Supply Chain Analytics最新文献_第7页

An optimization framework for emergency supply chains prioritizing elderly populations during pandemics 大流行期间优先考虑老年人的应急供应链优化框架

Supply Chain Analytics

Pub Date : 2025-05-20 DOI: 10.1016/j.sca.2025.100131

Behzad Mosallanezhad , Neale R. Smith , Fatemeh Gholian-Jouybari , Mostafa Hajiaghaei-Keshteli

Pandemics have severely disrupted supply chains, making it challenging to meet the demands of the elderly and other vulnerable populations. This study addresses the importance of developing a sustainable emergency supply chain network that ensures timely and fair resource allocation for elderly communities. Therefore, an age-structured Susceptible-Infected-Recovered (SIR) system dynamics framework is utilized to simulate pandemic development and estimate age-specific demand for highly-demand items. Then, a multi-objective stochastic mathematical model is proposed to optimize cost, decrease unfulfilled demand, and reduce environmental effects. A numerical example inspired by the recent COVID-19 pandemic in Mexico is introduced, which focuses on the distribution of personal protective equipment (PPE), medical supplies, and test kits to hospitals, pharmacies, and other demand points. This approach couples the estimated demand from the system dynamics model and then optimizes the stochastic model. The results present optimal decisions for allocation, inventory, product flow, distribution, and waste management under different scenarios. A sensitivity analysis for the demand parameter is also performed, showing that total cost, unmet demand, and environmental effects increase as demand rises. The study demonstrates the model's capacity to enhance supply chain resilience and adaptability, providing valuable insights to improve emergency responses for at-risk populations.

大流行严重扰乱了供应链，使满足老年人和其他弱势群体的需求变得困难。本研究探讨了发展可持续的应急供应链网络的重要性，以确保及时和公平地为老年社区分配资源。因此，使用年龄结构的易感-感染-康复（SIR）系统动力学框架来模拟大流行的发展并估计对高需求物品的特定年龄需求。在此基础上，提出了优化成本、减少未满足需求和降低环境影响的多目标随机数学模型。本文介绍了一个受最近墨西哥COVID-19大流行启发的数值例子，该例子侧重于向医院、药房和其他需求点分发个人防护装备（PPE）、医疗用品和检测包。该方法将系统动力学模型中的估计需求耦合起来，对随机模型进行优化。结果给出了在不同场景下分配、库存、产品流、分配和废物管理的最佳决策。对需求参数进行敏感性分析，表明总成本、未满足需求和环境影响随着需求的增加而增加。该研究证明了该模型增强供应链弹性和适应性的能力，为改善风险人群的应急响应提供了有价值的见解。

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引用次数: 0

Optimization-based model of a circular supply chain for coffee waste 基于优化的咖啡废弃物循环供应链模型

Supply Chain Analytics

Pub Date : 2025-04-30 DOI: 10.1016/j.sca.2025.100126

Hanieh Zohourfazeli , Ali Sabaghpourfard , Amin Chaabane , Armin Jabbarzadeh

Spent coffee grounds (SCG) waste poses significant environmental challenges, including greenhouse gas emissions and contamination risks. However, the existing reverse logistics (RL) systems remain inefficient, costly, and prone to contamination. Although previous studies have explored RL strategies, economically viable logistics models for small-scale SCG operations remain underdeveloped. However, the role of digitalization in optimizing SCG collection has not yet been explored. This study addresses these gaps by developing and evaluating sustainable business models that integrate circular economy principles with Industry 4.0. A mixed-integer linear programming (MILP) model was formulated to optimize the location, allocation, and routing decisions for “circular coffee shops, ” which serve as local collection and preprocessing nodes. Using real data from 1000 coffee shops in Montreal, three case scenarios were analyzed to assess the impact of pre-drying technologies and smart logistics on cost reduction and environmental performance. The results show that, while smart bins and real-time data analytics improve network efficiency and sustainability, the strategic placement of pre-drying technologies significantly reduces transportation and processing costs. By introducing a novel framework that integrates digitalization and collaborative waste management, this study advances SCG valorization and minimizes waste-related environmental impact. The findings offer actionable strategies for municipalities and food service stakeholders, providing a scalable, data-driven approach to promote the adoption of circular economy principles in urban organic waste management.

