochemical reactions and physical conditions, the quality of used EV batteries (cores) is highly uncertain. The remanufacturer needs to make the acquisition decision under quality distributional ambiguity. Perfect quality distribution of cores cannot be known to the remanufacturer in practice. We develop distributionally robust optimization models based on phi -divergence measures and the imprecise Dirichlet model (DRO-IDM) to derive robust decisions. First, we find that the bounds of quality probability intervals are identified solely based on the collected data by introducing the imprecise Dirichlet model. The derived finite -sample boundary can reduce the scope of the uncertainty set and avoid the no -direction search issue. Second, our models can hedge against distributional uncertainty, reduce the probability of a robust solution that deviates from the optimal solution, and correct bias in decision making. Third, we extend the DRO-IDM to develop data -driven models, that can reassess the value of multisource quality information to improve the estimation accuracy of core quality and maximize the remanufacturer's profit. Our study provides new insights for remanufacturers: the new remanufacturing process proposed in our work can assist remanufacturers in utilizing the values of cores without disassembly; the information -aware algorithm can offer the remanufacturing sector a valuable tool for efficiently filtering out invalid information in optimizing acquisition decisions; this capability empowers decision -makers to leverage multiple sources of information and expedite the process of digital transformation in remanufacturing; our approach can also provide a manner of integrating information fusion and distribution learning into remanufacturing.
Keyword ：
Data-driven model Data-driven model Decision analysis Decision analysis Distributionally robust optimization Distributionally robust optimization Multisource information Multisource information Quality uncertainty Quality uncertainty
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| GB/T 7714 | Yang, Cheng-Hu , Su, Xiao-Li , Ma, Xin et al. A data-driven distributionally robust optimization approach for the core acquisition problem [J]. | EUROPEAN JOURNAL OF OPERATIONAL RESEARCH , 2024 , 318 (1) : 253-268 . |
| MLA | Yang, Cheng-Hu et al. "A data-driven distributionally robust optimization approach for the core acquisition problem" . | EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 318 . 1 (2024) : 253-268 . |
