Supply Chain Analytics最新文献_第2页

A machine learning and evolutionary optimization framework for carbon-aware supply chain routing 碳感知供应链路径的机器学习和进化优化框架

Supply Chain Analytics

Pub Date : 2025-11-28 DOI: 10.1016/j.sca.2025.100182

Lorena Sánchez-Pravos , Javier Parra-Domínguez , Sara Rodríguez González , Pablo Chamoso

The increasing urgency of carbon footprint reduction in supply chain operations demands innovative optimization approaches that balance economic efficiency with environmental sustainability. This paper presents a novel carbon-aware route optimization framework that integrates machine learning-based emission prediction with genetic algorithm optimization for sustainable supply chain management. Our hybrid approach combines Random Forest and XGBoost models in an optimized ensemble to predict carbon emissions with high accuracy (MAPE: 9.48%, R

^{2}

: 0.928), while a genetic algorithm optimizes routes considering both cost and carbon constraints. The framework is validated through two complementary scenarios: (1) controlled experiments on synthetic datasets (n=3,500 routes across three network sizes: 500, 1000, and 2000 routes) derived from real-world emission factors demonstrate 19.5% average emission reduction with 4.7% cost increase, and (2) a quasi-real case study on Salamanca regional distribution network (n=12 routes, 776.6 tons CO2e annually) achieves a 41.4% emission reduction with 8.6% cost increase through strategic modal shifts to rail transport. Both scenarios significantly outperform traditional cost-only optimization methods. The proposed approach provides supply chain managers with actionable insights for achieving sustainability goals while maintaining operational efficiency.

供应链运营中碳足迹减少的紧迫性日益增加，需要创新的优化方法来平衡经济效率和环境可持续性。本文提出了一种新的碳感知路径优化框架，该框架将基于机器学习的排放预测与遗传算法优化相结合，用于可持续供应链管理。我们的混合方法将随机森林和XGBoost模型结合在一个优化的集合中，以高精度预测碳排放（MAPE: 9.48%, R2: 0.928），而遗传算法同时考虑成本和碳约束来优化路线。该框架通过两种互补场景进行验证：(1)在合成数据集上进行控制实验（n=3,500条路由，跨越三种网络规模）；(2)以Salamanca区域配送网络为例（n=12条路线，每年776.6吨二氧化碳当量），通过战略方式转向铁路运输，实现了41.4%的减排和8.6%的成本增加。这两种方案都明显优于传统的纯成本优化方法。所提出的方法为供应链管理者提供了可操作的见解，以实现可持续发展目标，同时保持运营效率。

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引用次数: 0

An empirical study on technology adoption and supply chain optimization using structural modeling 基于结构模型的技术采用与供应链优化实证研究

Supply Chain Analytics

Pub Date : 2025-11-25 DOI: 10.1016/j.sca.2025.100181

Ali Mohaghar , Rohollah Ghasemi , Mojtaba Taghipour

This study examines the direct impact of Industry 4.0 on supply chain performance, focusing on the mediating role of coordination and integration. Data were collected via a questionnaire targeting companies active in the Iranian polyethylene supply chain and analyzed using Structural Equation Modeling in the Statistical Package for the Social Sciences (SPSS) and Analysis of Moment Structures (AMOS). Coordination and integration partially mediate this relationship and facilitate improved operational efficiency. The polyethylene industry faces significant challenges, including poor upstream-downstream coordination, supply-demand imbalances, and limited production quotas. Industry 4.0 technologies, including the Internet of Things, big data analytics, and automation, offer innovative solutions to these barriers, thereby increasing the resilience and sustainability of the supply chain. The findings show that Industry 4.0 has a significant impact on supply chain performance by enabling real-time data sharing and process optimization. This research demonstrates how adopting advanced Industry 4.0 technologies, such as the Internet of Things, big data analytics, and automation, can specifically enhance supply chain coordination, data transparency, and predictive decision-making. In the Iranian polyethylene industry, these technologies enable real-time monitoring of material flows, enhance collaboration between upstream and downstream partners, and reduce disruptions caused by sanctions and market volatility. The study provides practical implications for Iranian policymakers and managers, including developing digital infrastructure, establishing integrated information platforms, and promoting data-driven strategies to achieve sustainable and resilient supply chain performance.