废弃咖啡渣（SCG）废弃物对环境构成了重大挑战，包括温室气体排放和污染风险。然而，现有的逆向物流（RL）系统仍然效率低下，成本高，容易污染。虽然以前的研究已经探索了RL策略，但小规模SCG作业的经济可行的物流模型仍然不发达。然而，数字化在优化SCG收集中的作用尚未得到探索。本研究通过开发和评估将循环经济原则与工业4.0相结合的可持续商业模式来解决这些差距。制定了混合整数线性规划（MILP）模型来优化“圆形咖啡店”的位置、分配和路由决策，作为本地收集和预处理节点。利用蒙特利尔1000家咖啡店的真实数据，分析了三种情况，以评估预干燥技术和智能物流对降低成本和环境绩效的影响。结果表明，虽然智能垃圾箱和实时数据分析提高了网络效率和可持续性，但预干燥技术的战略性部署显著降低了运输和加工成本。通过引入一个集成数字化和协同废物管理的新框架，本研究推进了SCG的增值，并最大限度地减少了与废物相关的环境影响。研究结果为市政当局和食品服务利益相关者提供了可行的战略，提供了一种可扩展的、数据驱动的方法，以促进在城市有机废物管理中采用循环经济原则。

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引用次数: 0

A bibliometric analysis of industry 5.0 and healthcare supply chain research: Emerging opportunities and future challenges 工业5.0和医疗保健供应链研究的文献计量分析：新出现的机会和未来的挑战

Supply Chain Analytics

Pub Date : 2025-04-25 DOI: 10.1016/j.sca.2025.100125

Rajesh Matha , Subhodeep Mukherjee , Rashmi Ranjan Panigrahi , Avinash K Shrivastava

This paper explores the emerging field of Industry 5.0 in the context of healthcare supply chains (HSC). It aims to improve resilience, sustainability, and efficiency through human-centred techniques and cutting-edge technology. This study focuses on HSC management and emerging technologies such as artificial intelligence (AI), the Internet of Things (IoT), and robotics. Using a bibliometric analysis of 142 academic publications, this paper identifies key publication trends, significant research contributions, and thematic clusters. The results show a steady increase in research interest since 2018, with a growth rate of 15 % year-on-year in publications and contributions from 20 countries, led by the United States of America, China and the United Kingdom. These suggest implementing Industry 5.0 technology to optimize operational processes, improve demand forecasting, and advance sustainable practices. Identified topic clusters highlight key aspects such as decision support systems, sustainability, resilience, and technological integration, demonstrating the potential of Industry 5.0 to transform healthcare logistics. Integrating human expertise with intelligent systems, Industry 5.0 addresses healthcare delivery challenges while ensuring high-quality patient care. Future research can build on this study’s contributions to explore the intersection of HSC management and technological advancements.

本文探讨了医疗保健供应链（HSC）背景下工业5.0的新兴领域。它旨在通过以人为本的技术和尖端技术提高弹性、可持续性和效率。本研究的重点是HSC管理和新兴技术，如人工智能（AI）、物联网（IoT）和机器人技术。通过对142份学术出版物的文献计量学分析，本文确定了主要的出版趋势、重要的研究贡献和专题集群。结果显示，自2018年以来，研究兴趣稳步增长，以美国、中国和英国为首的20个国家的出版物和贡献量同比增长15% %。这些建议采用工业5.0技术来优化操作流程，改进需求预测，并推进可持续实践。确定的主题集群突出了决策支持系统、可持续性、弹性和技术集成等关键方面，展示了工业5.0改变医疗保健物流的潜力。工业5.0将人类专业知识与智能系统相结合，解决了医疗保健交付方面的挑战，同时确保了高质量的患者护理。未来的研究可以在本研究的基础上探索HSC管理与技术进步的交集。

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引用次数: 0

An analytics-driven economic order quantity model integrating fuzzy learning for deteriorating imperfect items in sustainable supply chains 基于模糊学习的分析驱动经济订单数量模型