| APA | Yang, Cheng-Hu , Su, Xiao-Li , Ma, Xin , Talluri, Srinivas . A data-driven distributionally robust optimization approach for the core acquisition problem . | EUROPEAN JOURNAL OF OPERATIONAL RESEARCH , 2024 , 318 (1) , 253-268 . |
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背景 随着人口老龄化进程加快，居民疾病谱变化，以糖尿病为代表的慢性病患病率逐年攀升，亟需建立广覆盖、高效率的基层医防融合模式。已有研究多聚焦于健康管理服务需求及服务采纳的影响因素，鲜有对数字技术下慢病医防融合服务需求进行识别与分析。目的 探索数字健康背 景下居民对糖尿病医防融合服务需求，以及不同服务内容对服务对象接受度与满意度的影响，以期为公众完善全过程、全方位的医防融合服务提供理论依据。方法 结合相关研究与实际工作，确立了 20 项糖尿病医防融合服务需求调查项目，并于 2023 年 1—6 月，采用便利抽样法调查福建省、广东省和云南省的糖尿病患病及风险人群，获取 410 名受访者数据，根据性别、年龄、文化程度、居住地类型和医保类型五类人口学特征，依据 Kano 模型分析法进行属性分类分析，考察不同属性的服务需求与居民满意度的关系，进而提出糖尿病医防融合服务供给策略。结果 不同人口学特征的居民对糖尿病医防融合服务需求显示出共性和个性差异，其中，不同年龄段和文化程度的人群服务需求差异较大。糖尿病防治群体的医防融合服务需求聚焦在筛防和诊疗环节，但互联网与社交媒体提供的相关便捷服务与用户的满意度无关。结论 应当提升糖尿病基层医防融合服务个性化水平，充分满足服务人群的“糖尿病与并发症初步筛查”等必备属性需求，完善“建立全周期个人电子健康档案”等期望属性服务，以及提升“风险预测”“远程健康监测”等魅力属性需求的服务。
Keyword ：
Kano 模型 Kano 模型 医防融合 医防融合 数字健康 数字健康 服务需求 服务需求 糖尿病 糖尿病
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| GB/T 7714 | 吴心怡 , 张永泽 , 阳成虎 et al. 数字健康背景下糖尿病基层医防融合服务的需求研究 [J]. | 中国全科医学 , 2024 . |
| MLA | 吴心怡 et al. "数字健康背景下糖尿病基层医防融合服务的需求研究" . | 中国全科医学 (2024) . |
| APA | 吴心怡 , 张永泽 , 阳成虎 , 吴晓园 . 数字健康背景下糖尿病基层医防融合服务的需求研究 . | 中国全科医学 , 2024 . |
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在不同再制造渠道结构下,再制造产品对新产品的需求不仅产生异质的竞食效应,还可能对新产品感知价值造成抑制或促进影响.为了探索这两种影响如何共同作用于制造/再制造系统的决策,研究由原始设备制造商(OEM)与第三方再制造商(IO)主导再制造业务的两类结构下的制造/再制造系统生产与定价问题,得到了相应的均衡生产和定价决策的解析表达式,分析了抑制(或促进)作用对供应链各成员决策过程的影响.同时,还通过算例分析中的数值仿真的方法,深入分析了两类不同再制造渠道结构下竞食效应和抑制(或促进)作用对供应链成员利润的综合影响.研究结果表明,当OEM主导再制造业务时,再制造产品对新产品感知价值的抑制作用与内部竞食效应叠加,会降低新产品的需求,OEM与零售商的利润会随抑制作用的增强而持续下降.当抑制作用增强到一定程度时,再制造的成本优势不足以弥补新产品需求下降带来的利润损失,OEM将选择只生产新产品.当IO主导再制造业务时,再制造产品对新产品感知价值的促进作用会缓解外部竞食效应带来的负面影响.OEM、IO和零售商的利润会随促进作用的增强而上升.此外,OEM忽略再制造渠道结构对新产品感知价值的抑制和促进作用,将高估外部竞食效应的负面影响.当抑制作用和促进作用增强到一定程度时,IO主导再制造业务可能会更有利于再制造系统的发展.
Keyword ：
再制造 再制造 原始设备制造商 原始设备制造商 感知价值 感知价值 生产决策 生产决策 零售商 零售商
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| GB/T 7714 | 阳成虎 , 江雨薇 , 苏晓丽 . 再制造渠道结构对制造/再制造系统生产与定价决策的影响分析 [J]. | 福州大学学报（哲学社会科学版） , 2024 , 38 (5) : 41-52 . |
| MLA | 阳成虎 et al. "再制造渠道结构对制造/再制造系统生产与定价决策的影响分析" . | 福州大学学报（哲学社会科学版） 38 . 5 (2024) : 41-52 . |
| APA | 阳成虎 , 江雨薇 , 苏晓丽 . 再制造渠道结构对制造/再制造系统生产与定价决策的影响分析 . | 福州大学学报（哲学社会科学版） , 2024 , 38 (5) , 41-52 . |
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基层医疗服务质量低下是阻碍基本医疗均等化的重要原因.医联体的有效建设可以推进优质医疗资源下沉、提升基层医疗服务能力,进而提升社会整体医疗服务效率.构建由三级医院和基层医院组成的医联体,运用Hotelling模型考察医联体成员提升基层医疗服务质量的努力决策与患者回流的均衡结果,探究政府不同补贴情形下,医联体各成员医院最优努力策略的变化及对医疗市场均衡绩效产生的影响.研究表明:医联体能够提高基层医疗机构的服务质量,促进患者回流基层,但是质量提升及患者就诊秩序改善的程度与基层医院初始医疗质量水平正相关;政府补贴通过影响医院的绩效成本比来促进各医院质量努力提升,从而降低医院的运营成本,但成本降低程度会随着各医院绩效成本比的比值差异而有所不同;政府补贴医联体内任一成员,补贴比例相同且患者就诊的质量偏好大于单位距离成本时,政府的补贴比例应当高于门槛阈值,才能使全社会健康剩余较政府不补贴时有所提升.
