In this study, we develop a discrete-event simulation model to aid the Trager Institute, an outpatient clinic for optimal aging located in Louisville, Kentucky, in determining their safe reopening strategies during the COVID-19 pandemic and operational strategies beyond the pandemic. The model studies the movement of several groups of people (e.g., healthcare providers, navigators, patients, staff) and the operations of the clinic’s primary and ancillary services. The main objective is to ensure that the clinic operates safely while COVID-19 restrictions are in place and to improve its providers’ utilization rate. The model simulates people’s movement in the clinic, monitors the congestion level in four common areas, and identifies the peak hours during a day. We also study various overbooking and telehealth policies to overcome high cancelation or no-show rates and low utilization for providers. Simulation results using AnyLogic have helped the management decide to reopen the in-person services during the COVID-19 pandemic based on the safe congestion level demonstrated by the simulation. Insights on optimal overbooking and telehealth policies can shed a broader light on other healthcare organizations. History: This paper was refereed.
{"title":"A Simulation Study for a Safe Reopening and Operation of the Trager Institute Optimal Aging Clinic During the COVID-19 Pandemic","authors":"Shahab Sadri, Arsalan Paleshi, Lihui Bai, Monica Gentili","doi":"10.1287/inte.2022.0032","DOIUrl":"https://doi.org/10.1287/inte.2022.0032","url":null,"abstract":"In this study, we develop a discrete-event simulation model to aid the Trager Institute, an outpatient clinic for optimal aging located in Louisville, Kentucky, in determining their safe reopening strategies during the COVID-19 pandemic and operational strategies beyond the pandemic. The model studies the movement of several groups of people (e.g., healthcare providers, navigators, patients, staff) and the operations of the clinic’s primary and ancillary services. The main objective is to ensure that the clinic operates safely while COVID-19 restrictions are in place and to improve its providers’ utilization rate. The model simulates people’s movement in the clinic, monitors the congestion level in four common areas, and identifies the peak hours during a day. We also study various overbooking and telehealth policies to overcome high cancelation or no-show rates and low utilization for providers. Simulation results using AnyLogic have helped the management decide to reopen the in-person services during the COVID-19 pandemic based on the safe congestion level demonstrated by the simulation. Insights on optimal overbooking and telehealth policies can shed a broader light on other healthcare organizations. History: This paper was refereed.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"28 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89159269","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}
Sönke Wieczorrek, Christian Thies, Christian Weckenborg, M. Grunewald, T. S. Spengler
Volkswagen Group Logistics (VWGL) is responsible for the logistics and supply processes of the automotive brands of the Volkswagen Group. In this context, supplier development is vital for efficient and reliable material flows between the process partners. In recent years, VWGL implemented a collaborative approach for supplier development in logistics wherein it is crucial to identify disrupting suppliers and apply improvement measures to increase their logistics performance. Against this background, VWGL initiated a project to examine how supplier development measures can be implemented efficiently to improve the overall logistics performance of VWGL’s supply base. This paper presents the developed operations research approach, which integrates Monte Carlo simulation and a knapsack model on the specific problem of supplier development. The approach consists of three stages: (1) data preparation, (2) measure evaluation, and (3) measure allocation. The approach is validated based on 18 existing less-than-truckload networks of VWGL. We find that, on average, considerable cost savings of 31% can be achieved throughout the networks compared with VWGL’s previous procedure. A new workflow facilitates our approach to lift its potential in practical application sustainably. History: This paper was refereed.