Supply Chain Analytics

Pub Date : 2025-04-22 DOI: 10.1016/j.sca.2025.100120

M. Palanivel , M. Venkadesh , S. Vetriselvi , M. Suganya

This study presents an advanced Economic Order Quantity inventory model that integrates intuitionistic fuzzy sets and fuzzy learning to enhance decision-making under environmental uncertainty. The model systematically incorporates green technology adoption and accounts for the uncertain impact of emerging technologies on carbon emissions. The proposed framework embeds carbon reduction incentives and tax policies into the inventory decision-making process by leveraging real-time data from environmental regulations and technological advancements. Additionally, the study explores the role of fuzzy learning in optimizing supply chain networks, enabling improved environmental performance, and minimizing carbon emissions. Integrating intuitionistic fuzzy sets, fuzzy learning, green technology, and carbon emission reduction strategies provides a mathematically rigorous approach to developing adaptive inventory models that achieve economic efficiency and environmental sustainability. Numerical experiments are validated by MATLAB software. Based on the numerical experiments, sensitivity analyses are performed on key model parameters to validate the effectiveness of the proposed methodology. The findings are further reinforced by computational simulations and mathematical insights, demonstrating the practical applicability and robustness of the model.

本文提出了一种将直觉模糊集与模糊学习相结合的先进经济订货量库存模型，以增强环境不确定性下的决策能力。该模型系统地纳入了绿色技术的采用，并考虑了新兴技术对碳排放的不确定性影响。拟议的框架通过利用来自环境法规和技术进步的实时数据，将碳减排激励措施和税收政策纳入库存决策过程。此外，本研究还探讨了模糊学习在优化供应链网络、改善环境绩效和减少碳排放方面的作用。将直觉模糊集、模糊学习、绿色技术和碳减排策略相结合，提供了一种数学上严谨的方法来开发适应性库存模型，从而实现经济效率和环境可持续性。利用MATLAB软件进行了数值实验验证。在数值实验的基础上，对关键模型参数进行了敏感性分析，验证了该方法的有效性。计算模拟和数学见解进一步强化了这些发现，证明了该模型的实用性和鲁棒性。

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引用次数: 0

A supply chain analytics approach for optimizing milk collection routing in multi-depot networks 在多仓库网络优化牛奶收集路线的供应链分析方法

Supply Chain Analytics

Pub Date : 2025-04-17 DOI: 10.1016/j.sca.2025.100123

Mattia Neroni , Marta Rinaldi

This study presents a supply chain model for optimizing milk collection routing in multi-depot networks. The problem consists of a fleet of vehicles that leaves their depots (i.e., typically the driver’s houses), visits an assigned set of farms to collect the raw milk, and delivers it to the processing plant. This problem has not yet been formulated explicitly in the literature, and it can be classified in the middle between the Team Orienteering Problem (TOP) and the Multi-Depot Vehicle Routing Problem (MDVRP) with heterogeneous vehicles. However, it cannot be reduced to any previously mentioned problems before introducing slight modifications and additional constraints to the mathematical formulation. We introduce a new formulation and propose six heuristic algorithms to minimize the distance covered in milk collection in the dairy sector. The proposed solutions are validated by using new benchmarks and tested in a set of real case applications. Computational experiments on real-life data are performed to investigate the performance of the heuristics varying the milk demand. The results demonstrate the applicability of the proposed approach to the real world and identify the best algorithm in terms of solution quality and computational time.

本研究提出了一个供应链模型，用于优化多仓库网络中的牛奶收集路线。这个问题包括一个车队，这些车队离开他们的仓库（即通常是司机的家），访问指定的农场收集生牛奶，并将其运送到加工厂。这个问题在文献中还没有明确的表述，它可以被分类在团队定向问题（TOP）和多仓库车辆路径问题（MDVRP）之间。然而，在引入对数学公式的轻微修改和附加约束之前，它不能简化为前面提到的任何问题。我们介绍了一个新的公式，并提出了六种启发式算法，以尽量减少牛奶收集在乳制品部门覆盖的距离。通过使用新的基准测试和在一组实际案例应用程序中进行测试，验证了所提出的解决方案。在实际数据上进行了计算实验，以研究启发式方法改变牛奶需求的性能。结果证明了所提出的方法在现实世界中的适用性，并在求解质量和计算时间方面确定了最佳算法。

