Samyukta Sethuraman, Ankur Bansal, Setareh Mardan, Mauricio G. C. Resende, Timothy L. Jacobs
Amazon Locker is a self-service delivery or pickup location where customers can pick up packages and drop off returns. A basic first-come-first-served policy for accepting package delivery requests to lockers results in lockers becoming full with standard shipping speed (3- to 5-day shipping) packages, leaving no space for expedited packages, which are mostly next-day or two-day shipping. This paper proposes a solution to the problem of determining how much locker capacity to reserve for different ship-option packages. Yield management is a much-researched field with popular applications in the airline, car rental, and hotel industries. However, Amazon Locker poses a unique challenge in this field because the number of days a package will wait in a locker (package dwell time) is, in general, unknown. The proposed solution combines machine learning techniques to predict locker demand and package dwell time with linear programming to maximize throughput in lockers. The decision variables from this optimization provide optimal capacity reservation values for different ship options. This resulted in a year-over-year increase of 9% in Locker throughput worldwide during the holiday season of 2018, impacting millions of customers. History: This paper was refereed.
{"title":"Amazon Locker Capacity Management","authors":"Samyukta Sethuraman, Ankur Bansal, Setareh Mardan, Mauricio G. C. Resende, Timothy L. Jacobs","doi":"10.1287/inte.2023.0005","DOIUrl":"https://doi.org/10.1287/inte.2023.0005","url":null,"abstract":"Amazon Locker is a self-service delivery or pickup location where customers can pick up packages and drop off returns. A basic first-come-first-served policy for accepting package delivery requests to lockers results in lockers becoming full with standard shipping speed (3- to 5-day shipping) packages, leaving no space for expedited packages, which are mostly next-day or two-day shipping. This paper proposes a solution to the problem of determining how much locker capacity to reserve for different ship-option packages. Yield management is a much-researched field with popular applications in the airline, car rental, and hotel industries. However, Amazon Locker poses a unique challenge in this field because the number of days a package will wait in a locker (package dwell time) is, in general, unknown. The proposed solution combines machine learning techniques to predict locker demand and package dwell time with linear programming to maximize throughput in lockers. The decision variables from this optimization provide optimal capacity reservation values for different ship options. This resulted in a year-over-year increase of 9% in Locker throughput worldwide during the holiday season of 2018, impacting millions of customers. History: This paper was refereed.","PeriodicalId":510763,"journal":{"name":"INFORMS Journal on Applied Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140364862","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}
Arti Mann, Ben Cleveland, Dan Bumblauskas, Shashidhar Kaparthi
This study highlights the development and application of a predictive analytics system in a Midwestern hospital to assess and manage the risk of patient readmissions within 30 days of discharge. By integrating advanced analytical modeling with electronic health records, the system enables the creation of personalized care plans by accurately predicting patients' readmission risks and the optimal timing for interventions. The results suggest that such models can significantly improve resource allocation and the personalization of care plans, thereby reducing unnecessary readmissions and aligning with value-based, patient-centered healthcare goals.
{"title":"Reducing Hospital Readmission Risk Using Predictive Analytics","authors":"Arti Mann, Ben Cleveland, Dan Bumblauskas, Shashidhar Kaparthi","doi":"10.1287/inte.2022.0086","DOIUrl":"https://doi.org/10.1287/inte.2022.0086","url":null,"abstract":"This study highlights the development and application of a predictive analytics system in a Midwestern hospital to assess and manage the risk of patient readmissions within 30 days of discharge. By integrating advanced analytical modeling with electronic health records, the system enables the creation of personalized care plans by accurately predicting patients' readmission risks and the optimal timing for interventions. The results suggest that such models can significantly improve resource allocation and the personalization of care plans, thereby reducing unnecessary readmissions and aligning with value-based, patient-centered healthcare goals.","PeriodicalId":510763,"journal":{"name":"INFORMS Journal on Applied Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140239749","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}
Sam Sung Ho Kim, M. Husted, Eli V. Olinick, Alexandra Newman
We develop mathematical programming models to calculate enhanced “magic numbers” for determining playoff elimination and clinches in the Korean Baseball Organization. These magic numbers are disseminated on a website for fans seeking accurate updates on their team’s postseason prospects. The website has received attention from Korean sports writers and fans alike.