本研究考察了工业4.0对供应链绩效的直接影响，重点关注协调和整合的中介作用。通过针对活跃在伊朗聚乙烯供应链中的公司的问卷调查收集数据，并使用社会科学统计软件包（SPSS）中的结构方程模型和力矩结构分析（AMOS）进行分析。协调和整合在一定程度上调解了这种关系，并促进了业务效率的提高。聚乙烯行业面临着重大挑战，包括上下游协调不佳、供需失衡和生产配额有限。工业4.0技术，包括物联网、大数据分析和自动化，为这些障碍提供了创新的解决方案，从而提高了供应链的弹性和可持续性。研究结果表明，工业4.0通过实现实时数据共享和流程优化，对供应链绩效产生了重大影响。这项研究展示了如何采用先进的工业4.0技术，如物联网、大数据分析和自动化，可以具体地增强供应链协调、数据透明度和预测性决策。在伊朗的聚乙烯行业，这些技术可以实时监控物料流动，加强上下游合作伙伴之间的合作，减少制裁和市场波动造成的中断。该研究为伊朗的政策制定者和管理者提供了实际意义，包括发展数字基础设施，建立综合信息平台，促进数据驱动战略，以实现可持续和弹性的供应链绩效。

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引用次数: 0

A hybrid learning framework for forecasting uncertainty and adaptive inventory planning in retail supply chains 零售供应链中不确定性预测与适应性库存规划的混合学习框架

Supply Chain Analytics

Pub Date : 2025-11-22 DOI: 10.1016/j.sca.2025.100180

Zizi Mohammed, Chafi Anas, Mohammed El Hammoume

Demand forecasting and quantification of uncertainty is an essential asset of the retail supply chain optimization and risk-based inventory decisions. This study will introduce a new hybrid conditional variance model (combining gradient boosting machines (XGBoost, LightGBM), recurrent neural networks (LSTM-GRU hybrid), and econometric volatility modeling (GARCH) using a stacked ensemble meta-learning method to make retail demand forecasts over multiple horizons. The framework handles important deficiencies of current methods by providing simultaneously high-precision point predictions and probability prediction intervals by conditional estimation of variance. The M5 Walmart benchmark dataset of 8000 high-volume product time series including all features engineered in terms of 58 time, statistic, price and event dimensions are empirically validated. Stacked ensemble architecture has high predictive work at R

^{2}

= 0.9681, root mean squared = 1.48 units and mean absolute error = 0.77 units, which is significantly better than base models. Integrated GARCH(1,1) component effectively explains forecast residual volatility whose mean conditional variance is 2.82 square units, which allows it to construct dynamically adaptive 95% confidence intervals. Forecast shift analysis shows average magnitude of day-to-day revision of 3.21 units with great correlation between the magnitude of the predicted variance and the actual forecast volatility. The proposed framework offers supply chain practitioners actionable probabilistic predictions to aid risk-conscious inventory location and adaptive safety inventory determination, which is a major improvement over traditional point estimation techniques.

需求预测和不确定性量化是零售供应链优化和基于风险的库存决策的重要资产。本研究将引入一种新的混合条件方差模型（结合梯度增强机（XGBoost、LightGBM）、循环神经网络（LSTM-GRU混合）和计量波动模型（GARCH）），使用堆叠集成元学习方法进行多视域零售需求预测。该框架通过方差条件估计同时提供高精度点预测和概率预测区间，解决了现有方法的重要不足。M5沃尔玛基准数据集包含8000个大批量产品时间序列，包括在58个时间、统计、价格和事件维度上设计的所有特征。堆叠集成体系结构具有较高的预测效果，R2= 0.9681，均方根= 1.48单位，平均绝对误差= 0.77单位，显著优于基础模型。综合GARCH（1,1）分量能有效解释平均条件方差为2.82平方单位的预测剩余波动率，构造动态自适应的95%置信区间。预测偏移分析显示，日均修正幅度为3.21个单位，预测方差的大小与实际预测波动率之间存在较大的相关性。提出的框架为供应链从业者提供了可操作的概率预测，以帮助风险意识库存定位和自适应安全库存确定，这是对传统点估计技术的重大改进。

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引用次数: 0

An interpretive analysis of influential drivers for control tower adoption in supply chains 对供应链中控制塔采用的影响因素的解释性分析

