Informs Journal on Applied Analytics最新文献

{"title":"Alleviating Court Congestion: The Case of the Jerusalem District Court","authors":"Shany Azaria, B. Ronen, Noam Shamir","doi":"10.1287/inte.2023.0026","DOIUrl":"https://doi.org/10.1287/inte.2023.0026","url":null,"abstract":"This paper investigates court congestion through a field study conducted at the Jerusalem District Court in Israel, aiming to reduce case processing time by adapting successful operational concepts to this unique environment. Using a modified difference-in-differences approach, the study suggests a notable 46.1% reduction in the duration of the treated part of the judicial process, demonstrating the efficacy of operational management tools in alleviating court congestion without compromising process quality or requiring additional resources.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"14 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139009587","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}

{"title":"Data-Driven Order Fulfillment Consolidation for Online Grocery Retailing","authors":"Yang Wang, Tong Wang, Xiaoqing Wang, Yuming Deng, Lei Cao","doi":"10.1287/inte.2022.0068","DOIUrl":"https://doi.org/10.1287/inte.2022.0068","url":null,"abstract":"Improving fulfillment efficiency is critical for long-term sustainability of online grocery retailing. In this paper, we study reducing order fulfillment cost by order consolidation. Motivated by the observation that a significant percentage of buyers place multiple orders within a short time interval, we propose a scheme that attempts to consolidate such “multiorders” to reduce the number of parcels and hence, the shipping cost. At the same time, it cannot significantly disturb the existing order fulfillment process or undermine the customer service level. Successful execution of the scheme requires a prediction of multiorder probabilities and a control policy that selectively prioritizes order processing. For the prediction task, we formulate a binary classification problem and use machine-learning algorithms to predict in real time the probability of a multiorder. For the control task, our proposal is to hold arriving orders in a temporary order pool for potential consolidation and to determine the release timing by a dynamic program. The proposed solution is estimated to capture 92.8% of all the multiorders at the cost of holding the orders for about 20.3 minutes on average. This translates to more than 10 million U.S. dollars of order fulfillment cost saving annually. History: This paper was refereed.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136012847","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}

{"title":"In Memoriam: Srinagesh Gavirneni, 1967–2023","authors":"Srinivas Bollapragada, Milind Dawande","doi":"10.1287/inte.2023.memorial.v53.n6","DOIUrl":"https://doi.org/10.1287/inte.2023.memorial.v53.n6","url":null,"abstract":"Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeINFORMS Journal on Applied AnalyticsAhead of Print In Memoriam: Srinagesh Gavirneni, 1967–2023Srinivas Bollapragada , Milind Dawande Srinivas Bollapragada , Milind Dawande Published Online:6 Oct 2023https://doi.org/10.1287/inte.2023.memorial.v53.n6We are deeply saddened by the sudden passing of Srinagesh Gavirneni, a distinguished associate editor of INFORMS Journal on Applied Analytics (IJAA; formerly Interfaces), on July 21, 2023. He was only 56 years old. He is survived by his wife Ramadevi, his children Siddharth and Meghana, his parents Surendranath and Rajyalakshmi Gavirneni, and his siblings Srinivas Gavirneni and Sridevi Paleti.Nagesh was a dear friend and a brilliant scholar who made significant and sustained contributions to the field of operations management. We had the privilege of knowing him since he was a PhD student at Carnegie Mellon University and collaborating with him on several research papers. He had a broad set of research interests in the field, including the value of information in supply chains, production and inventory decisions, and operations challenges in developing countries, such as the distribution of scarce agricultural water among farmers, the design of contracts for food import, and the abatement of shortages of staple vegetables.Nagesh had a remarkable professional career that spanned over 25 years in academia and industry. At the time of his passing, he was professor of operations, technology, and information management at the SC Johnson College of Business, Cornell University. Prior to joining Cornell, he was assistant professor at the Kelley School of Business, Indiana University, and a research scientist at Schlumberger. Nagesh’s work was influential, widely published, and recognized with several awards. He also served the OR/MS community through his editorial roles in several of our journals, including IJAA, Operations Research, Management Science, Manufacturing & Service Operations Management, Decision Sciences, and Production and Operations Management. He had served as a diligent associate editor for IJAA since 2014. Srinagesh Gavirneni (1967–2023)Nagesh was an outstanding student, a dedicated researcher, a passionate teacher, and a kind-hearted person who went out of his way to offer his help to everyone and anyone who needed it. His wide network of friends will fondly remember his parting words during phone conversations: “Let me know if I can be of any help to you.” He will be greatly missed by all who knew him. We extend our heartfelt condolences to his family and friends. May his soul rest in peace. Back to Top Next FiguresReferencesRelatedInformation Articles In Advance Article Information Metrics Information Published Online:October 06, 2023 Copyright © 2023, INFORMSCite asSrinivas Bollapragada, Milind Dawande (2023) In ","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"299 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135351789","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}

