AbstractRecent years have witnessed the emergence of many new mobile Apps and user-centered systems that interact with users by offering choices with rewards. These applications have been promising to address challenging societal problems such as congestion in transportation and behavior changes for healthier lifestyles. Considerable research efforts have been devoted to model the user behaviors in these new applications. However, as real-world user data is often prone to data corruptions, the success of these models hinges on a robust learning method. Building on the recently proposed Latent Decision Threshold (LDT) model, this paper shows that, among the existing robust learning frameworks, the L0 norm based framework can outperform other state-of-the-art methods in terms of prediction accuracy and model estimation. And based on the L0 norm framework, we further develop a user screening algorithm to identify potential bad actors.Keywords: Choice Behavior ModelingLatent Decision Threshold ModelRobust learningData CorruptionBad Actor DetectionDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
{"title":"Modeling User Choice Behavior under Data Corruption: Robust Learning of the Latent Decision Threshold Model","authors":"Feng Lin, Xiaoning Qian, Bobak Mortazavi, Zhangyang Wang, Shuai Huang, Cynthia Chen","doi":"10.1080/24725854.2023.2279080","DOIUrl":"https://doi.org/10.1080/24725854.2023.2279080","url":null,"abstract":"AbstractRecent years have witnessed the emergence of many new mobile Apps and user-centered systems that interact with users by offering choices with rewards. These applications have been promising to address challenging societal problems such as congestion in transportation and behavior changes for healthier lifestyles. Considerable research efforts have been devoted to model the user behaviors in these new applications. However, as real-world user data is often prone to data corruptions, the success of these models hinges on a robust learning method. Building on the recently proposed Latent Decision Threshold (LDT) model, this paper shows that, among the existing robust learning frameworks, the L0 norm based framework can outperform other state-of-the-art methods in terms of prediction accuracy and model estimation. And based on the L0 norm framework, we further develop a user screening algorithm to identify potential bad actors.Keywords: Choice Behavior ModelingLatent Decision Threshold ModelRobust learningData CorruptionBad Actor DetectionDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135137917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-08DOI: 10.1080/24725854.2023.2280606
Weizhi Lin, Qiang Huang
AbstractLandmarks are essential in non-rigid shape registration for identifying the correspondence between designs and actual products. In 3D printing, manual selection of landmarks becomes labor-intensive due to complex product geometries and their non-uniform shape deviations. Automatic selection, however, has to pinpoint landmarks indicative of geometric regions prone to deviations for accuracy qualification. Existing automatic landmarking methods often generate clustered and redundant landmarks for prominent features with high curvatures, compromising the balance between global and local registration errors. To address these issues, we propose an automatic landmark selection method through deviation-aware segmentation and landmarking. As opposed to segmentation for semantic feature identification, deviation-aware segmentation partitions a freeform product for high-curvature region identification. Prone to deviation, these regions are generated through curvature-sensitive remeshing to extract vertices of high curvature and automatic clustering of vertices based on vertex density. Within each segment or high-curvature region, a curvature-weighted function is tailored for the Gaussian process landmarking to sequentially select landmarks with the highest local curvatures. Furthermore, we propose a new evaluation criterion to assess the effectiveness of selected landmarks through registration. The proposed approach is tested through automatic landmarking of printed dental models.Keywords: 3D printing qualificationnon-rigid shape registrationshape segmentationclusteringGaussian process landmarkingDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Additional informationNotes on contributorsWeizhi LinWeizhi Lin is a PhD student in the Daniel J. Epstein Department of Industrial and Systems Engineering at the University of Southern California (USC) in Los Angeles. She completed her B.E. degree in Statistics at Beihang University in 2019. Her research focuses on leveraging domain knowledge to develop models for analyzing complex manifold data, with a specific emphasis on addressing challenges in the field of advanced manufacturing.Qiang HuangDr. Qiang Huang is a professor at the Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles. His research focuses on Machine Learning for Smart Manufacturing and Quality Control for Personalized Manufacturing. He was the holder of the Gordon S. Marshall Early Career Chair in Engineering at USC from 2012 to 2016. He received the IISE Fellow Award, ASME Fellow Award, NS