Keyword ：
医联体 医联体 基本医疗服务 基本医疗服务 政府补贴 政府补贴 服务质量努力 服务质量努力
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| GB/T 7714 | 吴晓园 , 章洋洋 , 李晓超 et al. 基于患者选择行为的医联体服务质量努力与协调研究 [J]. | 运筹与管理 , 2024 , 33 (11) : 226-232 . |
| MLA | 吴晓园 et al. "基于患者选择行为的医联体服务质量努力与协调研究" . | 运筹与管理 33 . 11 (2024) : 226-232 . |
| APA | 吴晓园 , 章洋洋 , 李晓超 , 阳成虎 . 基于患者选择行为的医联体服务质量努力与协调研究 . | 运筹与管理 , 2024 , 33 (11) , 226-232 . |
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In intelligent manufacturing systems, the industrial informatics has features of multi-source, multi-noise, and time series. It is difficult for small and medium enterprises (SMEs) to directly exploit the enormous amounts of data due to the limited budgets and computing capabilities. Edge intelligence is a key technique to power intelligent manufacturing systems and provide knowledge transferred from the cloud to SMEs at the edges. To address edge-cloud collaboration issue, we propose a refined data-driven distributionally robust newsvendor model based on & phi;-divergence measures and imprecise Dirichlet models (DRN-IDM). We construct new distributional uncertainty sets by effectively integrating local censored demand data and cloud knowledge, which helps SMEs to make intelligent production decisions and reduce significant decision deviations, even under a small censored data set. In particular, the novel demand uncertainty sets can depict the distance between distributions and probability intervals. Then, we transform the DRN-IDM model into a convex optimization model that is amenable to algorithmic implementation. Additionally, based on the coefficient of variation of limited historical data, we propose an adaptive demand information fusion procedure to achieve excellent synergy effect from cloud knowledge. We also validate the effectiveness of the DRN-IDM model and the practicability of adaptive procedure using extensive numerical studies with both simulated and real-life data. Furthermore, we measure the relative expected value of cloud knowledge and investigate the effect of censored demand samples. Our results verify the effectiveness condition of the DRN-IDM model and indicate that cloud knowledge can improve the precision and robustness of SMEs' production decisions with small-scale censored data. Interestingly, the verified adaptive procedure can be applied in the learning criteria design of metaheuristics in intelligent manufacturing systems, and the reconstructed uncertainty set can narrow the search space to improve the convergence performance of algorithms.
Keyword ：
Data-driven newsvendor problem Data-driven newsvendor problem Distributionally robust optimization Distributionally robust optimization Edge-cloud collaboration Edge-cloud collaboration Edge production Edge production Intelligent manufacturing systems Intelligent manufacturing systems
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| GB/T 7714 | Yang, Cheng-hu , Su, Xiao-li , Wu, Peng . A data-driven distributionally newsvendor problem for edge-cloud collaboration in intelligent manufacturing systems [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2023 , 126 . |
| MLA | Yang, Cheng-hu et al. "A data-driven distributionally newsvendor problem for edge-cloud collaboration in intelligent manufacturing systems" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 126 (2023) . |
| APA | Yang, Cheng-hu , Su, Xiao-li , Wu, Peng . A data-driven distributionally newsvendor problem for edge-cloud collaboration in intelligent manufacturing systems . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2023 , 126 . |
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In this paper, a data-driven newsvendor problem is studied by mapping high-dimensional and mixed-frequency features of historical data to replenishment decisions. Instead of relying on a known demand distribution, we propose using machine learning algorithms that incorporate demand features into the replenishment decisions to solve single-and multi-product newsvendor problems. In particular, our algorithms simultaneously optimize the demand estimation and replenishment decisions. To extract key features from the historical data, we propose a frequency alignment method to transform high-dimensional mixed-frequency data and historical data into the same frequency. We then propose two variable selection policies based on the empirical risk minimization principle, and employ the regularization method to tackle the parameter proliferation issue. In addition, a feature-based machine learning algorithm is designed to solve the multi-product newsvendor problem. Finally, we numerically justify the performances of proposed machine learning algorithms. We find that (1) data-informed replenishment decisions can effectively leverage the identified key demand features to avoid losing demand information, and (2) the optimal replenishment quantity behaves robustly and shows minimal variation across different cost structures. Our work provides meaningful insights for newsvendors making replenishment decisions under stochastic demand.