{"title":"Volkswagen Group Logistics Applies Operations Research to Optimize Supplier Development","authors":"Sönke Wieczorrek, Christian Thies, Christian Weckenborg, M. Grunewald, T. S. Spengler","doi":"10.1287/inte.2022.0026","DOIUrl":"https://doi.org/10.1287/inte.2022.0026","url":null,"abstract":"Volkswagen Group Logistics (VWGL) is responsible for the logistics and supply processes of the automotive brands of the Volkswagen Group. In this context, supplier development is vital for efficient and reliable material flows between the process partners. In recent years, VWGL implemented a collaborative approach for supplier development in logistics wherein it is crucial to identify disrupting suppliers and apply improvement measures to increase their logistics performance. Against this background, VWGL initiated a project to examine how supplier development measures can be implemented efficiently to improve the overall logistics performance of VWGL’s supply base. This paper presents the developed operations research approach, which integrates Monte Carlo simulation and a knapsack model on the specific problem of supplier development. The approach consists of three stages: (1) data preparation, (2) measure evaluation, and (3) measure allocation. The approach is validated based on 18 existing less-than-truckload networks of VWGL. We find that, on average, considerable cost savings of 31% can be achieved throughout the networks compared with VWGL’s previous procedure. A new workflow facilitates our approach to lift its potential in practical application sustainably. History: This paper was refereed.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88778897","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}
Socially missioned nonprofit organizations exist to connect at-risk populations to critical healthcare, food, and financial services. However, these organizations face several internal (resource infrastructure, management capability) and external (multiple stakeholders, client demographics and capabilities) constraints within which they must work to attain their goal of helping people in need. The extant academic literature offers a variety of solutions to such allocation problems for use in for-profit organizations. Yet noted differences in the operational systems of for-profit and nonprofit operations limit practitioners’ capabilities to readily leverage these tools. Thus, in this paper, we extend the applicability of classic resource allocation principles to the context of nonprofit operations’ outreach efforts. We describe a detailed case study analysis at one nonprofit organization (SC Thrive) wherein we implemented the theory of constraints–linear programming framework to help maximize the effectiveness of outreach initiatives carried out by the organization. Based on the results from our stepwise, nonlinear yield model, SC Thrive is now capable of doubling the annual number of applications submitted by potential beneficiaries for assistance services. History: This paper was refereed.
{"title":"Enhancing the Reach of Socially Missioned Nonprofits: Insights from a TOC-LP Application","authors":"Erin C. McKie, S. Ahire","doi":"10.1287/inte.2021.0095","DOIUrl":"https://doi.org/10.1287/inte.2021.0095","url":null,"abstract":"Socially missioned nonprofit organizations exist to connect at-risk populations to critical healthcare, food, and financial services. However, these organizations face several internal (resource infrastructure, management capability) and external (multiple stakeholders, client demographics and capabilities) constraints within which they must work to attain their goal of helping people in need. The extant academic literature offers a variety of solutions to such allocation problems for use in for-profit organizations. Yet noted differences in the operational systems of for-profit and nonprofit operations limit practitioners’ capabilities to readily leverage these tools. Thus, in this paper, we extend the applicability of classic resource allocation principles to the context of nonprofit operations’ outreach efforts. We describe a detailed case study analysis at one nonprofit organization (SC Thrive) wherein we implemented the theory of constraints–linear programming framework to help maximize the effectiveness of outreach initiatives carried out by the organization. Based on the results from our stepwise, nonlinear yield model, SC Thrive is now capable of doubling the annual number of applications submitted by potential beneficiaries for assistance services. History: This paper was refereed.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"14 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83801088","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}
Federico Bertero, M. Cerdeiro, Guillermo A. Durán, Nazareno A. Faillace Mullen
We apply mathematical and computational techniques to the development of an approach for optimizing the waste collection system of the Argentine city of Berazategui, 26 km south of Buenos Aires. Taking full account of the city’s particular characteristics, our objective is to not only improve the system’s efficiency but also ensure equitable workloads for waste collection truck crews, including both drivers and collectors. The optimization problem is partitioned into three stages. In the first stage, a heuristic constructs structurally simple collection zones that are balanced in terms of collectors’ walking distances. In the second stage, a mixed-integer linear programming model designs a collection truck route for each zone and minimizes its length. In the third and final stage, each truck is assigned to two zones in such a way as to equalize to the extent possible the length of drivers’ working day. Because working-day length is influenced by multiple factors, we formulate this objective as a biobjective optimization problem and solve it by integer linear programming coupled with an iterative algorithm. The city implemented the approach in early 2020, resulting in a markedly more equitable workload distribution and significant fuel savings and maintenance expense for the city. History: This paper was refereed. Funding: This work was partly financed by the Universidad de Buenos Aires Ciencia y Técnica [Grant 20020170100495BA] (Argentina) and Proyectos de Investigación Plurianuales - Consejo Nacional de Investigaciones Científicas y Técnicas [Grant 11220200100084CO (Argentina)]. G. A. Durán is also funded by the Instituto Sistemas Complejos de Ingeniería in Chile [Grants Iniciativa Científica Milenio - Fondo de Innovación para la Competitividad P05-004-F and Comisión Nacional de Investigación Científica y Tecnológica FB0816].