摘要在非刚性形状配准中，标志是识别设计与实际产品对应关系的关键。在3D打印中，由于复杂的产品几何形状及其不均匀的形状偏差，手动选择地标成为劳动密集型。然而，自动选择必须精确定位容易偏离精度的几何区域的标志。现有的自动地标标记方法往往会对高曲率的显著特征产生聚类和冗余的地标，从而影响全局和局部配准误差的平衡。为了解决这些问题，我们提出了一种基于偏差感知分割和标记的自动地标选择方法。与用于语义特征识别的分割不同，偏差感知分割是用于高曲率区域识别的自由曲面产品。这些区域容易产生偏差，通过曲率敏感重网格提取高曲率的顶点，并根据顶点密度自动聚类。在每个分段或高曲率区域内，为高斯过程地标定制曲率加权函数，依次选择具有最高局部曲率的地标。此外，我们还提出了一种新的评价标准，通过注册来评价所选地标的有效性。通过打印牙模型的自动标记对该方法进行了验证。关键词:3D打印资格;非刚性形状配准;形状分割;聚类;高斯过程里程碑免责声明作为对作者和研究人员的服务，我们提供此版本的接受稿件(AM)。在最终出版版本记录(VoR)之前，将对该手稿进行编辑、排版和审查。在制作和印前，可能会发现可能影响内容的错误，所有适用于期刊的法律免责声明也与这些版本有关。林伟志，洛杉矶南加州大学Daniel J. Epstein工业与系统工程系的一名博士生。她于2019年在北京航空航天大学获得统计学学士学位。她的研究重点是利用领域知识开发模型来分析复杂的流形数据，特别强调解决先进制造领域的挑战。羌族HuangDr。黄强，美国南加州大学洛杉矶分校Daniel J. Epstein工业与系统工程系教授。主要研究方向为智能制造的机器学习和个性化制造的质量控制。2012年至2016年，他是南加州大学工程学院Gordon S. Marshall早期职业主席。他获得了IISE Fellow奖，ASME Fellow奖，NSF CAREER奖，2021年IEEE案例最佳会议论文奖，2013年IEEE自动化科学与工程交易最佳论文奖等。他拥有增材制造质量控制方面的五项专利。他曾担任IISE Transactions的部门编辑，ASME Transactions的副编辑，Journal of Manufacturing Science and Engineering。
{"title":"Automated Deviation-Aware Landmark Selection for Freeform Product Accuracy Qualification in 3D Printing","authors":"Weizhi Lin, Qiang Huang","doi":"10.1080/24725854.2023.2280606","DOIUrl":"https://doi.org/10.1080/24725854.2023.2280606","url":null,"abstract":"AbstractLandmarks are essential in non-rigid shape registration for identifying the correspondence between designs and actual products. In 3D printing, manual selection of landmarks becomes labor-intensive due to complex product geometries and their non-uniform shape deviations. Automatic selection, however, has to pinpoint landmarks indicative of geometric regions prone to deviations for accuracy qualification. Existing automatic landmarking methods often generate clustered and redundant landmarks for prominent features with high curvatures, compromising the balance between global and local registration errors. To address these issues, we propose an automatic landmark selection method through deviation-aware segmentation and landmarking. As opposed to segmentation for semantic feature identification, deviation-aware segmentation partitions a freeform product for high-curvature region identification. Prone to deviation, these regions are generated through curvature-sensitive remeshing to extract vertices of high curvature and automatic clustering of vertices based on vertex density. Within each segment or high-curvature region, a curvature-weighted function is tailored for the Gaussian process landmarking to sequentially select landmarks with the highest local curvatures. Furthermore, we propose a new evaluation criterion to assess the effectiveness of selected landmarks through registration. The proposed approach is tested through automatic landmarking of printed dental models.Keywords: 3D printing qualificationnon-rigid shape registrationshape segmentationclusteringGaussian process landmarkingDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Additional informationNotes on contributorsWeizhi LinWeizhi Lin is a PhD student in the Daniel J. Epstein Department of Industrial and Systems Engineering at the University of Southern California (USC) in Los Angeles. She completed her B.E. degree in Statistics at Beihang University in 2019. Her research focuses on leveraging domain knowledge to develop models for analyzing complex manifold data, with a specific emphasis on addressing challenges in the field of advanced manufacturing.Qiang HuangDr. Qiang Huang is a professor at the Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles. His research focuses on Machine Learning for Smart Manufacturing and Quality Control for Personalized Manufacturing. He was the holder of the Gordon S. Marshall Early Career Chair in Engineering at USC from 2012 to 2016. He received the IISE Fellow Award, ASME Fellow Award, NS","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135390464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-08DOI: 10.1080/24725854.2023.2281580
Shuchen Cao, Ruizhi Zhang