Supply Chain Analytics

Pub Date : 2025-11-20 DOI: 10.1016/j.sca.2025.100178

Magesh kumar M. Nadar , Angappa Gunasekaran , Vaibhav S. Narwane

A Supply Chain Control Tower (SCCT) provides real-time information, analytics, and decision support for supply chain management, helping organizations manage disruptions and inefficiencies before they occur. The complexity of contemporary supply chains is characterized by various influential factors that significantly affect the performance of Supply Chain Control Towers (SCCT). Interpreting the interactions among these factors is the key for supply chain managers in their efforts to improve decision quality and performance. Factor analysis is used to identify, prioritize, and rank the influential success factors that help accomplish SCCT effectiveness. This study investigates the influential drivers that shape SCCT adoption by applying Total Interpretive Structural Modeling (TISM) to evaluate how they relate and MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée à un Classement) analysis to determine their relative importance. The results illustrate that SC visibility and transparency are the principal factors, while the sustainable growth strategy is the least important factor influencing SCCT. This study delivers valuable practical understanding to supply chain managers regarding expediting efforts and effectively applying SCCT, ultimately boosting supply chain performance.

供应链控制塔（SCCT）为供应链管理提供实时信息、分析和决策支持，帮助组织在中断和效率低下发生之前对其进行管理。现代供应链的复杂性以各种影响因素为特征，这些因素显著影响着供应链控制塔（SCCT）的性能。解释这些因素之间的相互作用是供应链管理者努力提高决策质量和绩效的关键。因子分析用于识别、确定优先级，并对有助于实现SCCT有效性的有影响的成功因素进行排序。本研究通过应用总解释结构模型（Total Interpretive Structural Modeling， TISM）来评估SCCT采用的影响因素，并通过MICMAC （matrix d 'Impacts croissamas Multiplication appliqusame Classement）分析来确定它们的相对重要性。结果表明，供应链可见度和透明度是影响供应链绩效的主要因素，而可持续增长战略是影响供应链绩效的次要因素。本研究为供应链管理者提供了宝贵的实践理解，帮助他们加快工作速度，有效地应用SCCT，最终提高供应链绩效。

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引用次数: 0

An analytical approach to risk assessment in agri-food supply chains using fuzzy inference systems 基于模糊推理系统的农业食品供应链风险评估分析方法

Supply Chain Analytics

Pub Date : 2025-11-19 DOI: 10.1016/j.sca.2025.100179

Madushan Madhava Jayalath , R.M. Chandima Ratnayake , H. Niles Perera , Amila Thibbotuwawa

This study presents a structured, quantitative risk assessment framework for agri-food supply chains (AFSCs), aligned with the guidelines of ISO 31000:2018. The approach integrates Fuzzy Inference Systems (FIS) to quantify and mitigate risks, offering an effective tool to reduce subjectivity, manage uncertainty, and enhance decision-making accuracy. A FIS based risk assessment model was developed using the Probability of Failure (PoF), Consequence of Failure (CoF) and Potential Failure Risk (PFR). Employing the developed FIS models, three disruption scenarios in AFSCs in developing economies were evaluated. The scenarios include: (1) lack of quality farm inputs, (2) lack of logistics infrastructure, and (3) supply-demand mismatches. As per the results, lack of farm inputs results in very high risk in price volatility, high risk in farmer revenue loss and food availability, and moderate risk in post-harvest waste. Logistics inefficiencies are leading to moderate risk in farmer revenue loss while posing low risk in food availability, price volatility, and post-harvest waste. Systemic risks due to supply-demand mismatches result in high risks in price volatility, farmer revenue loss, food availability and post-harvest waste. The proposed risk assessment framework provides the blueprint to develop a risk assessment software for AFSCs in developing economies, which can provide insights on how to combine risk assessment in policy development for supply chain modernisation. Findings of the study suggest that there is a need for a policy-driven systematic approach through market intelligence to manage this volatile supply chain.

本研究提出了一个结构化的、定量的农业食品供应链风险评估框架，与ISO 31000:2018的指导方针保持一致。该方法集成了模糊推理系统（FIS）来量化和降低风险，为减少主观性、管理不确定性和提高决策准确性提供了有效的工具。利用失效概率（PoF）、失效后果（CoF）和潜在失效风险（PFR）建立了基于FIS的风险评估模型。采用已开发的FIS模型，对发展中经济体中afsc的三种中断情景进行了评估。这些情景包括：(1)缺乏优质的农业投入；(2)缺乏物流基础设施；(3)供需不匹配。结果表明，农业投入的缺乏导致价格波动的风险非常高，农民收入损失和粮食供应的风险很高，收获后浪费的风险中等。物流效率低下导致农民收入损失的风险较小，而在粮食供应、价格波动和收获后浪费方面的风险较低。供需错配导致的系统性风险导致价格波动、农民收入损失、粮食供应和收获后浪费的高风险。拟议的风险评估框架为发展中经济体的供应链服务供应商开发风险评估软件提供了蓝图，它可以为如何将风险评估与供应链现代化的政策制定结合起来提供见解。研究结果表明，有必要通过市场情报采取政策驱动的系统方法来管理这一不稳定的供应链。