我们应用数学和计算技术来开发一种方法，以优化阿根廷城市贝拉萨特吉的废物收集系统，布宜诺斯艾利斯以南26公里。充分考虑到城市的特点，我们的目标不仅是提高系统的效率，而且要确保垃圾收集车工作人员(包括司机和收集人员)的工作量公平。优化问题分为三个阶段。在第一阶段，启发式构建结构简单的收集区域，这些区域在收集者的步行距离方面是平衡的。在第二阶段，混合整数线性规划模型为每个区域设计一条集运车路线并使其长度最小化。在第三个也是最后一个阶段，每辆卡车被分配到两个区域，以便尽可能地使司机的工作日长度相等。由于工作日长度受多种因素的影响，我们将该目标表述为一个双目标优化问题，并采用整数线性规划与迭代算法相结合的方法进行求解。该市在2020年初实施了这一方法，从而使工作量分配明显更加公平，并为该市节省了大量燃料和维护费用。历史:本文被审稿。资助:本工作部分由布宜诺斯艾利斯大学(university of Buenos Aires Ciencia y tacimnica) [Grant 20020170100495BA](阿根廷)和Investigación Plurianuales - conjo Nacional de investigacones Científicas y tacimnicas [Grant 11220200100084CO(阿根廷)]项目资助。g.a. Durán也得到了智利系统综合研究所Ingeniería的资助[Grants Iniciativa Científica Milenio - Fondo de Innovación para la competitivad P05-004-F和Comisión Nacional de Investigación Científica y Tecnológica FB0816]。
{"title":"Developing Optimization Tools for Municipal Solid Waste Collection in the Argentine City of Berazategui","authors":"Federico Bertero, M. Cerdeiro, Guillermo A. Durán, Nazareno A. Faillace Mullen","doi":"10.1287/inte.2022.0042","DOIUrl":"https://doi.org/10.1287/inte.2022.0042","url":null,"abstract":"We apply mathematical and computational techniques to the development of an approach for optimizing the waste collection system of the Argentine city of Berazategui, 26 km south of Buenos Aires. Taking full account of the city’s particular characteristics, our objective is to not only improve the system’s efficiency but also ensure equitable workloads for waste collection truck crews, including both drivers and collectors. The optimization problem is partitioned into three stages. In the first stage, a heuristic constructs structurally simple collection zones that are balanced in terms of collectors’ walking distances. In the second stage, a mixed-integer linear programming model designs a collection truck route for each zone and minimizes its length. In the third and final stage, each truck is assigned to two zones in such a way as to equalize to the extent possible the length of drivers’ working day. Because working-day length is influenced by multiple factors, we formulate this objective as a biobjective optimization problem and solve it by integer linear programming coupled with an iterative algorithm. The city implemented the approach in early 2020, resulting in a markedly more equitable workload distribution and significant fuel savings and maintenance expense for the city. History: This paper was refereed. Funding: This work was partly financed by the Universidad de Buenos Aires Ciencia y Técnica [Grant 20020170100495BA] (Argentina) and Proyectos de Investigación Plurianuales - Consejo Nacional de Investigaciones Científicas y Técnicas [Grant 11220200100084CO (Argentina)]. G. A. Durán is also funded by the Instituto Sistemas Complejos de Ingeniería in Chile [Grants Iniciativa Científica Milenio - Fondo de Innovación para la Competitividad P05-004-F and Comisión Nacional de Investigación Científica y Tecnológica FB0816].","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"53 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79899059","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}
A. Consiglio, Akis Kikas, Odysseas P. Michaelides, S. Zenios
The Audit Office of the Republic of Cyprus conducted the first-ever audit of the country’s public debt, seeking answers to two key questions. Is government debt sustainable, and is debt financing efficient and effective in securing the lowest cost with acceptable risks? The audit’s findings were discussed by the parliament and can have significant ramifications for public finance. However, public debt management is quite complex, and the International Organization of Supreme Audit Institutions suggests that sufficient technical knowledge is essential in undertaking an audit, including an understanding of the uncertain macroeconomy, financing conditions, and government fiscal stance. We use a risk management model based on scenario trees in conducting the audit. The model determines optimal debt financing strategies to benchmark the performance of the country’s Public Debt Management Office and answer the audit questions. We also incorporate an integrated assessment model to examine the risks from climate change. The auditor general presented the findings to the Parliamentary Audit Committee in the presence of the Minister of Finance, and his recommendations are expected to have a significant impact on the debt operations of the country. History: This paper was refereed. Funding: This work was supported by the Auditor General Office of the Republic of Cyprus. A. Consiglio was partially funded by NRRP-GRINS [Grant PE00000018].