AbstractIn this paper, we propose an adaptive top-r method to monitor large-scale data streams where the change may affect a set of unknown data streams at some unknown time. Motivated by parallel and distributed computing, we propose to develop global monitoring schemes by parallel running local detection procedures and then use the Benjamin-Hochberg (BH) false discovery rate (FDR) control procedure to estimate the number of changed data streams adaptively. Our approach is illustrated in two concrete examples: one is a homogeneous case when all data streams are i.i.d with the same known pre-change and post-change distributions. The other is when all data are normally distributed, and the mean shifts are unknown and can be positive or negative. Theoretically, we show that when the pre-change and post-change distributions are completely specified, our proposed method can estimate the number of changed data streams for both the pre-change and post-change status. Moreover, we perform simulations and two case studies to show its detection efficiency.Keywords: False discovery rateCUSUMquickest change detectionprocess controlDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
{"title":"An Adaptive Approach for Online Monitoring of Large Scale Data Streams","authors":"Shuchen Cao, Ruizhi Zhang","doi":"10.1080/24725854.2023.2281580","DOIUrl":"https://doi.org/10.1080/24725854.2023.2281580","url":null,"abstract":"AbstractIn this paper, we propose an adaptive top-r method to monitor large-scale data streams where the change may affect a set of unknown data streams at some unknown time. Motivated by parallel and distributed computing, we propose to develop global monitoring schemes by parallel running local detection procedures and then use the Benjamin-Hochberg (BH) false discovery rate (FDR) control procedure to estimate the number of changed data streams adaptively. Our approach is illustrated in two concrete examples: one is a homogeneous case when all data streams are i.i.d with the same known pre-change and post-change distributions. The other is when all data are normally distributed, and the mean shifts are unknown and can be positive or negative. Theoretically, we show that when the pre-change and post-change distributions are completely specified, our proposed method can estimate the number of changed data streams for both the pre-change and post-change status. Moreover, we perform simulations and two case studies to show its detection efficiency.Keywords: False discovery rateCUSUMquickest change detectionprocess controlDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135340531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-07DOI: 10.1080/24725854.2023.2271536
Thomas C. Sharkey, Burcu B. Keskin, Renata Konrad, Maria E. Mayorga
Click to increase image sizeClick to decrease image size AcknowledgmentsWe appreciate the work of Cole Smith on this special issue in coordinating the review process, especially the work done well after his term as the Focus Issue Editor of Operations Engineering and Analytics came to an end. We would also like to acknowledge the contributions of the reviewers of papers submitted to this special issue.
{"title":"Introduction to the Special Issue on Analytical Methods for Detecting, Disrupting, and Dismantling Illicit Operations","authors":"Thomas C. Sharkey, Burcu B. Keskin, Renata Konrad, Maria E. Mayorga","doi":"10.1080/24725854.2023.2271536","DOIUrl":"https://doi.org/10.1080/24725854.2023.2271536","url":null,"abstract":"Click to increase image sizeClick to decrease image size AcknowledgmentsWe appreciate the work of Cole Smith on this special issue in coordinating the review process, especially the work done well after his term as the Focus Issue Editor of Operations Engineering and Analytics came to an end. We would also like to acknowledge the contributions of the reviewers of papers submitted to this special issue.","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135474910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-02DOI: 10.1080/24725854.2023.2274898
Jingtong Zhao, Xin Pan, Van-Anh Truong, Jie Song
AbstractIn online platforms, the reviews posted by consumers who arrive earlier are playing an increasingly important role in the purchasing decisions of consumers who arrive later. Motivated by this observation, we study the problems faced by a platform selling a single product with no capacity constraint, where the demand is explicitly influenced by the reviews presented to the consumers. More precisely, we model a consumer’s browsing of reviews for a single product as following a cascade click model, with each consumer seeing some initial number of reviews and forming a utility estimate for the product based on the reviews the consumer has read. In the first part of the paper, we consider how to rank the reviews to induce short- and long-term revenue-maximizing purchasing behaviors. In the second part, we study how to set the price of the product. We derive structural insights and bounds on both problems. We also consider the case that the parameters of the model are unknown, where we propose algorithms that learn the parameters and optimize the ranking of the reviews or the price online. We show that our algorithms have regrets O(T23).Keywords: Analysis of algorithmsApproximations/heuristicsRevenue managementDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
