Ramesh Bollapragada, Venoo Kakar, J. Goodwin, Andrew Fremier
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.
{"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}
Andrew Bowers, M. R. Bowers, Nana Bryan, Paolo Letizia, Spencer Murphy
It is widely recognized that students’ learning can be enhanced and facilitated when students have the opportunity to work together in teams. As a consequence, the pursuit of a methodology to form optimal student teams continues to challenge academics. Based on a review of related literature, we propose a model that includes new approaches to two team criteria. The first is a discrete optimization approach to commonality of schedule. To facilitate team meetings, we offer an exact formulation to ensure students on a given team share a minimum number of common time slots during which they are available. The second team criterion is sufficient soft skills. Using a unique text analysis approach, we ensure that each team includes students with adequate soft skills, such as leadership and interpersonal skills. Our analytic approach enhances the students’ learning experience and class performance and simplifies the faculty task of forming teams.
{"title":"Forming Student Teams to Incorporate Soft Skills and Commonality of Schedule","authors":"Andrew Bowers, M. R. Bowers, Nana Bryan, Paolo Letizia, Spencer Murphy","doi":"10.1287/inte.2022.1129","DOIUrl":"https://doi.org/10.1287/inte.2022.1129","url":null,"abstract":"It is widely recognized that students’ learning can be enhanced and facilitated when students have the opportunity to work together in teams. As a consequence, the pursuit of a methodology to form optimal student teams continues to challenge academics. Based on a review of related literature, we propose a model that includes new approaches to two team criteria. The first is a discrete optimization approach to commonality of schedule. To facilitate team meetings, we offer an exact formulation to ensure students on a given team share a minimum number of common time slots during which they are available. The second team criterion is sufficient soft skills. Using a unique text analysis approach, we ensure that each team includes students with adequate soft skills, such as leadership and interpersonal skills. Our analytic approach enhances the students’ learning experience and class performance and simplifies the faculty task of forming teams.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80689840","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}
Nitish Umang, D. Kiefer, Patricio Gonzalez, H. Thevenot, Christopher D. Johnson, Jean Martin, Emily Stephenson, Jerrold Cline, Banu Gemici-Ozkan, Israel Beniaminy
We present an optimization-based decision support system to generate optimal aircraft engine maintenance schedules that reflect qualitative and quantitative trade-offs from customer, business, and shop perspectives. The approach is currently implemented at GE Aviation Services for global overhaul network induction planning for all commercial product lines.
{"title":"Practice Summary: GE Optimizes for Aircraft Engine Overhaul Scheduling and Shop Assignment","authors":"Nitish Umang, D. Kiefer, Patricio Gonzalez, H. Thevenot, Christopher D. Johnson, Jean Martin, Emily Stephenson, Jerrold Cline, Banu Gemici-Ozkan, Israel Beniaminy","doi":"10.1287/inte.2022.1130","DOIUrl":"https://doi.org/10.1287/inte.2022.1130","url":null,"abstract":"We present an optimization-based decision support system to generate optimal aircraft engine maintenance schedules that reflect qualitative and quantitative trade-offs from customer, business, and shop perspectives. The approach is currently implemented at GE Aviation Services for global overhaul network induction planning for all commercial product lines.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"30 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81586533","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}
Pub Date : 2022-01-01Epub Date: 2022-02-01DOI: 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
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.
{"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}
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