{"title":"Auditing Public Debt Using Risk Management","authors":"A. Consiglio, Akis Kikas, Odysseas P. Michaelides, S. Zenios","doi":"10.1287/inte.2023.1165","DOIUrl":"https://doi.org/10.1287/inte.2023.1165","url":null,"abstract":"The Audit Office of the Republic of Cyprus conducted the first-ever audit of the country’s public debt, seeking answers to two key questions. Is government debt sustainable, and is debt financing efficient and effective in securing the lowest cost with acceptable risks? The audit’s findings were discussed by the parliament and can have significant ramifications for public finance. However, public debt management is quite complex, and the International Organization of Supreme Audit Institutions suggests that sufficient technical knowledge is essential in undertaking an audit, including an understanding of the uncertain macroeconomy, financing conditions, and government fiscal stance. We use a risk management model based on scenario trees in conducting the audit. The model determines optimal debt financing strategies to benchmark the performance of the country’s Public Debt Management Office and answer the audit questions. We also incorporate an integrated assessment model to examine the risks from climate change. The auditor general presented the findings to the Parliamentary Audit Committee in the presence of the Minister of Finance, and his recommendations are expected to have a significant impact on the debt operations of the country. History: This paper was refereed. Funding: This work was supported by the Auditor General Office of the Republic of Cyprus. A. Consiglio was partially funded by NRRP-GRINS [Grant PE00000018].","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79168327","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}
Dwight Lewis, Nickolas Freeman, Irem Sengul Orgut, Thera Tyner, Ryan Tramp, Niranjan Biligowda, Matthew Hudnall, Xin Thomas Yang, Thomas English, Marilyn Whitman, Steven Samsel, James Cochran, Barry Cambron, Danny Rush, Kumari Seetala, Jason Parton
Analytics can help identify strategies to improve the equity and capacity of health services for populations. However, many government agencies experience challenges with heavy workloads, limited time for continued analytic education, and employee turnover among contracted staff. Therefore, streamlining analytical workflows has the potential to (1) improve labor cost-efficiencies and (2) identify strategies to improve health among enrollees. We describe an analytic framework design that automates several empirical methods and provides recommendations for increasing healthcare access for Alabama Medicaid Agency (AMA) enrollees. The described framework, which includes descriptive and prescriptive elements, has been successfully used to inform various day-to-day analyses conducted by AMA’s Analytics Department and comprehensively analyze AMA-enrolled youths’ accessibility to licensed dentists. Specifically, in the dental context, the framework assisted in identifying (1) dental procedures that were ideal candidates for increased reimbursement payments and (2) geographical locations that AMA should target for interventions to improve physical access to care for AMA’s youth enrollees. The insights offered by the framework for dental care impact more than 0.5 million underserved youth and roughly $90 million of annual revenue for licensed dentists through reimbursements. Funding: D. Lewis, J. Parton, M. Hudnall, R. Tramp, X. T. Yang, and S. Samsel received salary support from the Alabama Medicaid Agency during the execution of this study. B. Cambron, T. Tyner, N. Biligowda, and D. Rush were employed at the Alabama Medicaid Agency during the execution of this study. The other authors do not declare the receipt of funding associated with entities affiliated with this study.