{"title":"Ranking and Pricing under a Cascade Model of Consumer Review Browsing","authors":"Jingtong Zhao, Xin Pan, Van-Anh Truong, Jie Song","doi":"10.1080/24725854.2023.2274898","DOIUrl":"https://doi.org/10.1080/24725854.2023.2274898","url":null,"abstract":"AbstractIn online platforms, the reviews posted by consumers who arrive earlier are playing an increasingly important role in the purchasing decisions of consumers who arrive later. Motivated by this observation, we study the problems faced by a platform selling a single product with no capacity constraint, where the demand is explicitly influenced by the reviews presented to the consumers. More precisely, we model a consumer’s browsing of reviews for a single product as following a cascade click model, with each consumer seeing some initial number of reviews and forming a utility estimate for the product based on the reviews the consumer has read. In the first part of the paper, we consider how to rank the reviews to induce short- and long-term revenue-maximizing purchasing behaviors. In the second part, we study how to set the price of the product. We derive structural insights and bounds on both problems. We also consider the case that the parameters of the model are unknown, where we propose algorithms that learn the parameters and optimize the ranking of the reviews or the price online. We show that our algorithms have regrets O(T23).Keywords: Analysis of algorithmsApproximations/heuristicsRevenue managementDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135933218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-31DOI: 10.1080/24725854.2023.2275166
Ryan B. Christianson, Robert B. Gramacy
AbstractBayesian Optimization (BO) links Gaussian Process (GP) surrogates with sequential design toward optimizing expensive-to-evaluate black-box functions. Example design heuristics, or so-called acquisition functions, like expected improvement (EI), balance exploration and exploitation to furnish global solutions under stringent evaluation budgets. However, they fall short when solving for robust optima, meaning a preference for solutions in a wider domain of attraction. Robust solutions are useful when inputs are imprecisely specified, or where a series of solutions is desired. A common mathematical programming technique in such settings involves an adversarial objective, biasing a local solver away from “sharp” troughs. Here we propose a surrogate modeling and active learning technique called robust expected improvement (REI) that ports adversarial methodology into the BO/GP framework. After describing the methods, we illustrate and draw comparisons to several competitors on benchmark synthetic exercises and real problems of varying complexity.Keywords: Robust OptimizationGaussian ProcessActive LearningSequential DesignDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
{"title":"Robust expected improvement for Bayesian optimization","authors":"Ryan B. Christianson, Robert B. Gramacy","doi":"10.1080/24725854.2023.2275166","DOIUrl":"https://doi.org/10.1080/24725854.2023.2275166","url":null,"abstract":"AbstractBayesian Optimization (BO) links Gaussian Process (GP) surrogates with sequential design toward optimizing expensive-to-evaluate black-box functions. Example design heuristics, or so-called acquisition functions, like expected improvement (EI), balance exploration and exploitation to furnish global solutions under stringent evaluation budgets. However, they fall short when solving for robust optima, meaning a preference for solutions in a wider domain of attraction. Robust solutions are useful when inputs are imprecisely specified, or where a series of solutions is desired. A common mathematical programming technique in such settings involves an adversarial objective, biasing a local solver away from “sharp” troughs. Here we propose a surrogate modeling and active learning technique called robust expected improvement (REI) that ports adversarial methodology into the BO/GP framework. After describing the methods, we illustrate and draw comparisons to several competitors on benchmark synthetic exercises and real problems of varying complexity.Keywords: Robust OptimizationGaussian ProcessActive LearningSequential DesignDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-20DOI: 10.1080/24725854.2023.2273373
Seulchan Lee, Alexandar Angelus, Jon M. Stauffer, Chelliah Sriskandarajah