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引用次数: 0

A data-driven analysis of quality management impacts on energy supply chain performance 质量管理对能源供应链绩效影响的数据驱动分析

Supply Chain Analytics

Pub Date : 2025-11-15 DOI: 10.1016/j.sca.2025.100175

Ruhaimatu Abudu , Beatrice Agbeko

Energy supply chains increasingly adopt digital quality management systems, but research on their performance impact remains limited, especially in emerging markets. While individual digital quality dimensions exist in literature, his study provides empirical validation of an integrated framework of seven dimensions specifically for energy supply chain contexts. Using survey data from 120 supply chain professionals at Ghana National Gas Company, we examine relationships between digital quality analytics implementation and supply chain performance through factor analysis and multiple regression. Results suggest digital quality analytics implementation is associated with 65.3% of supply chain performance variance within this organizational context (R

^{2}

= 0.653, F

_{7, 112}

= 30.13, p

<

0.001), with all seven factors showing significant positive relationships. Digital customer analytics proves the strongest predictor (

β

= 0.243), followed by blockchain integration (

β

= 0.171) and data-driven improvement (

β

= 0.156). Digital maturity shows no moderation association, suggesting consistent effectiveness across organizational readiness levels. Implementation patterns across maturity groups align with institutional theory predictions about technology adoption in emerging markets. While findings are based on a single organization and require broader validation, results offer a preliminarily tested framework that may inform digital quality analytics in similar energy supply chain contexts, extending quality management theory and suggesting potential guidance for digital transformation efforts in similar organizational settings.

能源供应链越来越多地采用数字质量管理系统，但对其绩效影响的研究仍然有限，特别是在新兴市场。虽然文献中存在单独的数字质量维度，但他的研究为能源供应链背景下的七个维度的集成框架提供了实证验证。利用来自加纳国家天然气公司120名供应链专业人员的调查数据，我们通过因素分析和多元回归研究了数字质量分析实施与供应链绩效之间的关系。结果表明，在这个组织背景下，数字质量分析的实施与65.3%的供应链绩效差异相关（R2 = 0.653, f7112 = 30.13, p < 0.001），所有七个因素都显示出显著的正相关关系。数字客户分析被证明是最强的预测因子（β = 0.243），其次是区块链集成（β = 0.171）和数据驱动的改进（β = 0.156）。数字成熟度显示没有适度关联，表明跨组织准备水平的一致有效性。成熟度群体之间的实施模式与新兴市场技术采用的制度理论预测相一致。虽然研究结果是基于单一组织，需要更广泛的验证，但结果提供了一个初步测试的框架，可以为类似能源供应链背景下的数字质量分析提供信息，扩展质量管理理论，并为类似组织环境下的数字化转型工作提供潜在的指导。

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引用次数: 0

A structural analysis of barriers to sustainable construction supply chains 可持续建筑供应链障碍的结构分析

Supply Chain Analytics

Pub Date : 2025-11-13 DOI: 10.1016/j.sca.2025.100177

Seyed Pendar Toufighi , Amir Mohammad Norouzzadeh , Iman Ghasemian Sahebi , Jan Vang

The construction industry is a major contributor to environmental degradation, underscoring the urgency of adopting Green Supply Chain Management (GSCM) to advance sustainability. Despite its importance, GSCM adoption in construction remains limited, particularly in developing countries. This study systematically identifies and models the barriers hindering GSCM implementation in the construction industry, using Iran as a case study. A mixed-method approach was applied, integrating expert interviews with Interpretive Structural Modeling (ISM) and MICMAC analysis to capture both qualitative insights and quantitative interdependencies. Sixteen interrelated barriers were identified and hierarchically structured into a ten-level model. Results indicate that the lack of governmental support and incentives (B3) acts as the most critical driver at the top of the hierarchy, influencing nearly all other factors. Barriers such as the absence of green experts (B1), lack of green suppliers (B4), and limited knowledge and awareness (B12) were found to hold high driving power, while issues like stakeholder collaboration (B7) and managerial commitment (B8) were highly dependent outcomes. The ISM-MICMAC framework thus highlights how systemic and structural deficiencies shape GSCM adoption. By offering a data-driven structural model tailored to the construction context, this study provides both theoretical advancement and practical guidance for policymakers and industry leaders seeking to prioritize interventions that enhance sustainability in Construction Supply Chains (CSCs).