{"title":"AI vs. Human Buyers: A Study of Alibaba’s Inventory Replenishment System","authors":"Jiaxi Liu, Shuyi Lin, Linwei Xin, Yidong Zhang","doi":"10.1287/inte.2023.1160","DOIUrl":"https://doi.org/10.1287/inte.2023.1160","url":null,"abstract":"Inventory management is one of the most important components of Alibaba’s business. Traditionally, human buyers make replenishment decisions: although artificial intelligence (AI) algorithms make recommendations, human buyers can choose to ignore these recommendations and make their own decisions. The company has been exploring a new replenishment system in which algorithmic recommendations are final. The algorithms combine state-of-the-art deep reinforcement learning techniques with the framework of fictitious play. By learning the supplier’s behavior, we are able to address the important issues of lead time and fill rate on order quantity, which have been ignored in the extant literature of stochastic inventory control. We present evidence that our algorithms outperform human buyers in terms of reducing out-of-stock rates and inventory levels. More interestingly, we have seen additional benefits amid the pandemic. Over the last two years, cities in China partially and intermittently locked down to mitigate COVID-19 outbreaks. We have observed panic buying from human buyers during lockdowns, leading to the bullwhip effect. By contrast, panic buying and the bullwhip effect can be mitigated using our algorithms due to their ability to recognize changes in the supplier’s behavior during lockdowns. History: This paper has been accepted for the INFORMS Journal on Applied Analytics Special Issue—2022 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134961607","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}

{"title":"Applying Analytics to Design Lung Transplant Allocation Policy","authors":"Theodore Papalexopoulos, James Alcorn, Dimitris Bertsimas, Rebecca Goff, Darren Stewart, Nikolaos Trichakis","doi":"10.1287/inte.2023.0036","DOIUrl":"https://doi.org/10.1287/inte.2023.0036","url":null,"abstract":"In 2019, the United Network for Sharing (UNOS), which has been operating the Organ Procurement and Transplantation Network (OPTN) in the United States since 1984, was seeking to design a new national lung transplant allocation policy. The goal was to develop a point system that would prioritize candidates on the waiting list in a way that would yield more efficient and equitable outcomes. Our joint Massachusetts Institute of Technology (MIT)/UNOS team joined forces with the OPTN Lung Transplantation Committee in these policy design efforts. We discuss how our team applied a novel analytical framework, which was developed at MIT and utilizes optimization, regression, and simulation techniques, to illuminate salient trade-offs among outcomes and guide the choice of how to weigh different point attributes in the allocation formula. The committee selected for the allocation formula weights that were highlighted in the team’s analysis. The team’s proposal was implemented as the national lung allocation policy on March 9, 2023 across the United States. History: This paper has been accepted for the INFORMS Journal on Applied Analytics Special Issue—2022 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134961601","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}

{"title":"Introduction: 2022 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research","authors":"Margret V. Bjarnadottir, Lawrence D. Stone","doi":"10.1287/inte.2023.intro.v53.n5","DOIUrl":"https://doi.org/10.1287/inte.2023.intro.v53.n5","url":null,"abstract":"The judges for the 2022 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research selected the four finalist papers featured in this special issue of the INFORMS Journal on Applied Analytics (IJAA). The prestigious Wagner Prize—awarded for achievement in implemented operations research, management science, and advanced analytics—emphasizes the quality and originality of mathematical models along with clarity of written and oral exposition. This year’s winning application describes the design and deployment of a generalized synthetic control, a powerful and innovative statistical method for identifying, in a noisy environment, retailing innovations that produce a small percentage improvement in a large volume of sales for Anheuser Busch Inbev. The remaining three papers describe an inverse control approach to allocating lung transplants that best meets targeted outcomes and has been implemented as the national lung allocation policy on March 9, 2023, across the United States; a human-centric, optimized parcel delivery system developed for Deutsche Post that saves money while meeting constraints learned dynamically from driver behavior; and an AI-based system developed for Alibaba that learns supplier behavior to improve replenishment ordering and inventory control. Supplemental Material: Full presentation videos with slides are available in the INFORMS Video Library at https://www.informs.org/Resource-Center/Video-Library and as electronic companions to the INFORMS Journal on Applied Analytics articles.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134961606","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}

{"title":"Human-Centric Parcel Delivery at Deutsche Post with Operations Research and Machine Learning","authors":"Uğur Arıkan, Thorsten Kranz, Baris Cem Sal, Severin Schmitt, Jonas Witt","doi":"10.1287/inte.2023.0031","DOIUrl":"https://doi.org/10.1287/inte.2023.0031","url":null,"abstract":"Features such as estimated delivery time windows and live tracking of shipments play a key role in improving the customer experience in last-mile delivery. The building blocks for enabling these features are reliable knowledge about the expected order of deliveries in a tour and precise delivery time window predictions. For Deutsche Post’s parcel delivery service in Germany, we developed a courier-centric routing algorithm and a corresponding state-of-the-art machine learning model for delivery time window predictions. The routing algorithm combines operations research with statistics and machine learning to implicitly gather and use the tacit knowledge of our experienced couriers within the tour generation. This is achieved by deducing and selecting appropriate precedence constraints from historical delivery data. This novel combination of optimization with data-driven constraints enabled us to provide custom routes to the individual couriers. It proved to be a main driver allowing us to provide accurate delivery time window predictions and live tracking of shipments. Our solution is used by Deutsche Post to plan the daily routes of couriers to the approximately 13,000 parcel delivery districts in Germany as well as to provide live tracking and estimated delivery time windows for 1.6 million parcels each day. History: This paper has been accepted for the INFORMS Journal on Applied Analytics Special Issue—2022 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134962200","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}

