Pub Date : 2023-06-09DOI: 10.1080/24725854.2023.2222162
Wei-Kai Lin, Cesar Ruiz, Matan Aroosh, H. Ben‐Yoav, Qiang Huang
{"title":"Multiresolution Functional Characterization and Correction of Biofouling for Improved Biosensing Efficacy","authors":"Wei-Kai Lin, Cesar Ruiz, Matan Aroosh, H. Ben‐Yoav, Qiang Huang","doi":"10.1080/24725854.2023.2222162","DOIUrl":"https://doi.org/10.1080/24725854.2023.2222162","url":null,"abstract":"","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46583258","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 : 2023-06-08DOI: 10.1080/24725854.2023.2223245
Di Wang, Changyue Song, Xi Zhang
{"title":"Multimodal Regression and Mode Recognition via An Integrated Deep Neural Network","authors":"Di Wang, Changyue Song, Xi Zhang","doi":"10.1080/24725854.2023.2223245","DOIUrl":"https://doi.org/10.1080/24725854.2023.2223245","url":null,"abstract":"","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49109241","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 : 2023-06-08DOI: 10.1080/24725854.2023.2223246
Xuecheng Yin, Sabah Bushaj, Yue Yuan, I. E. Büyüktahtakin
This paper presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location-allocation mixed-integer programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
{"title":"COVID-19: Agent-Based Simulation-Optimization to Vaccine Center Location Vaccine Allocation Problem","authors":"Xuecheng Yin, Sabah Bushaj, Yue Yuan, I. E. Büyüktahtakin","doi":"10.1080/24725854.2023.2223246","DOIUrl":"https://doi.org/10.1080/24725854.2023.2223246","url":null,"abstract":"This paper presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location-allocation mixed-integer programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49610286","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 : 2023-06-08DOI: 10.1080/24725854.2023.2222402
Hui Wu, Yanfang Li
{"title":"A Multi-Sensor Fusion-based Prognostic Model for Systems with Partially Observable Failure Modes","authors":"Hui Wu, Yanfang Li","doi":"10.1080/24725854.2023.2222402","DOIUrl":"https://doi.org/10.1080/24725854.2023.2222402","url":null,"abstract":"","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47281480","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 : 2023-06-02DOI: 10.1080/24725854.2023.2220772
Hanqi Wen, Jingtong Zhao, Van-Anh Truong, Jie Song
{"title":"Dynamic Expansions of Social Followings with Lotteries and Give-aways","authors":"Hanqi Wen, Jingtong Zhao, Van-Anh Truong, Jie Song","doi":"10.1080/24725854.2023.2220772","DOIUrl":"https://doi.org/10.1080/24725854.2023.2220772","url":null,"abstract":"","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48150283","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 : 2023-05-22DOI: 10.1080/24725854.2023.2217248
Byeongmok Kim, Jong Gwang Kim, Seokcheon Lee
The COVID-19 pandemic has significantly disrupted global supply chains (SCs), emphasizing the importance of SC resilience, which refers to the ability of SCs to return to their original or more desirable state following disruptions. This study focuses on collaboration, a key component of SC resilience, and proposes a novel collaborative structure that incorporates a fictitious agent to manage inventory transshipment decisions between retailers in a centralized manner while maintaining the retailers' autonomy in ordering. The proposed collaborative structure offers the following advantages from SC resilience and operational perspectives: (1) it facilitates decision synchronization for enhanced collaboration among retailers, and (2) it allows retailers to collaborate without the need for information sharing, addressing the potential issue of information sharing reluctance. Additionally, this study employs non-stationary probability to capture the deeply uncertain nature of the ripple effect and the highly volatile customer demand caused by the pandemic. A new reinforcement learning (RL) algorithm is developed to handle non-stationary environments and to implement the proposed collaborative structure. Experimental results demonstrate that the proposed collaborative structure using the new RL algorithm achieves superior SC resilience compared with centralized inventory management systems with transshipment and decentralized inventory management systems without transshipment using traditional RL algorithms. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
2019冠状病毒病大流行严重扰乱了全球供应链，强调了供应链复原力的重要性，这是指供应链在中断后恢复到原始或更理想状态的能力。本研究关注供应链弹性的关键组成部分协作，并提出了一种新的协作结构，该结构包含一个虚拟代理，以集中的方式管理零售商之间的库存转运决策，同时保持零售商的订购自主权。从供应链弹性和运营角度来看，所提出的协作结构具有以下优势:(1)它促进了零售商之间协作的决策同步;(2)它允许零售商在不需要信息共享的情况下进行协作，解决了信息共享不情愿的潜在问题。此外，本研究采用非平稳概率来捕捉大流行引起的连锁反应的高度不确定性和客户需求的高度波动性。提出了一种新的强化学习(RL)算法来处理非平稳环境并实现所提出的协作结构。实验结果表明，与采用传统RL算法的集中库存管理系统和不采用转运的分散库存管理系统相比，采用新RL算法的协同结构具有更好的供应链弹性。IISE Transactions的版权是Taylor & Francis Ltd的财产，未经版权所有者的明确书面许可，其内容不得复制或通过电子邮件发送到多个网站或发布到listserv。但是，用户可以打印、下载或通过电子邮件发送文章供个人使用。这可以删节。对副本的准确性不作任何保证。用户应参阅原始出版版本的材料的完整。(版权适用于所有人。)
