Naval Research Logistics最新文献_第7页

Demand side perception: The mediating role of public procurement regulatory framework in successful road construction implementation 需求侧感知:公共采购监管框架在道路建设成功实施中的中介作用

IF 2.3 4区管理学

Naval Research Logistics Pub Date : 2023-01-01 DOI: 10.23773/2023_2

Noah Mwelu, Susan Watundu, M. Moya

引用次数: 0

Parcel delivery systems for city logistics: a cost-based comparison between different transportation technologies 城市物流包裹递送系统:不同运输技术之间基于成本的比较

IF 2.3 4区管理学

Naval Research Logistics Pub Date : 2023-01-01 DOI: 10.23773/2023_3

B. Himstedt, F. Meisel

引用次数: 0

Decentral decision-making for energy-aware charging of intralogistics equipment 基于能量感知的物流设备充电分散决策

IF 2.3 4区管理学

Naval Research Logistics Pub Date : 2023-01-01 DOI: 10.23773/2023_4

S. Scholz

引用次数: 0

Automated Trucks and the Future of Logistics - A Delphi-Based Scenario Study 自动化卡车与物流的未来——基于delphi的情景研究

IF 2.3 4区管理学

Naval Research Logistics Pub Date : 2023-01-01 DOI: 10.23773/2023_1

Svenja Escherle, Emilia Darlagiannis, Anna Sprung

引用次数: 0

Where to locate COVID-19 mass vaccination facilities? 新冠肺炎大规模疫苗接种设施在哪里？

IF 1.9 4区管理学

Naval Research Logistics Pub Date : 2022-03-01 Epub Date: 2021-06-11 DOI: 10.1002/nav.22007

Dimitris Bertsimas, Vassilis Digalakis, Alexander Jacquillat, Michael Lingzhi Li, Alessandro Previero

{"title":"Where to locate COVID-19 mass vaccination facilities?","authors":"Dimitris Bertsimas, Vassilis Digalakis, Alexander Jacquillat, Michael Lingzhi Li, Alessandro Previero","doi":"10.1002/nav.22007","DOIUrl":"10.1002/nav.22007","url":null,"abstract":"The outbreak of COVID-19 led to a record-breaking race to develop a vaccine. However, the limited vaccine capacity creates another massive challenge: how to distribute vaccines to mitigate the near-end impact of the pandemic? In the United States in particular, the new Biden administration is launching mass vaccination sites across the country, raising the obvious question of where to locate these clinics to maximize the effectiveness of the vaccination campaign. This paper tackles this question with a novel data-driven approach to optimize COVID-19 vaccine distribution. We first augment a state-of-the-art epidemiological model, called DELPHI, to capture the effects of vaccinations and the variability in mortality rates across age groups. We then integrate this predictive model into a prescriptive model to optimize the location of vaccination sites and subsequent vaccine allocation. The model is formulated as a bilinear, nonconvex optimization model. To solve it, we propose a coordinate descent algorithm that iterates between optimizing vaccine distribution and simulating the dynamics of the pandemic. As compared to benchmarks based on demographic and epidemiological information, the proposed optimization approach increases the effectiveness of the vaccination campaign by an estimated 20%, saving an extra 4000 extra lives in the United States over a 3-month period. The proposed solution achieves critical fairness objectives-by reducing the death toll of the pandemic in several states without hurting others-and is highly robust to uncertainties and forecast errors-by achieving similar benefits under a vast range of perturbations.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":"69 1","pages":"179-200"},"PeriodicalIF":1.9,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8441649/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49412464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Screening multi-dimensional heterogeneous populations for infectious diseases under scarce testing resources, with application to COVID-19. 在缺乏检测资源的情况下筛查传染病的多维异质性人群，并应用于COVID - 19

IF 1.9 4区管理学

Naval Research Logistics Pub Date : 2022-02-01 Epub Date: 2021-03-16 DOI: 10.1002/nav.21985

Hussein El Hajj, Douglas R Bish, Ebru K Bish, Hrayer Aprahamian

{"title":"Screening multi-dimensional heterogeneous populations for infectious diseases under scarce testing resources, with application to COVID-19.","authors":"Hussein El Hajj, Douglas R Bish, Ebru K Bish, Hrayer Aprahamian","doi":"10.1002/nav.21985","DOIUrl":"10.1002/nav.21985","url":null,"abstract":"Testing provides essential information for managing infectious disease outbreaks, such as the COVID-19 pandemic. When testing resources are scarce, an important managerial decision is who to test. This decision is compounded by the fact that potential testing subjects are heterogeneous in multiple dimensions that are important to consider, including their likelihood of being disease-positive, and how much potential harm would be averted through testing and the subsequent interventions. To increase testing coverage, pooled testing can be utilized, but this comes at a cost of increased false-negatives when the test is imperfect. Then, the decision problem is to partition the heterogeneous testing population into three mutually exclusive sets: those to be individually tested, those to be pool tested, and those not to be tested. Additionally, the subjects to be pool tested must be further partitioned into testing pools, potentially containing different numbers of subjects. The objectives include the minimization of harm (through detection and mitigation) or maximization of testing coverage. We develop data-driven optimization models and algorithms to design pooled testing strategies, and show, via a COVID-19 contact tracing case study, that the proposed testing strategies can substantially outperform the current practice used for COVID-19 contact tracing (individually testing those contacts with symptoms). Our results demonstrate the substantial benefits of optimizing the testing design, while considering the multiple dimensions of population heterogeneity and the limited testing capacity.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":"69 1","pages":"3-20"},"PeriodicalIF":1.9,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8251476/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42640282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ontology-based Forecast of the Duration of Logistics Processes in One-of-a-Kind Production in SME 基于本体的中小企业独一无二生产物流过程持续时间预测