{"title":"Improving South Korea’s Crystal Ball for Baseball Postseason Clinching and Elimination","authors":"Sam Sung Ho Kim, M. Husted, Eli V. Olinick, Alexandra Newman","doi":"10.1287/inte.2023.0035","DOIUrl":"https://doi.org/10.1287/inte.2023.0035","url":null,"abstract":"We develop mathematical programming models to calculate enhanced “magic numbers” for determining playoff elimination and clinches in the Korean Baseball Organization. These magic numbers are disseminated on a website for fans seeking accurate updates on their team’s postseason prospects. The website has received attention from Korean sports writers and fans alike.","PeriodicalId":510763,"journal":{"name":"INFORMS Journal on Applied Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140480929","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}
Hao Hu, Yongzhi Qi, Hau L. Lee, Zuo-Jun Max Shen, Curtis Liu, Weimeng Zhu, Ningxuan Kang
JD.com utilizes advanced analytical techniques to strengthen its supply chain capability. The end-to-end inventory management model, intelligent risk management system and consumer-to-manufacturer system are implemented to attain agility, resilience and shared value. These efforts have led to significant revenue increases, cost savings, and value creation across the retail ecosystem, benefiting consumers and business partners.
{"title":"Supercharged by Advanced Analytics, JD.com Attains Agility, Resilience, and Shared Value Across Its Supply Chain","authors":"Hao Hu, Yongzhi Qi, Hau L. Lee, Zuo-Jun Max Shen, Curtis Liu, Weimeng Zhu, Ningxuan Kang","doi":"10.1287/inte.2023.0078","DOIUrl":"https://doi.org/10.1287/inte.2023.0078","url":null,"abstract":"JD.com utilizes advanced analytical techniques to strengthen its supply chain capability. The end-to-end inventory management model, intelligent risk management system and consumer-to-manufacturer system are implemented to attain agility, resilience and shared value. These efforts have led to significant revenue increases, cost savings, and value creation across the retail ecosystem, benefiting consumers and business partners.","PeriodicalId":510763,"journal":{"name":"INFORMS Journal on Applied Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140523206","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}
Xiaoming Yuan, Pengxiang Zhao, Hanyu Hu, Jintao You, Changpeng Yang, Wen Peng, Yonghong Kang, K. M. Teo
The rapid evolution of cloud computing technologies has instigated a paradigm shift across various traditional industries, with the live streaming sector standing as a compelling exemplification of this transformation. Huawei Cloud, which has become an influential player in the business-to-business live streaming arena, with its services spanning over 60 countries since 2020, is at the forefront of this shift. Amid the flourishing live streaming market, Huawei Cloud faces the dual challenge of satisfying the escalating demand, while managing the mounting operational costs, predominantly associated with the network bandwidth. To offer premium services while minimizing the bandwidth cost, we developed a dynamic traffic allocation system called GSCO. This system was engineered using an array of operations research methodologies such as continuous optimization, integer programming, graph theory, scheduling, and network-flow problem solving, along with state-of-the-art machine learning algorithms. The GSCO system has been proven highly effective in cost optimization, reducing network bandwidth expenses by about 30% and leading to savings exceeding $49.6 million from Q1 2020 to Q3 2022. In addition, it has significantly bolstered Huawei Cloud’s market share, amplifying peak bandwidth from an initial 1.5 terabits per second (Tbps) to a substantial 16 Tbps.
{"title":"Huawei Cloud Adopts Operations Research for Live Streaming Services to Save Network Bandwidth Cost: The GSCO System","authors":"Xiaoming Yuan, Pengxiang Zhao, Hanyu Hu, Jintao You, Changpeng Yang, Wen Peng, Yonghong Kang, K. M. Teo","doi":"10.1287/inte.2023.0079","DOIUrl":"https://doi.org/10.1287/inte.2023.0079","url":null,"abstract":"The rapid evolution of cloud computing technologies has instigated a paradigm shift across various traditional industries, with the live streaming sector standing as a compelling exemplification of this transformation. Huawei Cloud, which has become an influential player in the business-to-business live streaming arena, with its services spanning over 60 countries since 2020, is at the forefront of this shift. Amid the flourishing live streaming market, Huawei Cloud faces the dual challenge of satisfying the escalating demand, while managing the mounting operational costs, predominantly associated with the network bandwidth. To offer premium services while minimizing the bandwidth cost, we developed a dynamic traffic allocation system called GSCO. This system was engineered using an array of operations research methodologies such as continuous optimization, integer programming, graph theory, scheduling, and network-flow problem solving, along with state-of-the-art machine learning algorithms. The GSCO system has been proven highly effective in cost optimization, reducing network bandwidth expenses by about 30% and leading to savings exceeding $49.6 million from Q1 2020 to Q3 2022. In addition, it has significantly bolstered Huawei Cloud’s market share, amplifying peak bandwidth from an initial 1.5 terabits per second (Tbps) to a substantial 16 Tbps.","PeriodicalId":510763,"journal":{"name":"INFORMS Journal on Applied Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140527023","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}
Yibo Dang, Theodore T. Allen, Manjeet Singh, Jason Gillespie, Jon Cox, James Monkmeyer
DHL Supply Chain North America helped by the Ohio State University developed and implemented a suite of software called the Transportation Network Optimizer. The four modules relate to the same large scale vehicle routing integer programming including outsourcing. The software helped DHL save over $116M through improved bidding and outsourcing by reducing fuel and personnel costs.