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引用次数: 0

A meta-analysis assessment of adaptive and transformative approaches to supply chain resilience 供应链弹性的适应性和变革性方法的元分析评估

Supply Chain Analytics

Pub Date : 2025-04-16 DOI: 10.1016/j.sca.2025.100124

Ruhaimatu Abudu , Emmanuel Anu Thompson , Frank Selase Dzawu , Alfredo Roa-Henriquez

Amid rising global disruptions, including pandemics, geopolitical conflicts, and economic shocks, supply chain resilience has become a strategic imperative. Despite growing attention, limited synthesis exists on how resilience strategies affect supply chain performance under varying conditions. This study addresses that gap through a meta-analysis of 52 empirical studies comprising 236 independent samples and 22,955 observations. Two key strategies, Adaptive Resilience (AR), focused on rapid recovery, and Transformative Resilience (TR), centered on long-term structural adaptation, were examined concerning resilience antecedents, contextual moderators, and outcome metrics. AR was found to be more closely associated with short-term operational recovery, while TR showed stronger links to sustainability and innovation. When applied jointly, these strategies yielded significantly improved performance outcomes compared to their separate implementation. Supply chain complexity emerged as a critical moderating factor, shaping the effectiveness of each strategy based on network characteristics. This study contributes a comprehensive, evidence-based framework that links resilience strategies to their drivers and impacts. Practical implications are also offered by guiding managers on tailoring resilience investments according to the type of disruption and structural features of their supply chains. The findings support the design of more agile and robust supply chains capable of withstanding future global uncertainties.

在流行病、地缘政治冲突和经济冲击等全球破坏日益加剧的情况下，供应链弹性已成为一项战略要务。尽管受到越来越多的关注，但在不同条件下弹性策略如何影响供应链绩效的综合研究有限。本研究通过对52项实证研究的荟萃分析，包括236个独立样本和22,955个观察结果，解决了这一差距。研究了以快速恢复为重点的适应性弹性（AR）和以长期结构适应为中心的变革弹性（TR）两种关键策略，包括弹性的前因、情境调节因子和结果指标。研究发现，AR与短期业务恢复的关系更为密切，而TR与可持续性和创新的关系更为密切。当联合应用时，与单独实现相比，这些策略产生了显著提高的性能结果。供应链的复杂性成为一个关键的调节因素，塑造了基于网络特征的每种策略的有效性。本研究提供了一个全面的、基于证据的框架，将弹性战略与其驱动因素和影响联系起来。通过指导管理人员根据其供应链的破坏类型和结构特征定制弹性投资，还提供了实际意义。研究结果支持设计更灵活、更稳健的供应链，以抵御未来全球的不确定性。

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引用次数: 0

An analytical risk mitigation framework for steel fabrication supply chains using fuzzy inference and house of risk 基于模糊推理和风险屋的钢铁制造供应链风险缓解分析框架

Supply Chain Analytics

Pub Date : 2025-04-10 DOI: 10.1016/j.sca.2025.100122

Fadhil Adita Ramadhan , Agus Mansur , Nashrullah Setiawan , Mohd Rizal Salleh

This study integrates the House of Risk (HOR) approach with the Fuzzy Inference System (FIS) to manage supply chain risks in steel fabrication by addressing market uncertainties and operational challenges to enhance stability and productivity. The study begins with risk identification using HOR and the calculation of fuzzy aggregate risk priority (FARP) based on severity and frequency. A Mamdani based FIS is then applied to prioritize risks and develop mitigation strategies, leveraging data from expert interviews and literature reviews. The findings highlight supplier order failures as the top risk with the highest FARP score, leading to the proposal of 50 mitigation actions, including managed inventory systems and supplier diversification, to strengthen supply chain resilience and reduce vulnerabilities. However, this study is limited to the steel fabrication industry and relies on expert opinions and secondary data, which may affect generalizability. Future research can apply this approach to other industries and incorporate realtime data for validation. The proposed mitigation strategies offer actionable insights for supply chain managers, helping companies improve operational stability and adapt effectively to market uncertainties. By introducing an integrated HOR and FIS approach, this study provides a dynamic and systematic framework for comprehensive supply chain risk management, offering original insights to the field.

