INFORMS J. Appl. Anal.最新文献

{"title":"Alibaba Vehicle Routing Algorithms Enable Rapid Pick and Delivery","authors":"Haoyuan Hu, Ying Zhang, Jiangwen Wei, Yang Zhan, Xinhui Zhang, Shaojian Huang, Guangrui Ma, Yuming Deng, Siwei Jiang","doi":"10.1287/inte.2021.1108","DOIUrl":"https://doi.org/10.1287/inte.2021.1108","url":null,"abstract":"Alibaba Group pioneered integrated online and offline retail models to allow customers to place online orders of e-commerce and grocery products at its participating stores or restaurants for rapid delivery—in some cases, in as little as 30 minutes after an order has been placed. To meet these service commitments, quick online routing decisions must be made to optimize order picking routes at warehouses and delivery routes for drivers. The solutions to these routing problems are complicated by stringent service commitments, uncertainties, and complex operations in warehouses with limited space. Alibaba has developed a set of algorithms for vehicle routing problems (VRPs), which include an open-architecture adaptive large neighborhood search to support the solution of variants of routing problems and a deep learning-based approach that trains neural network models offline to generate almost instantaneous solutions online. These algorithms have been implemented to solve VRPs in several Alibaba subsidiaries, have generated more than $50 million in annual financial savings, and are applicable to the broader logistics industry. The success of these algorithms has fermented an inner-source community of operations researchers within Alibaba, boosted the confidence of the company’s executives in operations research, and made operations research one of the core competencies of Alibaba Group.","PeriodicalId":430990,"journal":{"name":"INFORMS J. Appl. Anal.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116779293","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}

{"title":"JD.com: Operations Research Algorithms Drive Intelligent Warehouse Robots to Work","authors":"Hengle Qin, Jun Xiao, Dongdong Ge, Linwei Xin, Jianjun Gao, Simai He, Haodong Hu, J. Carlsson","doi":"10.1287/inte.2021.1100","DOIUrl":"https://doi.org/10.1287/inte.2021.1100","url":null,"abstract":"JD.com pioneered same-day delivery as a standard service in China’s business-to-consumer e-commerce sector in 2010. To balance the urgent need to meet growing demands while maintaining high-quality logistics services, the company built intelligent warehouses that use analytics to significantly improve warehouse efficiency. The brain of the intelligent warehouse system is the dispatching algorithm for storage rack-moving robots, which makes real-time dispatching decisions among robots, racks, and workstations after solving large-scale integer programs in seconds. The intelligent warehouse technology has helped the company decrease its fulfillment expense ratio to a world-leading level of 6.5%. The construction of intelligent warehouses has led to estimated annual savings of hundreds of millions of dollars. In 2020, JD.com delivered 90% of its first-party-owned retail orders on the same day or on the day after the order was placed. The agility of such intelligent warehouses has allowed JD.com to handle 10 times the normal volume of orders during peak sales seasons and has also helped the company respond quickly to COVID-19 and ensure the rapid recovery of production capabilities.","PeriodicalId":430990,"journal":{"name":"INFORMS J. Appl. Anal.","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123897971","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}

{"title":"Introduction: 2021 Franz Edelman Award for Achievement in Advanced Analytics, Operations Research, and Management Science","authors":"Carrie Beam, R. Milne","doi":"10.1287/inte.2021.1107","DOIUrl":"https://doi.org/10.1287/inte.2021.1107","url":null,"abstract":"This special issue of the INFORMS Journal on Applied Analytics (formerly Interfaces) is devoted to the finalists of the 50th 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":430990,"journal":{"name":"INFORMS J. Appl. Anal.","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129241089","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}