分析可以帮助确定战略，以提高人口保健服务的公平性和能力。然而，许多政府机构面临着繁重的工作量、有限的继续分析教育时间以及合同员工之间的员工流动等挑战。因此，简化分析工作流程有可能(1)提高劳动力成本效率和(2)确定改善参保人健康的策略。我们描述了一个分析框架设计，使几个经验方法自动化，并为增加阿拉巴马州医疗补助机构(AMA)登记者的医疗保健访问提供建议。所描述的框架，包括描述性和规范性的元素，已经成功地用于AMA分析部门进行的各种日常分析，并全面分析AMA注册的年轻人对持照牙医的可及性。具体而言，在牙科方面，该框架有助于确定(1)牙科手术是增加报销支付的理想人选;(2)AMA应该针对哪些地理位置进行干预，以改善AMA青年参保者获得医疗服务的实际途径。牙科保健框架提供的见解影响了50多万得不到服务的年轻人，并通过报销为持牌牙医带来了大约9000万美元的年收入。资助:D. Lewis, J. Parton, M. Hudnall, R. Tramp, X. T. Yang和S. Samsel在本研究执行期间获得阿拉巴马州医疗补助机构的工资支持。B. Cambron, T. Tyner, N. Biligowda和D. Rush在本研究执行期间受雇于阿拉巴马州医疗补助机构。其他作者没有声明收到与本研究相关的实体的资助。
{"title":"Analytic Framework to Improve Access for a State Medicaid Agency","authors":"Dwight Lewis, Nickolas Freeman, Irem Sengul Orgut, Thera Tyner, Ryan Tramp, Niranjan Biligowda, Matthew Hudnall, Xin Thomas Yang, Thomas English, Marilyn Whitman, Steven Samsel, James Cochran, Barry Cambron, Danny Rush, Kumari Seetala, Jason Parton","doi":"10.1287/inte.2023.1161","DOIUrl":"https://doi.org/10.1287/inte.2023.1161","url":null,"abstract":"Analytics can help identify strategies to improve the equity and capacity of health services for populations. However, many government agencies experience challenges with heavy workloads, limited time for continued analytic education, and employee turnover among contracted staff. Therefore, streamlining analytical workflows has the potential to (1) improve labor cost-efficiencies and (2) identify strategies to improve health among enrollees. We describe an analytic framework design that automates several empirical methods and provides recommendations for increasing healthcare access for Alabama Medicaid Agency (AMA) enrollees. The described framework, which includes descriptive and prescriptive elements, has been successfully used to inform various day-to-day analyses conducted by AMA’s Analytics Department and comprehensively analyze AMA-enrolled youths’ accessibility to licensed dentists. Specifically, in the dental context, the framework assisted in identifying (1) dental procedures that were ideal candidates for increased reimbursement payments and (2) geographical locations that AMA should target for interventions to improve physical access to care for AMA’s youth enrollees. The insights offered by the framework for dental care impact more than 0.5 million underserved youth and roughly $90 million of annual revenue for licensed dentists through reimbursements. Funding: D. Lewis, J. Parton, M. Hudnall, R. Tramp, X. T. Yang, and S. Samsel received salary support from the Alabama Medicaid Agency during the execution of this study. B. Cambron, T. Tyner, N. Biligowda, and D. Rush were employed at the Alabama Medicaid Agency during the execution of this study. The other authors do not declare the receipt of funding associated with entities affiliated with this study.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135526088","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}
Biao Yuan, Zengde Deng, Na Geng, Yujie Chen, Haoyuan Hu
Cainiao collaborates with thousands of logistics partners to provide the most cost- and time-efficient delivery services. In this work, we develop a decision support system to select good fulfillment routes consisting of partners for newly created parcels. The system’s core optimization component contains a proportion algorithm based on solving an offline integer programming problem and an online algorithm based on dual values of the associated linear programming relaxation. The company has implemented the system to transport numerous parcels from China to other countries and regions, thus saving millions of dollars annually. History: This paper was refereed.
