Informs Journal on Applied Analytics最新文献_第5页

Adoption of FasTrak on San Francisco Bay Area Bridges: Impact of Operations Research Models in Relieving Congestion 旧金山湾区桥梁采用FasTrak:运筹学模型对缓解拥堵的影响

IF 1.4 4区管理学

Informs Journal on Applied Analytics Pub Date : 2022-08-11 DOI: 10.1287/inte.2022.1127

Ramesh Bollapragada, Venoo Kakar, J. Goodwin, Andrew Fremier

{"title":"Adoption of FasTrak on San Francisco Bay Area Bridges: Impact of Operations Research Models in Relieving Congestion","authors":"Ramesh Bollapragada, Venoo Kakar, J. Goodwin, Andrew Fremier","doi":"10.1287/inte.2022.1127","DOIUrl":"https://doi.org/10.1287/inte.2022.1127","url":null,"abstract":"Bay Area toll bridges are the main transportation link across the nine-county San Francisco Bay Area. These bridges experience extreme congestion and become bottlenecks during peak hours with long backups at the toll plazas. A solution to ensure smooth vehicle throughput at toll plazas is the widespread adoption of the electronic toll collection system called FasTrak. However, the FasTrak system has experienced low usage rates since its inception relative to other toll collection systems in the country. Forecasting, marketing, and operations research models were utilized to make recommendations and collaborate with transportation authorities to increase FasTrak usage during peak hours (5–10 a.m. and 3–7 p.m.) to address traffic congestion. After these recommendations were implemented, FasTrak usage increased from 40% in 2006 to the long-term target of 70% by 2016. This paper presents a synthesis of the challenges and the implementation of the FasTrak Strategic Plan. Furthermore, econometric models are presented that capture the effect on traffic volumes of increased FasTrak usage achieved through congestion pricing. Saved travel time resulted in productivity gains of approximately $569 million per year. This study contributes to an understanding of the role of effective transportation policies in reducing congestion and improving productivity.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"10 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75303671","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: 1

Forming Student Teams to Incorporate Soft Skills and Commonality of Schedule 组建学生团队，将软技能和时间表的共性结合起来

IF 1.4 4区管理学

Informs Journal on Applied Analytics Pub Date : 2022-07-29 DOI: 10.1287/inte.2022.1129

Andrew Bowers, M. R. Bowers, Nana Bryan, Paolo Letizia, Spencer Murphy

引用次数: 1

Practice Summary: GE Optimizes for Aircraft Engine Overhaul Scheduling and Shop Assignment 实践总结:GE优化飞机发动机大修计划和车间分配

IF 1.4 4区管理学

Informs Journal on Applied Analytics Pub Date : 2022-07-25 DOI: 10.1287/inte.2022.1130

Nitish Umang, D. Kiefer, Patricio Gonzalez, H. Thevenot, Christopher D. Johnson, Jean Martin, Emily Stephenson, Jerrold Cline, Banu Gemici-Ozkan, Israel Beniaminy

引用次数: 0

Automated and Clinically Optimal Treatment Planning for Cancer Radiotherapy. 癌症放疗的自动化和临床最佳治疗计划。

IF 1.4 4区管理学

Informs Journal on Applied Analytics Pub Date : 2022-01-01 Epub Date: 2022-02-01 DOI: 10.1287/inte.2021.1095

Masoud Zarepisheh, Linda Hong, Ying Zhou, Qijie Huang, Jie Yang, Gourav Jhanwar, Hai D Pham, Pinar Dursun, Pengpeng Zhang, Margie A Hunt, Gig S Mageras, Jonathan T Yang, Yoshiya Yamada, Joseph O Deasy

{"title":"Automated and Clinically Optimal Treatment Planning for Cancer Radiotherapy.","authors":"Masoud Zarepisheh, Linda Hong, Ying Zhou, Qijie Huang, Jie Yang, Gourav Jhanwar, Hai D Pham, Pinar Dursun, Pengpeng Zhang, Margie A Hunt, Gig S Mageras, Jonathan T Yang, Yoshiya Yamada, Joseph O Deasy","doi":"10.1287/inte.2021.1095","DOIUrl":"https://doi.org/10.1287/inte.2021.1095","url":null,"abstract":"<p><p>Each year, approximately 18 million new cancer cases are diagnosed worldwide, and about half must be treated with radiotherapy. A successful treatment requires treatment planning with the customization of penetrating radiation beams to sterilize cancerous cells without harming nearby normal organs and tissues. This process currently involves extensive manual tuning of parameters by an expert planner, making it a time-consuming and labor-intensive process, with quality and immediacy of critical care dependent on the planner's expertise. To improve the speed, quality, and availability of this highly specialized care, Memorial Sloan Kettering Cancer Center developed and applied advanced optimization tools to this problem (e.g., using hierarchical constrained optimization, convex approximations, and Lagrangian methods). This resulted in both a greatly improved radiotherapy treatment planning process and the generation of reliable and consistent high-quality plans that reflect clinical priorities. These improved techniques have been the foundation of high-quality treatments and have positively impacted over 4,000 patients to date, including numerous patients in severe pain and in urgent need of treatment who might have otherwise required longer hospital stays or undergone unnecessary surgery to control the progression of their disease. We expect that the wide distribution of the system we developed will ultimately impact patient care more broadly, including in resource-constrained countries.</p>","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"52 1","pages":"69-89"},"PeriodicalIF":1.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9284667/pdf/nihms-1821384.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40515368","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: 6

INFORMS MEETING CALENDAR 通知会议日程

IF 1.4 4区管理学

Informs Journal on Applied Analytics Pub Date : 2018-06-01 DOI: 10.1287/INTE.35.1.119.59493

Interfaces staff

引用次数: 4