AbstractMotivated by the supply chain of our oil-field service industry partner, we study shipping, collaboration, and outsourcing decisions in a decentralized, three-stage supply chain consisting of suppliers, a hybrid cross-dock facility, and oil well facilities. Unlike pure cross-docking, which transships arriving products quickly downstream, hybrid cross-docking allows for inventory to remain at the cross-dock for multiple periods. We formulate multi-period, optimization models to minimize costs of different members in a hybrid cross-docking supply chain and establish structural properties of optimal solutions. We make use of those results to identify conditions under which hybrid cross-docking is more cost efficient than pure cross-docking. Our results provide managerial insights regarding when a hybrid cross-dock should be enabled, and the value of the resulting cost savings. We also quantify the value of collaboration among different stages in the supply chain. Upstream collaboration results in 1% to 9% average cost savings for the cross-dock, while downstream collaboration generates 4% to 13% in average cost savings for oil well facilities, depending on the number of products and their holding cost. We also develop a Stackelberg pricing game between a logistics company and oil well facilities seeking to lower their costs by outsourcing their transportation and inventory operations. We identify the structure of oil well facilities’ best response to the price of outsourcing services, as well as the structure of the logistics provider’s optimal pricing policy. Our findings and models, based on current literature, provide application focused tools that allow managers to improve cross-docking operations in their supply chains, realize the benefits of collaborations, and make better outsourcing decisions.Keywords: Cross-dockingOutsourcingOil-field serviceDynamic lot sizingDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Data Availability StatementDue to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.
{"title":"Optimal Shipping, Collaboration, and Outsourcing Decisions in a Hybrid Cross-docking Supply Chain","authors":"Seulchan Lee, Alexandar Angelus, Jon M. Stauffer, Chelliah Sriskandarajah","doi":"10.1080/24725854.2023.2273373","DOIUrl":"https://doi.org/10.1080/24725854.2023.2273373","url":null,"abstract":"AbstractMotivated by the supply chain of our oil-field service industry partner, we study shipping, collaboration, and outsourcing decisions in a decentralized, three-stage supply chain consisting of suppliers, a hybrid cross-dock facility, and oil well facilities. Unlike pure cross-docking, which transships arriving products quickly downstream, hybrid cross-docking allows for inventory to remain at the cross-dock for multiple periods. We formulate multi-period, optimization models to minimize costs of different members in a hybrid cross-docking supply chain and establish structural properties of optimal solutions. We make use of those results to identify conditions under which hybrid cross-docking is more cost efficient than pure cross-docking. Our results provide managerial insights regarding when a hybrid cross-dock should be enabled, and the value of the resulting cost savings. We also quantify the value of collaboration among different stages in the supply chain. Upstream collaboration results in 1% to 9% average cost savings for the cross-dock, while downstream collaboration generates 4% to 13% in average cost savings for oil well facilities, depending on the number of products and their holding cost. We also develop a Stackelberg pricing game between a logistics company and oil well facilities seeking to lower their costs by outsourcing their transportation and inventory operations. We identify the structure of oil well facilities’ best response to the price of outsourcing services, as well as the structure of the logistics provider’s optimal pricing policy. Our findings and models, based on current literature, provide application focused tools that allow managers to improve cross-docking operations in their supply chains, realize the benefits of collaborations, and make better outsourcing decisions.Keywords: Cross-dockingOutsourcingOil-field serviceDynamic lot sizingDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Data Availability StatementDue to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135570102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-16DOI: 10.1080/24725854.2023.2271027
Haitao Liu, Ping Cao, Loo Hay Lee, Ek Peng Chew