{"title":"A multi-agent reinforcement learning model for inventory transshipments under supply chain disruption","authors":"Byeongmok Kim, Jong Gwang Kim, Seokcheon Lee","doi":"10.1080/24725854.2023.2217248","DOIUrl":"https://doi.org/10.1080/24725854.2023.2217248","url":null,"abstract":"The COVID-19 pandemic has significantly disrupted global supply chains (SCs), emphasizing the importance of SC resilience, which refers to the ability of SCs to return to their original or more desirable state following disruptions. This study focuses on collaboration, a key component of SC resilience, and proposes a novel collaborative structure that incorporates a fictitious agent to manage inventory transshipment decisions between retailers in a centralized manner while maintaining the retailers' autonomy in ordering. The proposed collaborative structure offers the following advantages from SC resilience and operational perspectives: (1) it facilitates decision synchronization for enhanced collaboration among retailers, and (2) it allows retailers to collaborate without the need for information sharing, addressing the potential issue of information sharing reluctance. Additionally, this study employs non-stationary probability to capture the deeply uncertain nature of the ripple effect and the highly volatile customer demand caused by the pandemic. A new reinforcement learning (RL) algorithm is developed to handle non-stationary environments and to implement the proposed collaborative structure. Experimental results demonstrate that the proposed collaborative structure using the new RL algorithm achieves superior SC resilience compared with centralized inventory management systems with transshipment and decentralized inventory management systems without transshipment using traditional RL algorithms. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43890912","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 : 2023-05-18DOI: 10.1080/24725854.2023.2215843
Mingda Liu, Yanlu Zhao, Xiaolei Xie
Abstract As the aging population grows, the demand for long-term continuously Attended Home Healthcare (AHH) services has increased significantly in recent years. AHH services are beneficial since they not only alleviate the pressure on hospital resources, but also provide more convenient care for patients. However, how to reasonably assign patients to doctors and arrange their visiting sequences is still a challenging task due to various complex factors such as heterogeneous doctors, skill-matching requirements, continuity of care, and uncertain travel and service times. Motivated by a practical problem faced by an AHH service provider, we investigate a deterministic continuity-skill-restricted scheduling and routing problem (CSRP) and its stochastic variant (SCSRP) to address these operational challenges. The problem is formulated as a heterogeneous site-dependent and consistent vehicle routing problem with time windows. However, there is not a compact model and a practically implementable exact algorithm in the literature to solve such a complicated problem. To fill this gap, we propose a branch-price-and-cut algorithm to solve the CSRP and a discrete-approximation-method adaption for the SCSRP. Extensive numerical experiments and a real case study verify the effectiveness and efficiency of the proposed algorithms and provide managerial insights for AHH service providers to achieve better performance.
{"title":"Continuity-skill-restricted Scheduling and Routing Problem: Formulation, Optimization and Implications","authors":"Mingda Liu, Yanlu Zhao, Xiaolei Xie","doi":"10.1080/24725854.2023.2215843","DOIUrl":"https://doi.org/10.1080/24725854.2023.2215843","url":null,"abstract":"Abstract As the aging population grows, the demand for long-term continuously Attended Home Healthcare (AHH) services has increased significantly in recent years. AHH services are beneficial since they not only alleviate the pressure on hospital resources, but also provide more convenient care for patients. However, how to reasonably assign patients to doctors and arrange their visiting sequences is still a challenging task due to various complex factors such as heterogeneous doctors, skill-matching requirements, continuity of care, and uncertain travel and service times. Motivated by a practical problem faced by an AHH service provider, we investigate a deterministic continuity-skill-restricted scheduling and routing problem (CSRP) and its stochastic variant (SCSRP) to address these operational challenges. The problem is formulated as a heterogeneous site-dependent and consistent vehicle routing problem with time windows. However, there is not a compact model and a practically implementable exact algorithm in the literature to solve such a complicated problem. To fill this gap, we propose a branch-price-and-cut algorithm to solve the CSRP and a discrete-approximation-method adaption for the SCSRP. Extensive numerical experiments and a real case study verify the effectiveness and efficiency of the proposed algorithms and provide managerial insights for AHH service providers to achieve better performance.","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45679492","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 : 2023-05-12DOI: 10.1080/24725854.2023.2213754
Adam Schmidt, Laura A. Albert
,
，
{"title":"The drop box location problem","authors":"Adam Schmidt, Laura A. Albert","doi":"10.1080/24725854.2023.2213754","DOIUrl":"https://doi.org/10.1080/24725854.2023.2213754","url":null,"abstract":",","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44128006","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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