{"title":"Innovative Integer Programming Software and Methods for Large-Scale Routing at DHL Supply Chain","authors":"Yibo Dang, Theodore T. Allen, Manjeet Singh, Jason Gillespie, Jon Cox, James Monkmeyer","doi":"10.1287/inte.2023.0087","DOIUrl":"https://doi.org/10.1287/inte.2023.0087","url":null,"abstract":"DHL Supply Chain North America helped by the Ohio State University developed and implemented a suite of software called the Transportation Network Optimizer. The four modules relate to the same large scale vehicle routing integer programming including outsourcing. The software helped DHL save over $116M through improved bidding and outsourcing by reducing fuel and personnel costs.","PeriodicalId":510763,"journal":{"name":"INFORMS Journal on Applied Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140517517","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}
Pub Date : 2024-01-01DOI: 10.1287/inte.2023.intro.v54.n1
Rajesh Tyagi, Pelin Pekgün
This special issue of the INFORMS Journal on Applied Analytics (formerly Interfaces) is devoted to the finalists of the 53rd annual competition for the Franz Edelman Award for Achievement in Advanced Analytics, Operations Research, and Management Science, the profession’s most prestigious award for deployed work. As in previous years, the finalists this year cover a wide range of industries and functions.
{"title":"Introduction: 2023 Franz Edelman Award for Achievement in Advanced Analytics, Operations Research, and Management Science","authors":"Rajesh Tyagi, Pelin Pekgün","doi":"10.1287/inte.2023.intro.v54.n1","DOIUrl":"https://doi.org/10.1287/inte.2023.intro.v54.n1","url":null,"abstract":"This special issue of the INFORMS Journal on Applied Analytics (formerly Interfaces) is devoted to the finalists of the 53rd annual competition for the Franz Edelman Award for Achievement in Advanced Analytics, Operations Research, and Management Science, the profession’s most prestigious award for deployed work. As in previous years, the finalists this year cover a wide range of industries and functions.","PeriodicalId":510763,"journal":{"name":"INFORMS Journal on Applied Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140523025","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}
Yile Liang, Haocheng Luo, Haining Duan, Donghui Li, Hongsen Liao, Jie Feng, Jiuxia Zhao, Hao Ren, Xuetao Ding, Ying Cha, Qingte Zhou, Chenqi Situ, Jinghua Hao, Ke Xing, Feifan Yin, Renqing He, Yang Sun, Yueqiang Zheng, Yipeng Feng, Zhizhao Sun, Jingfang Chen, J. Zheng, Ling Wang
Over the past decade, Meituan, China’s premier online food delivery (OFD) platform, has witnessed remarkable growth. Central to this expansion is its state-of-the-art real-time intelligent dispatch system. This advanced system harnesses the power of operations research and machine learning algorithms to fine-tune order assignments, simultaneously addressing the needs of consumers, couriers, merchants, and the platform itself.
