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

{"title":"An analytical review of vendor-managed inventory models in sustainable supply chains","authors":"Katherinne Salas-Navarro , Melissa Rojano-Flores , Valentina Salcedo-Villanueva , Leopoldo Eduardo Cárdenas-Barrón","doi":"10.1016/j.sca.2025.100189","DOIUrl":"10.1016/j.sca.2025.100189","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100189"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"A mixed-integer optimization approach to E-tailing supply chain resilience through substitution and transshipment","authors":"Ayar Karimi , Omid Boyer , Reza Tavakkoli-Moghaddam , Hadi Shirouyehzad","doi":"10.1016/j.sca.2026.100195","DOIUrl":"10.1016/j.sca.2026.100195","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"A hybrid learning framework for forecasting uncertainty and adaptive inventory planning in retail supply chains","authors":"Zizi Mohammed, Chafi Anas, Mohammed El Hammoume","doi":"10.1016/j.sca.2025.100180","DOIUrl":"10.1016/j.sca.2025.100180","url":null,"abstract":"<div><div>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<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>= 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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100180"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145584512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"An empirical study on technology adoption and supply chain optimization using structural modeling","authors":"Ali Mohaghar , Rohollah Ghasemi , Mojtaba Taghipour","doi":"10.1016/j.sca.2025.100181","DOIUrl":"10.1016/j.sca.2025.100181","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100181"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"An optimization framework for pricing and delivery in competing retail supply chains with strategic consumers","authors":"Hossein Choopani Asgarabad , Ata Allah Taleizadeh , Mohsen Afsharian","doi":"10.1016/j.sca.2026.100192","DOIUrl":"10.1016/j.sca.2026.100192","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100192"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"A machine learning and evolutionary optimization framework for carbon-aware supply chain routing","authors":"Lorena Sánchez-Pravos , Javier Parra-Domínguez , Sara Rodríguez González , Pablo Chamoso","doi":"10.1016/j.sca.2025.100182","DOIUrl":"10.1016/j.sca.2025.100182","url":null,"abstract":"<div><div>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<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>: 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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100182"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"An analytical framework for evaluating blockchain and IoT use cases in sustainable supply chains","authors":"Atefeh Shoomal , Mohammad Jahanbakht , Paul J. Componation","doi":"10.1016/j.sca.2026.100198","DOIUrl":"10.1016/j.sca.2026.100198","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100198"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"An analytical approach to risk assessment in agri-food supply chains using fuzzy inference systems","authors":"Madushan Madhava Jayalath , R.M. Chandima Ratnayake , H. Niles Perera , Amila Thibbotuwawa","doi":"10.1016/j.sca.2025.100179","DOIUrl":"10.1016/j.sca.2025.100179","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100179"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"A descriptive analytics framework for operational and environmental drivers in electricity supply chain networks","authors":"Sara Kamali","doi":"10.1016/j.sca.2026.100193","DOIUrl":"10.1016/j.sca.2026.100193","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100193"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"A metaheuristic approach for optimizing drone routing in healthcare supply chains","authors":"Tejinder Singh Lakhwani, Yerasani Sinjana","doi":"10.1016/j.sca.2025.100153","DOIUrl":"10.1016/j.sca.2025.100153","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100153"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0