{"title":"UN World Food Programme: Toward Zero Hunger with Analytics","authors":"Koen Peters, Sérgio Silva, Tim Sergio Wolter, Luis Anjos, Nina van Ettekoven, Éric Combette, Anna Melchiori, Hein A. Fleuren, D. Hertog, Özlem Ergun","doi":"10.1287/inte.2021.1097","DOIUrl":"https://doi.org/10.1287/inte.2021.1097","url":null,"abstract":"Each year, the United Nations World Food Programme (WFP) provides food assistance to around 100 million people in more than 80 countries. Significant investments over the last decade have put planning and optimization at the forefront of tackling emergencies at WFP. A data-driven approach to managing operations has gradually become the norm and has culminated in the creation of a supply chain planning unit and savings of more than USD 150 million—enough to support two million food-insecure people for an entire year. In this paper, we describe three analytical solutions in detail: the Supply Chain Management Dashboard, which uses descriptive and predictive analytics to bring end-to-end visibility and anticipate operational issues; Optimus, which uses a mixed-integer programming model to simultaneously optimize food basket composition and supply chain planning; and DOTS, which is a data integration platform that helps WFP automate and synchronize complex data flows. Three impact studies for Iraq, South Sudan, and COVID-19 show how these tools have changed the way WFP manages its most complex operations. Through analytics, decision makers are now equipped with the insights they need to manage their operations in the best way, thereby saving and changing the lives of millions and bringing the world one step closer to zero hunger.","PeriodicalId":430990,"journal":{"name":"INFORMS J. Appl. Anal.","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127634368","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}

{"title":"Lenovo Schedules Laptop Manufacturing Using Deep Reinforcement Learning","authors":"Yi Liang, Zan Sun, Tianheng Song, Qiang Chou, Wei Fan, Jianping Fan, Yong Rui, Qiping Zhou, Jessie Bai, Chun Yang, Peng Bai","doi":"10.1287/inte.2021.1109","DOIUrl":"https://doi.org/10.1287/inte.2021.1109","url":null,"abstract":"Lenovo Research teamed with members of the factory operations group at Lenovo’s largest laptop manufacturing facility, LCFC, to replace a manual production scheduling system with a decision-making platform built on a deep reinforcement learning architecture. The system schedules production orders at all LCFC’s 43 assembly manufacturing lines, balancing the relative priorities of production volume, changeover cost, and order fulfillment. The multiobjective optimization scheduling problem is solved using a deep reinforcement learning model. The approach combines high computing efficiency with a novel masking mechanism that enforces operational constraints to ensure that the machine-learning model does not waste time exploring infeasible solutions. The use of the new model transformed the production management process enabling a 20% reduction in the backlog of production orders and a 23% improvement in the fulfillment rate. It also reduced the entire scheduling process from six hours to 30 minutes while it retained multiobjective flexibility to allow LCFC to adjust quickly to changing objectives. The work led to increased revenue of US $1.91 billion in 2019 and US $2.69 billion in 2020 for LCFC. The methodology can be applied to other scenarios in the industry.","PeriodicalId":430990,"journal":{"name":"INFORMS J. Appl. Anal.","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131102608","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}

{"title":"Toward Global Food Security: Transforming OCP Through Analytics","authors":"A. Becker, Kassem Benabderrazik, D. Bertsimas, Nada Chtinna, Nada El Majdoub, Elham Mahboubi, Driss Lahlou Kitane, Steve Kokkotos, Georgia Mourtzinou, Ilyas Rakhis","doi":"10.1287/inte.2021.1111","DOIUrl":"https://doi.org/10.1287/inte.2021.1111","url":null,"abstract":"Humanity relies on cultivated lands to feed itself and thrive. Fertilizers are responsible for 30%–50% of food production, and phosphate, the naturally occurring form of phosphorus, which does not have a substitute, is an essential component of fertilizers. OCP, based in Morocco, is the world’s largest phosphate mining and processing company and therefore plays a critical role in global food security. Over the past decade, OCP, in collaboration with Dynamic Ideas, an analytics consulting company, developed a mixed-integer optimization model to holistically optimize its entire sales and supply chain—from the mines to physical treatments, to chemical facilities, to inventory facilities, and to the port for global distribution. The optimization model brings clarity to a complex supply chain, informs management decisions throughout OCP, and has consistently resulted in an improvement of over 20% in earnings before interest, taxes, depreciation, and amortization (EBITDA) annually. This amounted to over $2.3 billion in the period 2015–2020 (23.6% of the cumulative EBITDA of $9.9 billion over this period). This incremental profitability has fueled OCP’s financing capacity; as a result, OCP is implementing a $20 billion capital expenditures (CAPEX) program. The first phase of the CAPEX program led to the doubling of OCP’s mining capacity and the tripling of its fertilizer production capacity. As a result, OCP increased its fertilizer production capacity by eight million tons in the past decade. The model enabled OCP to produce customized fertilizers that helped improve agricultural yields, which in turn led to increased food production, especially in Africa. The increased production of fertilizers and the availability of customized fertilizers have contributed toward global food security. By demonstrating the interconnectedness of all OCP businesses, the model contributed to creating a culture of collaboration, innovation, and entrepreneurship across the company while breaking the existing silos among departments. This transformation led to the establishment of an analytics-based problem-solving approach throughout OCP and a successful executive education class at the Massachusetts Institute of Technology, thus enriching the university.","PeriodicalId":430990,"journal":{"name":"INFORMS J. Appl. Anal.","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131970312","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}