IF 2.3 4区管理学

Naval Research Logistics Pub Date : 2022-01-01 DOI: 10.23773/2022_5

Deike Gliem, U. Jessen, S. Wenzel, Wibke Kusturica, C. Laroque

引用次数: 0

Compatibility, opportunities and challenges in the combination of Industry 4.0 and Lean Production 工业4.0与精益生产相结合的兼容性、机遇与挑战

IF 2.3 4区管理学

Naval Research Logistics Pub Date : 2022-01-01 DOI: 10.23773/2022_9

Serdar Yürekli, Carola Schulz

{"title":"Compatibility, opportunities and challenges in the combination of Industry 4.0 and Lean Production","authors":"Serdar Yürekli, Carola Schulz","doi":"10.23773/2022_9","DOIUrl":"https://doi.org/10.23773/2022_9","url":null,"abstract":"The continuous increase in technology associated with the Fourth Industrial Revolution and advancing digitalization are inﬂuencing today’s production processes and the competitiveness of factories. Integrating Industry 4.0 into Lean Production can generate more efﬁcient, leaner production and logistics with the help of intelligent networking systems. However, combining the two concepts also poses some challenges, such as the complex integration of technology into the Lean Production tools, and a resistance from employees to accept new technological ways. Furthermore, there is a lack of practical studies that guide companies on which variant of the different Industry 4.0 technologies should and could be integrated into their Lean Production processes. This paper analyzes the compatibility of Industry 4.0 and Lean Production and presents practical solutions by highlighting the opportunities and challenges of combining the two. This research is based on a systematic literature analysis and evaluating expert interviews which leads to the ﬁndings and hypotheses. The results explain how Lean Production and Industry 4.0 can be successfully combined, and which aspects need to be and should be considered on a management level.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":"20 1","pages":"9"},"PeriodicalIF":2.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87915518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Cargo Accumulation Risks in Maritime Supply Chains: A new perspective towards Risk Management for Theory, and Recommendations for the Insurance Industry and Cargo Shippers 海运供应链中的货物积累风险:风险管理理论的新视角，以及对保险业和货主的建议

IF 2.3 4区管理学

Naval Research Logistics Pub Date : 2022-01-01 DOI: 10.23773/2022_4

Stephan L. K. Freichel, Johannes K. Wörtge, Arthur Haas, L. T. Veer

引用次数: 1

Forecasting Recovery Period of the Airfreight Transportation from Covid-19 Pandemic by using Time Series Modelling 基于时间序列模型的Covid-19大流行航空货运恢复期预测

IF 2.3 4区管理学

Naval Research Logistics Pub Date : 2022-01-01 DOI: 10.23773/2022_03

Tuzun Tolga Inan

{"title":"Forecasting Recovery Period of the Airfreight Transportation from Covid-19 Pandemic by using Time Series Modelling","authors":"Tuzun Tolga Inan","doi":"10.23773/2022_03","DOIUrl":"https://doi.org/10.23773/2022_03","url":null,"abstract":"COVID-19 has a dramatically negative effect globally, so all transportation modes also airfreight have been affected negatively. This study aims to forecast the airfreight load factor by applying time series to the selected variables. After providing general information about COVID-19, the forecasting results apply to the time series modeling finding the getting back time into the recovery period. It analyzes between January 2016-May 2021 with available tonne-kilometer, revenue tonne-kilometer, load factor, gross domestic product, domestic and international freight. The findings show that the cargo load factor is affected by domestic transportation in the long-term and international transport in the short-term periods. So, airfreight is firstly affected by international transport due to its global position. The forecast results show that the recovery period started in February 2021 and will continue with a robust growth trend in July 2021 due to the changing airlines’ focus on freight transportation. After the completion of vaccination, primarily related to passenger transportation, airfreight transportation also benefits from this growth trend with the configuration change of aircraft’. This paper’s contribution shows the necessity to minimize the economic damage by using passenger aircraft for freight transport to increase the speed of the recovery period in terms of GDP. © 2022, Bundesvereinigung Logistik (BVL). All rights reserved.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":"30 1","pages":"3"},"PeriodicalIF":2.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86402872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0