AbstractAbstract–We consider a feasibility determination problem via simulation with stochastic binary outcomes, in which the design space can be either discrete or continuous, and outcomes can be predicted through a functional relationship that depends on linear combinations of design variables. The goal is to identify all the feasible designs with means (i.e., probabilities) no smaller than a threshold. A logistic model is used to capture the relationship between the probability and design variables. Traditional binary rewards often conceal the numbers of correct and false determinations, thereby being inefficient in large and continuous design spaces. We thus propose a similarity measure to smooth binary rewards. Then, a sampling policy that optimizes a so-called similarity differential (SD) is developed. Under some mild conditions, we show that the SD policy is capable of identifying all the feasible designs as the sampling budget goes to infinity. Two approximate versions of the SD policy are developed to sequentially determine the sampling decisions in large and continuous design spaces. Extensive numerical experiments are conducted to demonstrate the superior performance of our SD policy, document computational savings, and reveal underlying sampling behaviors. Alternatively, we provide a simple but effective heuristic that can be easily used by practitioners.Keywords: simulationfeasibility determinationbinary outcomesoptimal computing budget allocationexperimental designDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. AcknowledgementsWe thank the editors and anonymous reviewers for valuable comments. This paper is supported by the National Science Foundation of China [Grant No. 72301187, 72122019, and 71771202], and by the Fundamental Research Funds for the Central Universities [Grant No. SXYPY202346]Additional informationNotes on contributorsHaitao LiuHaitao Liu received his Ph.D. degree in the Department of Industrial Systems Engineering and Management at National University of Singapore in 2022. He is currently an associate professor in Business School at Sichuan University. His research interests include simulation optimization, statistical learning, and supply chain management.Ping CaoPing Cao received his Ph.D. degree in Operational Research at Academy of Mathematics and Systems Science, Chinese Academy of Science in 2011. He is currently a professor at the School of Management in University of Science and Technology of China. He His research interests include stochastic control, queueing theory, Markov decision process, and dynamic pricing in reve
摘要:本文考虑了一个具有随机二元结果的模拟可行性确定问题，其中设计空间可以是离散的，也可以是连续的，并且结果可以通过依赖于设计变量的线性组合的函数关系来预测。目标是用不小于阈值的方法(即概率)识别所有可行的设计。逻辑模型用于捕获概率和设计变量之间的关系。传统的二元奖励通常会隐藏正确和错误决定的数量，因此在大型和连续的设计空间中效率低下。因此，我们提出了平滑二元奖励的相似性度量。然后，开发了一种优化所谓相似性差分(SD)的抽样策略。在一些温和的条件下，我们证明了当抽样预算趋于无穷时，SD策略能够识别出所有可行的设计。开发了两个近似版本的SD策略，以顺序确定大型连续设计空间中的采样决策。进行了大量的数值实验，以证明我们的SD策略的优越性能，记录计算节省，并揭示潜在的采样行为。另外，我们提供了一个简单但有效的启发式，可以很容易地被实践者使用。关键词:模拟可行性确定二元结果最优计算预算分配实验设计免责声明作为对作者和研究人员的服务，我们提供此版本的接受稿件(AM)。在最终出版版本记录(VoR)之前，将对该手稿进行编辑、排版和审查。在制作和印前，可能会发现可能影响内容的错误，所有适用于期刊的法律免责声明也与这些版本有关。感谢编辑和匿名审稿人的宝贵意见。国家自然科学基金[资助号:72301187,72122019,71771202];中央高校基本科研业务费专项基金[资助号:71771202];刘海涛于2022年毕业于新加坡国立大学工业系统工程与管理系，获博士学位。现任四川大学商学院副教授。他的研究兴趣包括仿真优化、统计学习和供应链管理。曹平，2011年获中国科学院数学与系统科学研究院运筹学博士学位。现任中国科学技术大学管理学院教授。主要研究方向为随机控制、排队理论、马尔可夫决策过程、收益管理中的动态定价等。Loo Hay Lee，获美国哈佛大学工程科学博士学位。他曾任新加坡国立大学工业系统工程与管理系教授。他的研究兴趣包括物流、车辆路线、供应链建模和基于仿真的优化。周泽鹏博士毕业于美国佐治亚理工学院工业工程专业。他目前是新加坡国立大学工业系统工程与管理系的教授。主要研究方向为港口物流、海运、库存管理。
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Pub Date : 2023-10-16DOI: 10.1080/24725854.2023.2272261
Kefei Liu, Zhibin Jiang, Liping Zhou
AbstractMotivated by make-to-order applications with committed delivery dates in a variety of industries, we investigate the integrated multi-plant collaborative production, inventory, and hub-spoke delivery problem in a complex production-distribution network. This network includes multi-location heterogeneous plants, distribution centers, and customers, for producing customized and splittable orders with one or more general-size multi-type jobs. Completed jobs are transported from plants to distribution centers, and then the orders whose all constituent jobs have arrived are delivered from distribution centers to customer sites. The objective is to make integrated scheduling decisions for production, inventory, and delivery, for minimizing total cost composed of production, transportation, tardiness, and inventory. We first formulate this problem as a mixed-integer programming model, and analyze its intractability by proving that the problem is NP-hard and no approximation algorithms exist with a constant worst-case ratio. We then reformulate this problem as a binary integer linear programming model to select a feasible schedule for each job, and propose a combined column generation and two-layer column enumeration algorithm to solve it. Through extensive numerical experiments, we demonstrate that our proposed algorithm is capable of generating optimal or near-optimal solutions expeditiously and outperforms four benchmark approaches, and gain valuable managerial insights for practitioners.Keywords: Customized and splittable ordersintegrated schedulingmulti-plant production and hub-spoke deliverymixed-integer programmingcolumn generation and column enumerationDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Additional