{"title":"A Digital Twin for Decision Making on Livestock Feeding","authors":"D. Raba, R. D. Tordecilla, P. Copado, A. Juan, Daniel Mount","doi":"10.1287/inte.2021.1110","DOIUrl":"https://doi.org/10.1287/inte.2021.1110","url":null,"abstract":"Looking for an accurate and cost-effective solution to measure feed inventories, forecast the feed demand and allow feed suppliers to optimize inventories, production batches, and delivery routes.","PeriodicalId":430990,"journal":{"name":"INFORMS J. Appl. Anal.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131587572","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}

{"title":"Filling a Theater During the COVID-19 Pandemic","authors":"D. Blom, R. Pendavingh, F. Spieksma","doi":"10.1287/inte.2021.1104","DOIUrl":"https://doi.org/10.1287/inte.2021.1104","url":null,"abstract":"In the summer of 2020, Music Building Eindhoven (MBE) had to deal with the economic consequences of the COVID-19 pandemic for theater halls because governmental regulations were having a severe impact on the occupancy. In particular, MBE faced the challenge of determining how to maximize the number of guests in a theater hall while respecting social distancing rules. We have developed and implemented an optimization model based on trapezoid packings to address this challenge. The model showed that up to 40% of the normal capacity can be realized for a single show setting and up to 70% in cases where artists opt for two consecutive performances per evening without reusing seats. The solution was adopted by MBE with significant monetary and managerial benefits.","PeriodicalId":430990,"journal":{"name":"INFORMS J. Appl. Anal.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129086572","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}

{"title":"Container Transportation Scheduling Between Port Yards and the Hinterland in Yunfeng","authors":"L. Zhen, Wenya Lv, Zheyi Tan, Bin Dong","doi":"10.1287/inte.2021.1102","DOIUrl":"https://doi.org/10.1287/inte.2021.1102","url":null,"abstract":"This paper introduces a container transportation scheduling problem in a large logistic company (Yunfeng) in Shanghai City, PR China. This problem considers two types of container transportation orders (i.e., door-to-door orders and warehouse orders) between port yards and the hinterland. Unlike the pickup and delivery problem with time windows, this problem needs to consider not only container pickups and deliveries, but also the loading or unloading of cargo inside containers. Moreover, this problem takes account of some constraints on container transportation regulations in China. We formulate this problem as a mixed-integer programming model and design an improved backtracking search optimization algorithm. Based on the proposed model and algorithm, a vehicle scheduling decision support system is also developed to integrate data collection, preprocessing, Amap (a digital map, navigation, and location service provider in China) interfacing, and optimization. The improvement of the operational efficiency is validated by the usage of the proposed system in practice.","PeriodicalId":430990,"journal":{"name":"INFORMS J. Appl. Anal.","volume":"9 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121632536","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}

{"title":"Practice Summary: Solving the External Candidates Exam Schedule in Norway","authors":"P. Avella, M. Boccia, C. Mannino, Sandro Viglione","doi":"10.1287/inte.2021.1093","DOIUrl":"https://doi.org/10.1287/inte.2021.1093","url":null,"abstract":"We developed a mixed-integer linear programming model to plan exam sessions for external candidates in the Vestfold region, Norway. With our model, the administration planned the last session of 2018, the two sessions of 2019, and the first session of 2020. The plans produced are of high quality and saved three weeks of person effort per session.","PeriodicalId":430990,"journal":{"name":"INFORMS J. Appl. Anal.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116690807","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}