本研究将风险之家（HOR）方法与模糊推理系统（FIS）相结合，通过解决市场不确定性和运营挑战来管理钢铁制造中的供应链风险，以提高稳定性和生产率。本研究首先使用HOR进行风险识别，并根据严重程度和频率计算模糊综合风险优先级（FARP）。然后，利用专家访谈和文献综述的数据，应用基于Mamdani的FIS来确定风险的优先级并制定缓解策略。研究结果强调，供应商订单失败是FARP得分最高的最大风险，因此提出了50项缓解措施，包括管理库存系统和供应商多样化，以加强供应链弹性并减少脆弱性。然而，本研究仅限于钢铁制造行业，并依赖于专家意见和二手数据，这可能会影响通用性。未来的研究可以将这种方法应用于其他行业，并结合实时数据进行验证。拟议的缓解战略为供应链管理人员提供了可行的见解，帮助公司提高运营稳定性并有效适应市场的不确定性。通过引入整合的HOR和FIS方法，本研究为全面的供应链风险管理提供了一个动态和系统的框架，为该领域提供了原创的见解。

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引用次数: 0

A text mining study of competencies in modern supply chain management with skillset mapping 现代供应链管理能力的文本挖掘研究与技能集映射

Supply Chain Analytics

Pub Date : 2025-04-05 DOI: 10.1016/j.sca.2025.100117

Parminder Singh Kang , Rickard Enstroem , Bhawna Bhawna , Owen Bennett

This study explores the skills and competencies required by modern supply chain management professionals, focusing on the shift toward advanced technological capabilities. We analyze job advertisements from a prominent Canadian employment platform using web scraping, natural language processing, and machine learning techniques, including Latent Dirichlet Allocation and Term Frequency-Inverse Document Frequency. The findings reveal that job postings primarily emphasize traditional operational skills such as logistics, inventory control, and customer relationship management. However, there is a noticeable underrepresentation of advanced technological competencies, such as machine learning, data analytics, and automation, which are increasingly critical in today's supply chain environment. This gap highlights the need for greater alignment between job market demands and supply chain management's evolving digital transformation landscape. The study identifies key themes, including technical, managerial, and soft skills integration, emphasizing adaptability, data literacy, and strategic decision-making. The results suggest a misalignment between the competencies highlighted in job advertisements and the skills necessary for managing the complexities of a digitalized supply chain. This research offers practical recommendations for industry leaders to refine hiring strategies, academic institutions to modernize curricula, and job platforms to better showcase emerging skill requirements. Addressing this gap is essential to equip supply chain professionals with the tools and expertise to meet the challenges of a technology-driven future.

本研究探讨了现代供应链管理专业人员所需的技能和能力，重点是向先进技术能力的转变。我们使用网络抓取、自然语言处理和机器学习技术，包括潜在狄利克雷分配和术语频率-逆文档频率，分析了来自加拿大一个著名就业平台的招聘广告。调查结果显示，招聘信息主要强调传统的操作技能，如物流、库存控制和客户关系管理。然而，先进技术能力的代表性明显不足，例如机器学习、数据分析和自动化，这些在当今的供应链环境中越来越重要。这一差距凸显了就业市场需求与供应链管理不断发展的数字化转型格局之间需要更大程度的协调。该研究确定了关键主题，包括技术、管理和软技能整合，强调适应性、数据素养和战略决策。结果表明，招聘广告中强调的能力与管理数字化供应链复杂性所需的技能之间存在不一致。这项研究为行业领导者提供了切实可行的建议，以完善招聘策略，使学术机构的课程现代化，以及更好地展示新兴技能需求的就业平台。解决这一差距对于为供应链专业人员提供工具和专业知识以应对技术驱动的未来挑战至关重要。

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引用次数: 0

Machine learning and artificial intelligence methods and applications for post-crisis supply chain resiliency and recovery 危机后供应链弹性和恢复的机器学习和人工智能方法和应用