informationNotes on contributorsKefei LiuKefei Liu is a Ph.D. candidate in Management Science and Engineering from Antai College of Economics & Management, Shanghai Jiao Tong University (SJTU), Shanghai, China. Her main research interests include operations management of manufacturing systems.Zhibin JiangZhibin Jiang is currently a distinguished Professor with the Antai College of Economics & Management, SJTU, Shanghai, China. He is also the Dean of the Sino-US Global Logistics Institute of SJTU. He received a Ph.D. degree in Engineering Management from the City University of Hong Kong, Hong Kong, China, in 1999. He is a fellow of the Institute of Industrial and Systems Engineers and an Associate Editor of the International Journal of Production Research. His research interests include discrete-event modeling and simulation, and operations managem
{"title":"Integrated multi-plant collaborative production, inventory, and hub-spoke delivery of make-to-order products","authors":"Kefei Liu, Zhibin Jiang, Liping Zhou","doi":"10.1080/24725854.2023.2272261","DOIUrl":"https://doi.org/10.1080/24725854.2023.2272261","url":null,"abstract":"AbstractMotivated by make-to-order applications with committed delivery dates in a variety of industries, we investigate the integrated multi-plant collaborative production, inventory, and hub-spoke delivery problem in a complex production-distribution network. This network includes multi-location heterogeneous plants, distribution centers, and customers, for producing customized and splittable orders with one or more general-size multi-type jobs. Completed jobs are transported from plants to distribution centers, and then the orders whose all constituent jobs have arrived are delivered from distribution centers to customer sites. The objective is to make integrated scheduling decisions for production, inventory, and delivery, for minimizing total cost composed of production, transportation, tardiness, and inventory. We first formulate this problem as a mixed-integer programming model, and analyze its intractability by proving that the problem is NP-hard and no approximation algorithms exist with a constant worst-case ratio. We then reformulate this problem as a binary integer linear programming model to select a feasible schedule for each job, and propose a combined column generation and two-layer column enumeration algorithm to solve it. Through extensive numerical experiments, we demonstrate that our proposed algorithm is capable of generating optimal or near-optimal solutions expeditiously and outperforms four benchmark approaches, and gain valuable managerial insights for practitioners.Keywords: Customized and splittable ordersintegrated schedulingmulti-plant production and hub-spoke deliverymixed-integer programmingcolumn generation and column enumerationDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Additional informationNotes on contributorsKefei LiuKefei Liu is a Ph.D. candidate in Management Science and Engineering from Antai College of Economics & Management, Shanghai Jiao Tong University (SJTU), Shanghai, China. Her main research interests include operations management of manufacturing systems.Zhibin JiangZhibin Jiang is currently a distinguished Professor with the Antai College of Economics & Management, SJTU, Shanghai, China. He is also the Dean of the Sino-US Global Logistics Institute of SJTU. He received a Ph.D. degree in Engineering Management from the City University of Hong Kong, Hong Kong, China, in 1999. He is a fellow of the Institute of Industrial and Systems Engineers and an Associate Editor of the International Journal of Production Research. His research interests include discrete-event modeling and simulation, and operations managem","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136079823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-05DOI: 10.1080/24725854.2023.2266001
Jun Xu, Jie Zhou, Xiaofang Huang, Kaibo Wang
AbstractMultimode processes are common in modern industry and refer to processes that work in multiple operating modes. Motivated by the torque control process of a wind turbine, we determine that there exist two types of changes in multimode processes: (1) mode transitions and (2) parameter changes. Detecting both types of changes is an important issue in practice, but existing methods mainly consider one type of change and thus do not work well. To address this issue, we propose a novel modeling framework for the offline change point detection problem of multimode processes, which simultaneously considers mode transitions and parameter changes. We characterize each mode with a parametric cost function and formulate the problem as an optimization model. In the model, two penalty terms penalize the number of change points, and a series of