{"title":"Practice Summary: Cainiao Optimizes the Fulfillment Routes of Parcels","authors":"Biao Yuan, Zengde Deng, Na Geng, Yujie Chen, Haoyuan Hu","doi":"10.1287/inte.2023.1166","DOIUrl":"https://doi.org/10.1287/inte.2023.1166","url":null,"abstract":"Cainiao collaborates with thousands of logistics partners to provide the most cost- and time-efficient delivery services. In this work, we develop a decision support system to select good fulfillment routes consisting of partners for newly created parcels. The system’s core optimization component contains a proportion algorithm based on solving an offline integer programming problem and an online algorithm based on dual values of the associated linear programming relaxation. The company has implemented the system to transport numerous parcels from China to other countries and regions, thus saving millions of dollars annually. History: This paper was refereed.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"16 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87563453","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}
Location decisions are strategic and usually multicriteria. In decision making, companies need to anticipate future developments at potential locations. Company-driven and municipal development measures change location conditions over time. For location-seeking companies, the realization of municipal measures is fraught with uncertainty. They are planned by several municipal actors, and their long-term implications are hard to predict. Thus, the early-systematic consideration of company-internal and -external development measures is vital for decision makers (DMs) in a future-oriented location assessment. In our paper, we develop a robust decision support framework for companies to solve the regional facility location and development planning problem (RFLDP). Our framework includes a quantitative planning approach based on established operations research (OR) optimization models and a practical guideline for a structured acquisition of relevant data. The chief executive officer (CEO) (or DM) of a small- or medium-sized enterprise (SME) asked us to solve his acute RFLDP. For this, we proposed a systematic workflow and accompanied the SME’s regional facility location and development planning. In doing so, we structured the CEO’s decision-making process effectively and created an objective-transparent basis for his strategic decisions. The core feature of our work is the inclusion of the human factor of DMs, as we interacted with the CEO along his decision-making process to gradually develop decision recommendations. As a result, the SME benefited from a better-informed and transparent planning process. We recommended a decision option that was structurally superior to other options, which emerged from the CEO’s intuition and conventional facility location problem solution approaches. Other stakeholders also benefited from the results of our work. History: This paper was refereed. Funding: This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [Grant 439640382].
选址决策是战略性的，通常是多标准的。在决策时，公司需要预测潜在地点的未来发展。随着时间的推移，公司驱动和市政发展措施会改变区位条件。对于寻找选址的公司来说，市政措施的实现充满了不确定性。它们是由几个市政参与者规划的，其长期影响很难预测。因此，在面向未来的区位评估中，早期系统地考虑公司内部和外部发展措施对于决策者(DMs)至关重要。在本文中，我们开发了一个强大的决策支持框架，为公司解决区域设施选址和发展规划问题(RFLDP)。我们的框架包括基于已建立的运筹学(OR)优化模型的定量规划方法和结构化获取相关数据的实用指南。某中小企业的首席执行官(CEO) (DM)要求我们解决他的急性RFLDP。为此，我们提出了系统的工作流程，并配合中小企业的区域设施选址和发展规划。在这样做的过程中，我们有效地构建了首席执行官的决策过程，并为他的战略决策创造了一个客观透明的基础。我们工作的核心特点是包含了决策决策的人为因素，因为我们在CEO的决策过程中与他互动，逐步制定决策建议。因此，中小企业受益于信息更灵通和更透明的规划过程。我们推荐了一个在结构上优于其他选项的决策选项，该选项来自首席执行官的直觉和传统的设施选址问题解决方法。其他利益攸关方也从我们的工作成果中受益。历史:本文被审稿。经费:本研究由德国研究基金会(German Research Foundation, DFG)资助[Grant 439640382]。
{"title":"Small- or Medium-Sized Enterprise Uses Operations Research to Select and Develop its Headquarters Location","authors":"D. Kik, M. Wichmann, T. S. Spengler","doi":"10.1287/inte.2023.1159","DOIUrl":"https://doi.org/10.1287/inte.2023.1159","url":null,"abstract":"Location decisions are strategic and usually multicriteria. In decision making, companies need to anticipate future developments at potential locations. Company-driven and municipal development measures change location conditions over time. For location-seeking companies, the realization of municipal measures is fraught with uncertainty. They are planned by several municipal actors, and their long-term implications are hard to predict. Thus, the early-systematic consideration of company-internal and -external development measures is vital for decision makers (DMs) in a future-oriented location assessment. In our paper, we develop a robust decision support framework for companies to solve the regional facility location and development planning problem (RFLDP). Our framework includes a quantitative planning approach based on established operations research (OR) optimization models and a practical guideline for a structured acquisition of relevant data. The chief executive officer (CEO) (or DM) of a small- or medium-sized enterprise (SME) asked us to solve his acute RFLDP. For this, we proposed a systematic workflow and accompanied the SME’s regional facility location and development