Supply Chain Analytics

Pub Date : 2025-04-04 DOI: 10.1016/j.sca.2025.100121

G. Sakthi Balan , V. Santhosh Kumar , S. Aravind Raj

Resilient and adaptive strategies for recovery have been underscored by supply chain disruptions induced by natural disasters, pandemics, and wars. Supply chain resilience protects enterprises, communities, and humanitarian activities during pandemics and wars. This study investigates the utilization of artificial intelligence and machine learning methodologies to enhance supply chain resilience and recovery in the aftermath of these crises. Leveraging data-driven methodologies, these technologies provide opportunities to improve the overall resilience of the supply chain, optimize resource allocation, and enhance decision-making. Proposed newer measures to protect economies, national security, lives, and a more resilient future are discussed in this study. Machine learning and artificial intelligence can process vast amounts of data quickly to provide real-time insights into the state of the supply chain, including damage assessments, demand fluctuations, and disruptions to transportation routes. Machine learning and artificial intelligence in supply chain management have reduced demand forecasting errors by 10–20 % and enhanced disruption reaction times by 20–30 %. The delivery reliability was also enhanced by 10–20 % as the artificial intelligence can forecast the delays and recommend alternate routes. Machine learning and artificial intelligence provide insights, automation, and agility to rebuild and enhance supply chains after challenging circumstances. This work is unique in showing how to improve supply chain resilience at critical moments by combining technologies and adopting hybrid methodologies.

自然灾害、流行病和战争造成的供应链中断凸显了恢复弹性和适应性战略。供应链弹性在大流行和战争期间保护企业、社区和人道主义活动。本研究探讨了利用人工智能和机器学习方法来增强供应链在这些危机之后的弹性和恢复。利用数据驱动的方法，这些技术为提高供应链的整体弹性、优化资源分配和增强决策提供了机会。本研究讨论了为保护经济、国家安全、生命和更有弹性的未来而提出的新措施。机器学习和人工智能可以快速处理大量数据，提供对供应链状态的实时洞察，包括损害评估、需求波动和运输路线中断。供应链管理中的机器学习和人工智能将需求预测误差降低了10 - 20% %，并将中断反应时间提高了20 - 30% %。由于人工智能可以预测延误并推荐替代路线，因此交付可靠性也提高了10 - 20% %。机器学习和人工智能为在充满挑战的环境下重建和增强供应链提供了洞察力、自动化和敏捷性。这项工作在展示如何通过结合技术和采用混合方法在关键时刻提高供应链弹性方面是独一无二的。

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引用次数: 0

An optimization-based analytics model for sustainable and blockchain-enabled supply chains in uncertain environments 一个基于优化的分析模型，用于不确定环境下的可持续和区块链支持的供应链

Supply Chain Analytics

Pub Date : 2025-04-03 DOI: 10.1016/j.sca.2025.100119

S. Priyan

The carbon footprint is highly uncertain and directly impacts demand forecasting, with uncertainty arising from both positive and negative perspectives. This duality highlights the contrasting viewpoints of decision-makers during the decision-making process. This study employs generalized trapezoidal bipolar fuzzy numbers to manage uncertainty in carbon emissions and integrates blockchain technology to enhance demand forecasting in the supply chain. Additionally, we incorporate a warm-up process to minimize faulty items during production and consider investments in green technologies to reduce emissions from various activities. This paper provides insights into sustainability, operational efficacy, and profit maximization in uncertain ecological settings. We mathematically formulate the proposed scenario and uniquely calculate the concave combination of expected values from both positive and negative membership components. Optimality is derived, and a numerical analysis is performed to effectively clarify the theory, followed by an extensive sensitivity analysis of various parameters.

碳足迹具有高度不确定性，直接影响需求预测，其不确定性来自积极和消极两个角度。这种双重性凸显了决策者在决策过程中截然不同的观点。本研究采用广义梯形双极模糊数来管理碳排放的不确定性，并整合区块链技术来加强供应链中的需求预测。此外，我们还纳入了预热流程，以最大限度地减少生产过程中的次品，并考虑投资绿色技术以减少各种活动的排放。本文深入探讨了不确定生态环境下的可持续性、运营效率和利润最大化。我们用数学方法制定了建议的方案，并唯一计算了正负成员成分预期值的凹形组合。我们推导出了最优性，并进行了数值分析以有效阐明理论，随后还对各种参数进行了广泛的敏感性分析。

引用次数: 0