{"title":"Meituan’s Real-Time Intelligent Dispatching Algorithms Build the World’s Largest Minute-Level Delivery Network","authors":"Yile Liang, Haocheng Luo, Haining Duan, Donghui Li, Hongsen Liao, Jie Feng, Jiuxia Zhao, Hao Ren, Xuetao Ding, Ying Cha, Qingte Zhou, Chenqi Situ, Jinghua Hao, Ke Xing, Feifan Yin, Renqing He, Yang Sun, Yueqiang Zheng, Yipeng Feng, Zhizhao Sun, Jingfang Chen, J. Zheng, Ling Wang","doi":"10.1287/inte.2023.0084","DOIUrl":"https://doi.org/10.1287/inte.2023.0084","url":null,"abstract":"Over the past decade, Meituan, China’s premier online food delivery (OFD) platform, has witnessed remarkable growth. Central to this expansion is its state-of-the-art real-time intelligent dispatch system. This advanced system harnesses the power of operations research and machine learning algorithms to fine-tune order assignments, simultaneously addressing the needs of consumers, couriers, merchants, and the platform itself.","PeriodicalId":510763,"journal":{"name":"INFORMS Journal on Applied Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140520921","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}
Prakhar Mehrotra, Mingang Fu, Jing Huang, Sai Rajesh Mahabhashyam, Minghui Liu, Ming (Arthur) Yang, Xiaojie Wang, Joseph Hendricks, Ranjith Moola, Daniel Morland, Kim Krozier, Tiantian Nie, Ou Sun, Fereydoun Adbesh, Ti Zhang, Monika Shrivastav, Jiefeng Xu, Sudarshan Rajan, Michael Turner, Samuel Tucker, Megan D. Jones, Fei Xiao, Ankush Bhargava, Deepak Deshpande, Shwetal Mokashi, Travis Johnson, Chandramouli Raman, Megan Ferguson, Mike Keller, Scott Donahue, Rajiv Bhutta, Mohan Akella, Parvez Musani, Srinivasan Venkatesan, David Guggina, John Furner
Walmart built end to end optimization capabilities in its supply chain to make strategic and operational decisions consisting of network planning and transformation, routing and loading systems and a simulation platform. This optimization-empowered decision framework is evolving and transforming Walmart’s supply chain while keeping its Every-Day-Low-Price (EDLP) promise to its customers.
{"title":"Optimizing Walmart’s Supply Chain from Strategy to Execution","authors":"Prakhar Mehrotra, Mingang Fu, Jing Huang, Sai Rajesh Mahabhashyam, Minghui Liu, Ming (Arthur) Yang, Xiaojie Wang, Joseph Hendricks, Ranjith Moola, Daniel Morland, Kim Krozier, Tiantian Nie, Ou Sun, Fereydoun Adbesh, Ti Zhang, Monika Shrivastav, Jiefeng Xu, Sudarshan Rajan, Michael Turner, Samuel Tucker, Megan D. Jones, Fei Xiao, Ankush Bhargava, Deepak Deshpande, Shwetal Mokashi, Travis Johnson, Chandramouli Raman, Megan Ferguson, Mike Keller, Scott Donahue, Rajiv Bhutta, Mohan Akella, Parvez Musani, Srinivasan Venkatesan, David Guggina, John Furner","doi":"10.1287/inte.2023.0093","DOIUrl":"https://doi.org/10.1287/inte.2023.0093","url":null,"abstract":"Walmart built end to end optimization capabilities in its supply chain to make strategic and operational decisions consisting of network planning and transformation, routing and loading systems and a simulation platform. This optimization-empowered decision framework is evolving and transforming Walmart’s supply chain while keeping its Every-Day-Low-Price (EDLP) promise to its customers.","PeriodicalId":510763,"journal":{"name":"INFORMS Journal on Applied Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140523341","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}
Jiayun Wang, Shanshan Wu, Qingwei Jin, Yijun Wang, Can Chen
The early phase of launching a new apparel product is critical for gaining insights of its performance and classifying it into different categories such as fast selling, average selling, and slow selling. We propose a new ranking-based method to identify the product popularity that predicts regional and national rankings of products based on sales data at an early stage of a sales season. Our method enables companies to efficiently identify popular products within a remarkably short span of two to four weeks.
{"title":"Identifying Popular Products at an Early Stage of Sales Season for Apparel Industry","authors":"Jiayun Wang, Shanshan Wu, Qingwei Jin, Yijun Wang, Can Chen","doi":"10.1287/inte.2023.0022","DOIUrl":"https://doi.org/10.1287/inte.2023.0022","url":null,"abstract":"The early phase of launching a new apparel product is critical for gaining insights of its performance and classifying it into different categories such as fast selling, average selling, and slow selling. We propose a new ranking-based method to identify the product popularity that predicts regional and national rankings of products based on sales data at an early stage of a sales season. Our method enables companies to efficiently identify popular products within a remarkably short span of two to four weeks.","PeriodicalId":510763,"journal":{"name":"INFORMS Journal on Applied Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139147283","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}
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