planning. In doing so, we structured the CEO’s decision-making process effectively and created an objective-transparent basis for his strategic decisions. The core feature of our work is the inclusion of the human factor of DMs, as we interacted with the CEO along his decision-making process to gradually develop decision recommendations. As a result, the SME benefited from a better-informed and transparent planning process. We recommended a decision option that was structurally superior to other options, which emerged from the CEO’s intuition and conventional facility location problem solution approaches. Other stakeholders also benefited from the results of our work. History: This paper was refereed. Funding: This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [Grant 439640382].","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"4 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73084908","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}
Pierre Dodin, Jingyi Xiao, Yossiri Adulyasak, Neda Etebari Alamdari, Lea Gauthier, Philippe Grangier, Paul Lemaitre, William L. Hamilton
Intermittent demand patterns are commonly present in business aircraft spare parts supply chains. Because of the infrequent arrivals and large variations in demand, aircraft aftermarket demand is difficult to forecast, which often leads to shortages or overstocking of spare parts. In this paper, we present the development and implementation of an advanced analytics framework at Bombardier Aerospace, which is carried out by the Bombardier inventory planning team and IVADO Labs to improve the aftermarket demand forecasting process. This integrated predictive analytics pipeline leverages machine-learning (ML) models and traditional time series models in a single framework in a systematic fashion. We also make use of a tree-based machine-learning method with a large set of input features to estimate two components of intermittent demand, namely demand sizes and interdemand intervals. Through the ML models, we incorporate different features, including those derived from flight data. Outputs of different forecasting models are combined using an ensemble technique that enhances the robustness and accuracy of the forecasts for different groups of aftermarket spare parts categorized by demand patterns. The validation results show an improvement in forecast accuracy of approximately 7% and in unbiased forecast of 5%. The ML-based Bombardier Aftermarket forecasting system has been successfully deployed and used to forecast the aftermarket demand at Bombardier of more than 1 billion Canadian dollars on a regular basis. History: This paper was refereed.
{"title":"Bombardier Aftermarket Demand Forecast with Machine Learning","authors":"Pierre Dodin, Jingyi Xiao, Yossiri Adulyasak, Neda Etebari Alamdari, Lea Gauthier, Philippe Grangier, Paul Lemaitre, William L. Hamilton","doi":"10.1287/inte.2023.1164","DOIUrl":"https://doi.org/10.1287/inte.2023.1164","url":null,"abstract":"Intermittent demand patterns are commonly present in business aircraft spare parts supply chains. Because of the infrequent arrivals and large variations in demand, aircraft aftermarket demand is difficult to forecast, which often leads to shortages or overstocking of spare parts. In this paper, we present the development and implementation of an advanced analytics framework at Bombardier Aerospace, which is carried out by the Bombardier inventory planning team and IVADO Labs to improve the aftermarket demand forecasting process. This integrated predictive analytics pipeline leverages machine-learning (ML) models and traditional time series models in a single framework in a systematic fashion. We also make use of a tree-based machine-learning method with a large set of input features to estimate two components of intermittent demand, namely demand sizes and interdemand intervals. Through the ML models, we incorporate different features, including those derived from flight data. Outputs of different forecasting models are combined using an ensemble technique that enhances the robustness and accuracy of the forecasts for different groups of aftermarket spare parts categorized by demand patterns. The validation results show an improvement in forecast accuracy of approximately 7% and in unbiased forecast of 5%. The ML-based Bombardier Aftermarket forecasting system has been successfully deployed and used to forecast the aftermarket demand at Bombardier of more than 1 billion Canadian dollars on a regular basis. History: This paper was refereed.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135165895","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 Decision Analysis to Diverse Domains: An Introduction to the Special Issue","authors":"S. Bansal, J. Keisler, J. Siebert, K. Jenni","doi":"10.1287/inte.2023.1163","DOIUrl":"https://doi.org/10.1287/inte.2023.1163","url":null,"abstract":"","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"47 33","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72445878","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}
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