Supply Chain Analytics最新文献_第2页

An analytical review of vendor-managed inventory models in sustainable supply chains 可持续供应链中供应商管理库存模型的分析综述

Supply Chain Analytics

Pub Date : 2026-03-01 Epub Date: 2025-12-17 DOI: 10.1016/j.sca.2025.100189

Katherinne Salas-Navarro , Melissa Rojano-Flores , Valentina Salcedo-Villanueva , Leopoldo Eduardo Cárdenas-Barrón

A vendor-managed inventory system is a collaborative strategy in network logistics. It helps establish a sourcing and inventory control policy that optimizes logistics costs and enhances the efficient use of resources. This research presents a meta-analysis and systematic literature review of 334 articles published in well-known peer-reviewed journals between 2014 and 2024. The main objective is to evaluate the importance and recent trends of the vendor-managed inventory strategy in sustainable supply chains. This study utilizes the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology to enhance the organization, transparency, and reproducibility of the systematic literature review. The meta-analysis presents a perspective on the principal authors, journals, institutions, countries, and sponsors that develop research and publish on the topic. The systematic literature review categorizes supply chain structures, demand types, shortages, vendor-managed inventory aggregations, and payment policies. Also, imperfect production systems, remanufacturing, product deterioration, inventory-routing challenges, carbon emission regulations, and proposed solutions are included. This study provides an overview of recent developments, applications, industries, supply chains, emerging trends, and future research directions. The main finding is that the vendor-managed inventory approach, applied in sustainable supply chains, improves stock availability, reduces waste of perishables, and yields environmental and sustainability benefits. Also, facilities synchronized decision-making and minimized inefficiencies associated with decentralized control. Future research should aim for greater realism, flexibility, and integration of behavioral and digital dimensions.

供应商管理库存系统是网络物流中的一种协同策略。它有助于建立采购和库存控制政策，优化物流成本，提高资源的有效利用。本研究对2014年至2024年间发表在知名同行评议期刊上的334篇文章进行了meta分析和系统文献综述。主要目的是评估供应商管理库存策略在可持续供应链中的重要性和最新趋势。本研究采用PRISMA（系统评价和荟萃分析的首选报告项目）方法来提高系统文献综述的组织性、透明度和可重复性。荟萃分析展示了主要作者、期刊、机构、国家和赞助者对该主题进行研究和发表的观点。系统的文献综述对供应链结构、需求类型、短缺、供应商管理的库存汇总和支付政策进行了分类。此外，不完善的生产系统、再制造、产品劣化、库存路线挑战、碳排放法规和建议的解决方案也包括在内。本研究概述了最新的发展、应用、产业、供应链、新兴趋势和未来的研究方向。主要发现是，供应商管理的库存方法应用于可持续供应链，提高了库存可用性，减少了易腐品的浪费，并产生了环境和可持续发展效益。此外，设施同步决策和最小化与分散控制相关的低效率。未来的研究应该以更大的现实性、灵活性和行为与数字维度的整合为目标。

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引用次数: 0

A mixed-integer optimization approach to E-tailing supply chain resilience through substitution and transshipment 基于替代和转运的电子零售供应链弹性的混合整数优化方法

Supply Chain Analytics

Pub Date : 2026-03-01 Epub Date: 2026-01-21 DOI: 10.1016/j.sca.2026.100195

Ayar Karimi , Omid Boyer , Reza Tavakkoli-Moghaddam , Hadi Shirouyehzad

Efficient order fulfillment in e-tailing networks is increasingly challenged by inventory shortages, dispersed fulfillment centers, and heterogeneous customer preferences. This study develops a mixed-integer programming model that jointly optimizes order allocation, substitution, and lateral transshipment while explicitly incorporating customer satisfaction through behavioral coefficients. The model is solved using a Genetic Algorithm (GA) and evaluated on a numerical case representing an online electronics retailer. The results show that dissatisfaction costs associated with product substitution dominate the objective function (around 72 % of the total cost), whereas lateral transshipment accounts for less than 6 %, indicating that well-designed substitution policies can substantially reduce the need for inter-center transfers. Sensitivity analysis on substitution-related parameters confirms that misestimating customer acceptance can noticeably increase total cost and alter the balance between substitution and transshipment. The findings provide actionable insights for e-tailers seeking to design resilient fulfillment strategies, improve inventory allocation, and maintain customer satisfaction under product shortages.

