For classroom scheduling during the COVID-19 pandemic, we develop several variations of mixed integer programs where we seek to balance multiple objectives and constraints, including maximizing in-person attendance while maintaining social distancing constraints and balancing in-person attendance across students and over time.
{"title":"Hybrid Scheduling with Mixed-Integer Programming at Columbia Business School","authors":"C. Moallemi, Utkarsh Patange","doi":"10.1287/inte.2022.0070","DOIUrl":"https://doi.org/10.1287/inte.2022.0070","url":null,"abstract":"For classroom scheduling during the COVID-19 pandemic, we develop several variations of mixed integer programs where we seek to balance multiple objectives and constraints, including maximizing in-person attendance while maintaining social distancing constraints and balancing in-person attendance across students and over time.","PeriodicalId":510763,"journal":{"name":"INFORMS Journal on Applied Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139250735","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}
Traditional auditing has been commonly practiced by multinational companies to monitor their suppliers for sustainability violations. Based on a collaborative supplier sustainability performance improvement program at Koninklijke (Royal) Philips N.V., we introduce a framework that offers a paradigm shift to an improvement-based proactive approach that makes use of suppliers’ self-assessments. We refer to this framework as CARE, consisting of the following phases: collecting supplier sustainability data, assessing suppliers’ sustainability levels, reacting to future violations proactively, and enhancing sustainability performance. The framework integrates analytics techniques to understand the link between the general characteristics of the carefully assessed suppliers—such as location, size, and sector—and their sustainability profile, enabling large-scale supplier assessment and improvement. This information is then used to leverage machine learning techniques to predict current and future sustainability levels of suppliers and to determine best actions for sustainability improvement using mathematical programming. The utilization of analytics constitutes a pivotal element in this endeavor and notably makes CARE highly scalable because it harnesses limited supplier data—namely, only general supplier information—while there is a need to support decision making concerning thousands of suppliers. Philips makes use of this framework and reports that the overall 2021 year-on-year improvement in sustainability performance was 24% for suppliers that entered the program in 2020, indicating the efficacy of the suggested approach. History: This paper was refereed. Funding: The authors gratefully acknowledge the support of TKI Dinalog–Dutch Institute for Advance Logistics on the project entitled “Supplier Sustainability Improvement” [Grant 2017-2-132TKI].
跨国公司通常采用传统的审计方法来监控供应商是否违反可持续发展规定。根据 Koninklijke (Royal) Philips N.V. 公司的一项供应商可持续发展绩效改进合作计划，我们介绍了一个框架，它提供了一种范式转变，即利用供应商的自我评估，采取基于改进的主动方法。我们将这一框架称为 CARE，包括以下几个阶段：收集供应商的可持续发展数据、评估供应商的可持续发展水平、主动应对未来的违规行为，以及提高可持续发展绩效。该框架整合了分析技术，以了解经过仔细评估的供应商的一般特征（如地理位置、规模和行业）与其可持续发展状况之间的联系，从而实现大规模的供应商评估和改进。这些信息随后被用于利用机器学习技术来预测供应商当前和未来的可持续发展水平，并通过数学编程来确定改善可持续发展的最佳行动。在这项工作中，分析技术的利用是一个关键因素，尤其使 CARE 具有很强的可扩展性，因为它利用的供应商数据有限，即只有一般供应商信息，但却需要为涉及数千家供应商的决策提供支持。飞利浦公司采用了这一框架，并报告称，2020 年加入该计划的供应商 2021 年的整体可持续发展绩效同比提高了 24%，这表明所建议的方法非常有效。历史：本文已通过评审。资助：作者感谢 TKI Dinalog-Dutch Institute for Advance Logistics 在 "供应商可持续性改进 "项目上的支持 [Grant 2017-2-132TKI]。
{"title":"Stop Auditing and Start to CARE: Paradigm Shift in Assessing and Improving Supplier Sustainability","authors":"Tarkan Tan, M. H. Akyüz, B. Urlu, Santiago Ruiz","doi":"10.1287/inte.2022.0015","DOIUrl":"https://doi.org/10.1287/inte.2022.0015","url":null,"abstract":"Traditional auditing has been commonly practiced by multinational companies to monitor their suppliers for sustainability violations. Based on a collaborative supplier sustainability performance improvement program at Koninklijke (Royal) Philips N.V., we introduce a framework that offers a paradigm shift to an improvement-based proactive approach that makes use of suppliers’ self-assessments. We refer to this framework as CARE, consisting of the following phases: collecting supplier sustainability data, assessing suppliers’ sustainability levels, reacting to future violations proactively, and enhancing sustainability performance. The framework integrates analytics techniques to understand the link between the general characteristics of the carefully assessed suppliers—such as location, size, and sector—and their sustainability profile, enabling large-scale supplier assessment and improvement. This information is then used to leverage machine learning techniques to predict current and future sustainability levels of suppliers and to determine best actions for sustainability improvement using mathematical programming. The utilization of analytics constitutes a pivotal element in this endeavor and notably makes CARE highly scalable because it harnesses limited supplier data—namely, only general supplier information—while there is a need to support decision making concerning thousands of suppliers. Philips makes use of this framework and reports that the overall 2021 year-on-year improvement in sustainability performance was 24% for suppliers that entered the program in 2020, indicating the efficacy of the suggested approach. History: This paper was refereed. Funding: The authors gratefully acknowledge the support of TKI Dinalog–Dutch Institute for Advance Logistics on the project entitled “Supplier Sustainability Improvement” [Grant 2017-2-132TKI].","PeriodicalId":510763,"journal":{"name":"INFORMS Journal on Applied Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139271905","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}
General Electric’s Renewable Energy business used to manually make annual sourcing and logistics plans to procure wind turbine towers from suppliers across the world and deliver them to customer sites. This process was time-consuming, cumbersome, suboptimal, and increased the cost of fulfilling customer demands. We developed an algorithm and a software tool to generate near-optimal towers’ sourcing and logistics plans, which minimized the total direct material and logistics costs incurred.
{"title":"Practice Summary: General Electric Company Optimizes Wind Turbine Towers Sourcing and Logistics Operations","authors":"Srinivas Bollapragada","doi":"10.1287/inte.2022.0058","DOIUrl":"https://doi.org/10.1287/inte.2022.0058","url":null,"abstract":"General Electric’s Renewable Energy business used to manually make annual sourcing and logistics plans to procure wind turbine towers from suppliers across the world and deliver them to customer sites. This process was time-consuming, cumbersome, suboptimal, and increased the cost of fulfilling customer demands. We developed an algorithm and a software tool to generate near-optimal towers’ sourcing and logistics plans, which minimized the total direct material and logistics costs incurred.","PeriodicalId":510763,"journal":{"name":"INFORMS Journal on Applied Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139272754","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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