constraints specify the multimode characteristics. With certain assumptions, the asymptotic property ensures the accuracy of the model solution. To solve the model, we propose an iterative algorithm and develop a multimode-pruned exact linear time (multi-PELT) method for initialization. The simulation study and the real case study demonstrate the effectiveness of our method against the state-of-the-art methods in terms of the accuracy of change point detection, mode identification, and parameter estimation.Keywords: Change point detectionconstrained optimization modelmultimode processeswind turbine torque controlDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Additional informationNotes on contributorsJun XuJun Xu is currently a Ph.D. student in Department of Industrial Engineering, Tsinghua University. He received his B.Eng. degree in Industrial Engineering from Tsinghua University in 2019. His research interests include modeling, monitoring, change detection and diagnosis of complex systems.Jie ZhouJie Zhou is a senior engineer in Goldwind Science & Technology Co.,Ltd, Beijing, China. He is focusing on wind turbine diagnosis and safety control. He is also currently working towards the D.Eng. degree in Industrial Engineering with Tsinghua University, Beijing, China. He received his B.S. and M.S. degrees in Electrical Engineering from Dalian University of Technology, Dalian, China.Xiaofang HuangXiaofang Huang is a senior engineer. She received her master's degree from Xidian University, Xi'an, China in 2006. She is currently a department lead of the R&D Center of Goldwind Science & Technology Co.,Ltd, mainly engaged in the development and localization of wind turbine main control system software, as well as the development of
{"title":"Change Point Detection of Multimode Processes Considering Both Mode Transitions and Parameter Changes","authors":"Jun Xu, Jie Zhou, Xiaofang Huang, Kaibo Wang","doi":"10.1080/24725854.2023.2266001","DOIUrl":"https://doi.org/10.1080/24725854.2023.2266001","url":null,"abstract":"AbstractMultimode processes are common in modern industry and refer to processes that work in multiple operating modes. Motivated by the torque control process of a wind turbine, we determine that there exist two types of changes in multimode processes: (1) mode transitions and (2) parameter changes. Detecting both types of changes is an important issue in practice, but existing methods mainly consider one type of change and thus do not work well. To address this issue, we propose a novel modeling framework for the offline change point detection problem of multimode processes, which simultaneously considers mode transitions and parameter changes. We characterize each mode with a parametric cost function and formulate the problem as an optimization model. In the model, two penalty terms penalize the number of change points, and a series of constraints specify the multimode characteristics. With certain assumptions, the asymptotic property ensures the accuracy of the model solution. To solve the model, we propose an iterative algorithm and develop a multimode-pruned exact linear time (multi-PELT) method for initialization. The simulation study and the real case study demonstrate the effectiveness of our method against the state-of-the-art methods in terms of the accuracy of change point detection, mode identification, and parameter estimation.Keywords: Change point detectionconstrained optimization modelmultimode processeswind turbine torque controlDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Additional informationNotes on contributorsJun XuJun Xu is currently a Ph.D. student in Department of Industrial Engineering, Tsinghua University. He received his B.Eng. degree in Industrial Engineering from Tsinghua University in 2019. His research interests include modeling, monitoring, change detection and diagnosis of complex systems.Jie ZhouJie Zhou is a senior engineer in Goldwind Science & Technology Co.,Ltd, Beijing, China. He is focusing on wind turbine diagnosis and safety control. He is also currently working towards the D.Eng. degree in Industrial Engineering with Tsinghua University, Beijing, China. He received his B.S. and M.S. degrees in Electrical Engineering from Dalian University of Technology, Dalian, China.Xiaofang HuangXiaofang Huang is a senior engineer. She received her master's degree from Xidian University, Xi'an, China in 2006. She is currently a department lead of the R&D Center of Goldwind Science & Technology Co.,Ltd, mainly engaged in the development and localization of wind turbine main control system software, as well as the development of ","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134974997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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