建筑行业是造成环境恶化的主要因素，因此采用绿色供应链管理（GSCM）来促进可持续发展的紧迫性日益凸显。尽管它很重要，但GSCM在建筑中的采用仍然有限，特别是在发展中国家。本研究系统地识别和建模阻碍GSCM在建筑行业实施的障碍，并以伊朗为例进行研究。采用混合方法，将专家访谈与解释结构建模（ISM）和MICMAC分析相结合，以获得定性见解和定量相互依赖关系。确定了16个相互关联的障碍，并按层次结构构建成一个十层模型。结果表明，缺乏政府支持和激励（B3）是最关键的驱动因素，影响几乎所有其他因素。研究发现，缺乏绿色专家（B1）、缺乏绿色供应商（B4）和有限的知识和意识（B12）等障碍具有较高的驱动力，而利益相关者合作（B7）和管理承诺（B8）等问题是高度依赖的结果。因此，ISM-MICMAC框架强调了系统和结构缺陷如何影响GSCM的采用。通过提供一个适合建筑环境的数据驱动结构模型，本研究为政策制定者和行业领导者提供了理论进步和实践指导，以优先考虑提高建筑供应链（CSCs）可持续性的干预措施。

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引用次数: 0

An equilibrium-based framework for managing collusion in multi-channel supply chains 基于均衡的多渠道供应链合谋管理框架

Supply Chain Analytics

Pub Date : 2025-11-11 DOI: 10.1016/j.sca.2025.100176

Mohammad Akbarzadeh Sarabi , Fariborz Jolai , Ata Allah Taleizadeh

Collusion among retailers remains a persistent concern in multi-channel supply chains, where competition has intensified with the rise of online platforms and direct manufacturer sales. Despite extensive research on channel coordination and pricing strategies, limited attention has been given to how e-tailers and direct web-store channels influence collusive behavior and welfare outcomes. To address this gap, this study develops a game-theoretic model of a manufacturer-led supply chain comprising a traditional retailer, an e-tailer, and a manufacturer’s direct web-store. We analyze four structural scenarios and three decision-making modes (competition, collusion, and centralized coordination) to derive the equilibrium strategies of all participants. The results show that collusion increases retailers’ joint profits only when no web-store channel exists, but introducing a direct channel weakens the profitability and stability of collusion. Moreover, the welfare effects depend critically on the e-tailer’s contractual design: under the agency format, collusion may enhance coordination and total welfare, while under the reselling format, it raises prices and harms consumers. The study contributes to the emerging literature on anti-collusion mechanisms in digital supply chains, offering analytical insights and managerial guidance for manufacturers, retailers, and regulators seeking to manage competition and fairness in multi-channel environments.

在多渠道供应链中，零售商之间的串通仍然是一个挥之不去的问题。随着在线平台和制造商直销的兴起，供应链上的竞争加剧了。尽管对渠道协调和定价策略进行了广泛的研究，但对电子零售商和直接网络商店渠道如何影响串通行为和福利结果的关注有限。为了解决这一差距，本研究建立了一个制造商主导的供应链的博弈论模型，该模型由传统零售商、电子零售商和制造商的直接网络商店组成。本文分析了四种结构情景和三种决策模式（竞争、合谋和集中协调），得出了所有参与者的均衡策略。结果表明，只有在不存在网店渠道的情况下，零售商合谋才会增加零售商的共同利润，而引入直接渠道会削弱零售商合谋的盈利能力和稳定性。此外，合谋的福利效应主要取决于电子零售商的契约设计：在代理模式下，合谋可以增强协调和总福利，而在转售模式下，合谋提高了价格，损害了消费者。该研究为数字供应链中反合谋机制的新兴文献做出了贡献，为寻求在多渠道环境中管理竞争和公平的制造商、零售商和监管机构提供了分析见解和管理指导。

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引用次数: 0

An analytical and bibliometric approach to decision-making in sustainable luxury supply chains 可持续奢侈品供应链决策的分析和文献计量方法

Supply Chain Analytics

Pub Date : 2025-10-17 DOI: 10.1016/j.sca.2025.100174

Francesco Mancusi , Fabio Fruggiero , Chiara Cimini , Alexandra Lagorio

The transition to sustainability is a key objective of global policies. However, the implementation and adoption of sustainable practices in supply chains vary significantly across market sectors. Research highlights the luxury sector as particularly critical, with key stakeholders - supply chain managers and consumers - requiring further investigation to better understand the barriers to adopting sustainable production processes and accepting green products. This study explores the current managerial capabilities and perspectives on integrating sustainable practices within luxury supply chains. A bibliometric analysis was conducted using the Scopus database and VOSviewer software to identify keyword clusters and their interrelationships. Six clusters were identified and analyzed, offering insights into effective strategies for overcoming specific managerial barriers. Practical contributions include an actionable playbook in which the sustainable luxury paradigm serves as an enabler for enhancing consumer value while creating managerial opportunities to align profitability with sustainability in circular supply chains.