{"title":"Generalized Synthetic Control for TestOps at ABI: Models, Algorithms, and Infrastructure","authors":"Luis Costa, Vivek F. Farias, Patricio Foncea, Jingyuan (Donna) Gan, Ayush Garg, Ivo Rosa Montenegro, Kumarjit Pathak, Tianyi Peng, Dusan Popovic","doi":"10.1287/inte.2023.0028","DOIUrl":"https://doi.org/10.1287/inte.2023.0028","url":null,"abstract":"We describe a novel optimization-based approach—generalized synthetic control (GSC)—in which we learn from experiments conducted in a physical retail environment. GSC solves a long-standing problem of learning from experiments conducted in this environment when treatment effects are small, the environment is extremely noisy and nonstationary, and interference and adherence problems are commonplace. The utilization of GSC has demonstrated a remarkable increase in statistical power, approximately one hundredfold (100×) higher than conventional inferential methods. This innovative approach forms the basis of TestOps, a pioneering large-scale experimentation platform designed specifically for physical retailers. TestOps was developed and has been broadly implemented as part of a collaboration between Anheuser Busch Inbev (ABI) and a team of operations researchers and data engineers from the Massachusetts Institute of Technology. TestOps currently runs physical experiments impacting approximately 135 million USD in revenue every month and routinely identifies innovations that result in a 1%–2% increase in sales volume. The vast majority of these innovations would have remained unidentified had we not developed our novel approach to inference. Prior to our implementation, statistically significant conclusions could be drawn on only ∼6% of all experiments, a fraction that has now increased by 10-fold. Given its success, TestOps is being rolled out globally at ABI, driving significant revenue growth and enabling the extraction of valuable insights from large-scale physical experiments. History: This paper has been accepted for the INFORMS Journal on Applied Analytics Special Issue—2022 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134962202","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}

{"title":"An Optimization Case Study in Analyzing Missouri Redistricting","authors":"Kiera W. Dobbs, Rahul Swamy, D. King, Ian G. Ludden, S. Jacobson","doi":"10.1287/inte.2022.0037","DOIUrl":"https://doi.org/10.1287/inte.2022.0037","url":null,"abstract":"Every 10 years, U.S. states redraw their congressional and state legislative district plans. This process decides the political landscape for the subsequent 10 years. Prior to the 2021 redistricting cycle, Missouri enacted new criteria for state legislative districts. The Missouri League of Women Voters (LWV-MO) contacted the authors to analyze the potential impact of these new criteria on the map-drawing process. We apply recombination (a spanning tree method) within a local search optimization framework to analyze the interplay between political geography, constitutional requirements, and political fairness in Missouri. We use this framework to produce district plans that satisfy the new criteria and prioritize different aspects of fairness. The results, quantified by several measures of fairness, reveal an inherent Republican advantage in Missouri because of the state’s political geography and constitutional requirements. We conclude that Missouri’s political geography and constitutional requirements prevent the optimization framework from substantially improving political fairness in state legislative plans. In contrast, the framework can substantially improve political fairness in Missouri congressional plans, which are not subject to the new requirements. The LWV-MO used this work to advocate for fairness and transparency in their testimonies for the Missouri redistricting commission’s public hearings. History: This paper was refereed. Funding: This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program [Grant DGE-1746047]. S. H. Jacobson was supported by the Air Force Office of Scientific Research [Grant FA9550-19-1-0106]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/inte.2022.0037 .","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"27 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73263646","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}

{"title":"Practice Summary: Optimal Student Group Reassignment at U.S. Naval Academy","authors":"Robert M. Curry, Joseph Foraker, G. Lazzaro, David M Ruth","doi":"10.1287/inte.2022.0055","DOIUrl":"https://doi.org/10.1287/inte.2022.0055","url":null,"abstract":"The U.S. Naval Academy is composed of 30 companies of students. Each student has a merit score, and each company has an average merit score. Leadership desires to minimize the deviation in average merit scores by splitting each company into first-year and upper-class groups and reassigning first-year groups to new upper-class groups. We perform this reassignment using greedy and optimal approaches. The standard deviation of average merit scores is reduced by more than half. History: This paper was refereed. Funding: This work was supported by the Office of Naval Research Global [Grant N0001421WX01983].","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"29 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76289710","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}