由于库存短缺、分散的履行中心和不同的客户偏好，电子零售网络中的高效订单履行日益受到挑战。本研究开发了一个混合整数规划模型，该模型通过行为系数明确地将客户满意度纳入其中，共同优化订单分配、替代和横向转运。采用遗传算法对模型进行求解，并以某在线电子零售商为例进行了评价。结果表明，与产品替代相关的不满意成本占目标函数的主导地位（约占总成本的72% %），而横向转运占不到6% %，这表明设计良好的替代政策可以大大减少对中心间转移的需求。对替代相关参数的敏感性分析证实，对客户接受程度的错误估计会显著增加总成本，并改变替代与转运之间的平衡。研究结果为寻求设计弹性履行策略、改善库存分配和在产品短缺情况下保持客户满意度的电子零售商提供了可操作的见解。

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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 : 2026-03-01 Epub 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 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 : 2026-03-01 Epub 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

An optimization framework for pricing and delivery in competing retail supply chains with strategic consumers 与战略消费者竞争的零售供应链中定价和交付的优化框架

Supply Chain Analytics

Pub Date : 2026-03-01 Epub Date: 2026-01-07 DOI: 10.1016/j.sca.2026.100192

Hossein Choopani Asgarabad , Ata Allah Taleizadeh , Mohsen Afsharian

In retail supply chains where products differ in quality and delivery times are quoted in advance, consumers often behave strategically by delaying purchases to benefit from future discounts or faster service. This paper studies a retail competition scenario in which two firms offer products of different quality levels and compete on both price and delivery time. A two-period model is developed to capture strategic consumer decisions based on valuation, patience, and sensitivity to delivery time. Retailers commit to prices and delivery times at the beginning of the selling season, and consumers decide whether, when, and from whom to make a purchase. The interaction is formulated as a Generalized Nash Equilibrium Problem and solved numerically using a Gauss-Seidel-based algorithm. The model considers both uniform and product-specific levels of consumer patience. Results show that an equilibrium exists when consumers are equally or more patient toward the low-quality product, while no equilibrium arises when patience favors the high-quality product. Higher patience for high-quality products reduces demand and profitability for both firms. Under low competition, the high-quality retailer tends to quote longer delivery times; as competition intensifies, both firms shorten their delivery commitments. The model provides analytical insights into how pricing and delivery strategies align with strategic consumer behavior in competitive retail supply chains.

在零售供应链中，产品的质量和交货时间都是提前报价的，消费者通常会采取策略，推迟购买，以从未来的折扣或更快的服务中获益。本文研究了一个零售竞争场景，其中两家公司提供不同质量水平的产品，并在价格和交货时间上竞争。开发了一个两期模型，以基于估值、耐心和对交付时间的敏感性来捕获战略性消费者决策。零售商在销售季节开始时承诺价格和交货时间，消费者决定是否、何时以及从谁那里购买。这种相互作用被表述为一个广义纳什均衡问题，并使用基于高斯-塞德尔的算法进行数值求解。该模型既考虑了统一的消费者耐心水平，也考虑了特定产品的消费者耐心水平。结果表明，当消费者对低质量产品具有同等或更大的耐心时，存在均衡，而当消费者对高质量产品具有更大的耐心时，不存在均衡。对高质量产品的更高耐心降低了两家公司的需求和盈利能力。在低竞争条件下，高质量的零售商倾向于报价更长的交货时间；随着竞争的加剧，两家公司都缩短了交货承诺。该模型提供了定价和交付策略如何与竞争性零售供应链中的战略性消费者行为保持一致的分析见解。

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引用次数: 0

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 : 2026-03-01 Epub 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 analytical framework for evaluating blockchain and IoT use cases in sustainable supply chains 可持续供应链中区块链和物联网用例评估分析框架