向可持续性过渡是全球政策的一个关键目标。然而，供应链中可持续实践的实施和采用在各个市场部门之间差异很大。研究强调，奢侈品行业尤为关键，关键利益相关者——供应链经理和消费者——需要进一步调查，以更好地了解采用可持续生产流程和接受绿色产品的障碍。本研究探讨了在奢侈品供应链中整合可持续实践的当前管理能力和观点。利用Scopus数据库和VOSviewer软件进行文献计量学分析，确定关键词集群及其相互关系。确定并分析了六个集群，为克服具体管理障碍的有效策略提供了见解。实际贡献包括可操作的剧本，其中可持续奢侈品范式作为提高消费者价值的推动者，同时创造管理机会，使盈利能力与循环供应链的可持续性保持一致。

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引用次数: 0

A multi-phase analytics framework for supply chain supplier selection and order allocation with delay risks and Industry 4.0 readiness 考虑延迟风险和工业4.0准备的供应链供应商选择和订单分配的多阶段分析框架

Supply Chain Analytics

Pub Date : 2025-10-17 DOI: 10.1016/j.sca.2025.100172

Hesam Shidpour , Nima Karimi , George Baryannis , Mohsen Shidpour

Numerous studies have addressed the Supplier Selection and Order Allocation (SSOA) problem, focusing on optimal quantity allocation. However, in practice, suppliers often fail to deliver allocated quantities on time due to operational delays or disruptions. Thus, incorporating supplier delays into order allocation decisions is essential. This paper introduces a multi-phase optimization framework that integrates the impact of delays into the SSOA process. In the initial phase, several Machine Learning (ML) algorithms are employed to predict delay probabilities at the order level. This study is the first to utilize ML-based delay probability predictions - rather than binary classification (on-time vs. delayed) - to determine optimal supplier allocations. The algorithms are evaluated using performance metrics such as accuracy, F1 score, precision, recall, and AUC, with TOPSIS used to select the most effective algorithm. Predicted probabilities are then aggregated to the supplier level for integration into the optimization model. Given the growing importance of Industry 4.0, the framework incorporates an Industry 4.0 Readiness Index (IRI), constructed using linguistic terms and interval numbers to handle subjective evaluations. The SWARA method is used to assign weights to evaluation criteria. These elements are embedded in a bi-objective optimization model, solved via the augmented ε-constraint method, aiming to minimize supply chain costs while maximizing suppliers' IRI scores. A numerical example based on a real-world case study validates the approach. Results show significant changes in supplier allocations when delay probabilities are considered, with a 4.84 % increase in total supply chain cost, primarily due to increased procurement in certain periods.

许多研究都针对供应商选择与订单分配（SSOA）问题，重点关注最优数量分配。然而，在实践中，由于运营延误或中断，供应商经常不能按时交付分配的数量。因此，将供应商延迟纳入订单分配决策是必不可少的。本文介绍了一个多阶段优化框架，它将延迟的影响集成到SSOA过程中。在初始阶段，使用几种机器学习（ML）算法来预测订单级别的延迟概率。这项研究首次利用基于机器学习的延迟概率预测——而不是二元分类（准时与延迟）——来确定最佳供应商分配。使用准确性、F1分数、精度、召回率和AUC等性能指标对算法进行评估，TOPSIS用于选择最有效的算法。然后将预测的概率聚合到供应商级别，以便集成到优化模型中。考虑到工业4.0的重要性日益增加，该框架纳入了工业4.0准备指数（IRI），该指数使用语言术语和区间数字构建，以处理主观评估。使用SWARA方法为评价标准分配权重。这些要素嵌入到一个双目标优化模型中，通过增广ε-约束方法求解，旨在最小化供应链成本，最大化供应商IRI得分。基于实际案例研究的数值示例验证了该方法的有效性。结果表明，当考虑延迟概率时，供应商分配发生了显著变化，供应链总成本增加了4.84 %，主要是由于某些时期采购的增加。

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引用次数: 0