Keyword ：
Data-driven Data-driven Machine learning Machine learning Mixed-frequency data Mixed-frequency data Newsvendor Newsvendor Variables selection Variables selection
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| GB/T 7714 | Yang, Cheng-Hu , Wang, Hai-Tang , Ma, Xin et al. A data-driven newsvendor problem: A high-dimensional and mixed-frequency method [J]. | INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS , 2023 , 266 . |
| MLA | Yang, Cheng-Hu et al. "A data-driven newsvendor problem: A high-dimensional and mixed-frequency method" . | INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 266 (2023) . |
| APA | Yang, Cheng-Hu , Wang, Hai-Tang , Ma, Xin , Talluri, Srinivas . A data-driven newsvendor problem: A high-dimensional and mixed-frequency method . | INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS , 2023 , 266 . |
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在人口老龄化纵深发展过程中,老年人慢性疾病高发频发,康复医疗作为保持或恢复老年人身体机能的有效手段,存在庞大的市场需求.该文采用扎根分析方法研究老年人康复医疗体系的影响因素,通过对政策制定者、行业专家、老年人的半结构化访谈,从产业层、个体层、社会层三个方面阐述老年人康复医疗体系需求影响理论模型的作用机制,以期为福建省主动应对老龄化挑战,提升老年健康服务水平,加快推进老年人康复医疗工作发展提供参考.
Keyword ：
康复医疗 康复医疗 影响因素 影响因素 扎根理论 扎根理论 理论框架 理论框架
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| GB/T 7714 | 林彬龙 , 王发圣 , 阳成虎 . 福建省老年人康复医疗体系构建的影响因素研究 [J]. | 海峡科学 , 2023 , (3) : 99-103 . |
| MLA | 林彬龙 et al. "福建省老年人康复医疗体系构建的影响因素研究" . | 海峡科学 3 (2023) : 99-103 . |
| APA | 林彬龙 , 王发圣 , 阳成虎 . 福建省老年人康复医疗体系构建的影响因素研究 . | 海峡科学 , 2023 , (3) , 99-103 . |
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该文从先天性信任、历史性信任、角色能力信任、社会规则信任、社会范畴信任5 个维度出发,运用AHP法和熵权法组合确定新能源汽车动力电池闭环供应链成员间信任程度影响因素的综合权重,测算样本信任度,并通过RFECV法识别出18 个关键影响因素,丰富了供应链信任理论,为提升供应链整体信任水平提供参考,对于促进新能源动力电池闭环供应链成员间合作具有指导意义.
Keyword ：
信任合作 信任合作 影响因素 影响因素 新能源动力电池 新能源动力电池 闭环供应链 闭环供应链
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| GB/T 7714 | 温红林 , 林彬龙 , 阳成虎 . 新能源动力电池闭环供应链成员间信任影响因素研究 [J]. | 海峡科学 , 2023 , (11) : 52-57 . |
| MLA | 温红林 et al. "新能源动力电池闭环供应链成员间信任影响因素研究" . | 海峡科学 11 (2023) : 52-57 . |
| APA | 温红林 , 林彬龙 , 阳成虎 . 新能源动力电池闭环供应链成员间信任影响因素研究 . | 海峡科学 , 2023 , (11) , 52-57 . |
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