Supply Chain Analytics

Pub Date : 2026-03-01 Epub Date: 2026-02-18 DOI: 10.1016/j.sca.2026.100198

Atefeh Shoomal , Mohammad Jahanbakht , Paul J. Componation

The integration of blockchain technology and the Internet of Things offers substantial potential to improve sustainability, transparency, and operational efficiency in supply chains. However, identifying the most appropriate blockchain–Internet of Things use case remains a complex multi-criteria decision problem due to the presence of uncertainty, conflicting objectives, and heterogeneous adoption factors. To address this challenge, this study proposes a hybrid decision-making framework that combines q-rung orthopair fuzzy sets with entropy weighting and the Weighted Aggregated Sum Product Assessment method to evaluate alternative adoption scenarios. Four blockchain–Internet of Things integration scenarios are assessed within a five-echelon manufacturing supply chain. Thirty adoption factors are identified through a systematic literature review and structured using the Technology–Organization–Environment framework. The results indicate that technology maturity (0.0375), sustainability performance (0.0368), reduction of emissions and pollution (0.0366), customer loyalty (0.0366), and investment cost (0.0364) are the most influential evaluation criteria. Among the evaluated scenarios, blockchain-enabled Internet of Things–based tracking achieves the highest preference score (0.629). Sensitivity analyses demonstrate that the rankings remain stable under varying conditions, while comparative analysis with established multi-criteria decision-making methods confirms the robustness of the proposed framework. Overall, the results provide a reliable and uncertainty-aware decision support approach that assists managers in prioritizing high-value blockchain–Internet of Things transformation pathways in complex supply chain environments.

区块链技术和物联网的整合为提高供应链的可持续性、透明度和运营效率提供了巨大的潜力。然而，由于存在不确定性、目标冲突和异质采用因素，确定最合适的区块链-物联网用例仍然是一个复杂的多标准决策问题。为了应对这一挑战，本研究提出了一个混合决策框架，该框架将q阶正形模糊集与熵权和加权总和产品评估方法相结合，以评估备选采用方案。在一个五级制造供应链中评估了四种区块链-物联网集成场景。通过系统的文献综述确定了30个采用因素，并使用技术-组织-环境框架进行了结构化。结果表明，技术成熟度（0.0375）、可持续发展绩效（0.0368）、减少排放和污染（0.0366）、客户忠诚度（0.0366）和投资成本（0.0364）是影响最大的评价标准。在评估的场景中，基于区块链的物联网跟踪获得了最高的偏好得分（0.629）。敏感性分析表明，排名在不同条件下保持稳定，而与已建立的多准则决策方法的比较分析证实了所提出框架的鲁棒性。总体而言，研究结果提供了一种可靠的、不确定性感知的决策支持方法，帮助管理者在复杂的供应链环境中优先考虑高价值的区块链-物联网转型路径。

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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 : 2026-03-01 Epub 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 descriptive analytics framework for operational and environmental drivers in electricity supply chain networks 电力供应链网络中运营和环境驱动因素的描述性分析框架

Supply Chain Analytics

Pub Date : 2026-03-01 Epub Date: 2026-01-12 DOI: 10.1016/j.sca.2026.100193

Sara Kamali

Electricity systems function as multi-stage supply chains comprising generation, transmission, distribution, and retail. Within this network, Transmission System Operators (TSOs) play a critical role in transporting high-voltage electricity from generators to distribution networks. This paper presents a descriptive analytics study of cost and contextual variables in the Brazilian electricity transmission sector, with implications for regulatory benchmarking and cost analysis in infrastructure systems. Drawing on a dataset of 74 observations collected through regulatory reporting, the study examines operational variables used in the benchmarking model and environmental variables that reflect contextual conditions beyond managerial control. For the operational variables, a series of analytical techniques, including multidimensional scaling, hierarchical clustering, principal component analysis, and a regression linking operational expenditure (Opex) to the resulting components, is applied to uncover structural relationships among variables. The results indicate a positive association between Opex and the overall scale of network infrastructure and show that, conditional on scale, TSOs with a stronger orientation toward high-voltage transmission components and reactive power management are associated with higher cost levels, whereas TSOs emphasizing lower-voltage network elements tend to exhibit lower Opex. Environmental variables further cluster into interpretable groupings related to vegetation and terrain conditions, climatic exposure, and physical accessibility, all of which may influence cost but lie outside managerial control. These findings provide insights for researchers, regulators, and practitioners by offering a structured framework for analyzing cost structures and performance variability in regulated electricity transmission networks.

电力系统的功能是多级供应链，包括发电、输电、配电和零售。在这个网络中，输电系统运营商（tso）在将高压电力从发电机输送到配电网方面发挥着关键作用。本文介绍了巴西电力传输部门的成本和环境变量的描述性分析研究，对基础设施系统的监管基准和成本分析具有影响。根据通过监管报告收集的74个观察数据集，该研究检查了基准模型中使用的操作变量和反映超出管理控制的环境变量。对于操作变量，应用了一系列分析技术，包括多维缩放、分层聚类、主成分分析和将运营支出（Opex）与结果组件联系起来的回归，以揭示变量之间的结构关系。结果表明，运营成本与网络基础设施的总体规模呈正相关，并且在规模条件下，更倾向于高压传输组件和无功管理的tso与更高的成本水平相关，而强调低压网络元件的tso往往表现出更低的运营成本。环境变量进一步聚集成与植被和地形条件、气候暴露和物理可达性有关的可解释的分组，所有这些都可能影响成本，但不受管理控制。这些发现为研究人员、监管机构和从业人员提供了一个结构化的框架来分析受监管的电力传输网络的成本结构和性能变化，从而为他们提供了见解。

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引用次数: 0

A metaheuristic approach for optimizing drone routing in healthcare supply chains 优化医疗供应链中无人机路线的元启发式方法

Supply Chain Analytics

Pub Date : 2025-12-01 Epub Date: 2025-08-14 DOI: 10.1016/j.sca.2025.100153

Tejinder Singh Lakhwani, Yerasani Sinjana

Healthcare logistics continue to encounter significant challenges in the timely and reliable delivery of blood bags, mainly due to urban traffic congestion, rugged terrain, and the perishability of medical supplies. Conventional transportation systems frequently fall short of meeting the stringent temporal and thermal requirements inherent to healthcare supply chains. Unmanned Aerial Vehicles (UAVs), or drones, offer a compelling alternative; however, their effective deployment is hindered by constraints such as limited payload capacity, restricted flight range, narrow delivery time windows, and evolving regulatory frameworks. This study proposes the HybridNGS algorithm, a hybrid metaheuristic framework that integrates Nearest Neighbour (NN) for solution initialization, Genetic Algorithm (GA) for global search, and Simulated Annealing (SA) for local refinement, to address the Drone Routing Problem (DRP) in blood logistics. The model incorporates domain-specific constraints, including blood-type compatibility, energy-aware routing, and cold-chain preservation. Empirical evaluations using synthetic and real-world datasets comprising 20–100 hospitals reveal that HybridNGS consistently outperforms benchmark approaches such as GRASP and TSP-D, achieving up to 20 % cost savings, 15 % reduction in drone usage, and notable energy efficiency. The algorithm demonstrates strong scalability and robustness under variable demand and environmental conditions. It is a viable solution for enhancing accessibility, reliability, and sustainability in routine and emergency healthcare delivery systems.

医疗物流在及时、可靠地运送血袋方面继续面临重大挑战，主要原因是城市交通拥堵、地形崎岖以及医疗用品易腐烂。传统的运输系统经常不能满足医疗保健供应链固有的严格的时间和热量要求。无人驾驶飞行器（uav）或无人机提供了一个令人信服的替代方案；然而，它们的有效部署受到诸如有限的有效载荷能力、有限的飞行距离、狭窄的交付时间窗口和不断发展的监管框架等制约因素的阻碍。本研究提出了HybridNGS算法，这是一种混合元启发式框架，集成了用于解决初始化的最近邻（NN），用于全局搜索的遗传算法（GA）和用于局部优化的模拟退火（SA），以解决血液物流中的无人机路由问题（DRP）。该模型结合了特定领域的约束，包括血型兼容性、能量感知路由和冷链保存。利用20 - 100家 医院的合成和真实数据集进行的实证评估表明，HybridNGS始终优于GRASP和TSP-D等基准方法，可节省高达20% %的成本，减少15% %的无人机使用，并显着提高能源效率。该算法在可变需求和环境条件下具有较强的可扩展性和鲁棒性。这是提高常规和紧急医疗保健提供系统的可及性、可靠性和可持续性的可行解决方案。

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引用次数: 0