Pub Date : 2023-12-03DOI: 10.1080/03155986.2023.2287995
Dawei Wang, Xiaoqi Zhang, Sheng Ang, Feng Yang
The metafrontier data envelopment analysis (DEA) model is a popular evaluation technique when different decision-making units (DMUs) may exhibit production technology heterogeneity. In this framewo...
{"title":"Aggregation of meta-technology ratio in DEA framework using the evidential reasoning approach","authors":"Dawei Wang, Xiaoqi Zhang, Sheng Ang, Feng Yang","doi":"10.1080/03155986.2023.2287995","DOIUrl":"https://doi.org/10.1080/03155986.2023.2287995","url":null,"abstract":"The metafrontier data envelopment analysis (DEA) model is a popular evaluation technique when different decision-making units (DMUs) may exhibit production technology heterogeneity. In this framewo...","PeriodicalId":13645,"journal":{"name":"Infor","volume":"194 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-29DOI: 10.1080/03155986.2023.2287997
Zhongmiao Sun, Qi Xu, Jinrong Liu
Blockchain technology is very useful for combating counterfeits and verifying the authenticity of products for consumers. This paper studies blockchain adoption in a two-level supply chain consisti...
区块链技术对于打击假冒产品和为消费者验证产品的真实性非常有用。本文研究了两级供应链中区块链的采用。
{"title":"Is blockchain technology desirable? When considering power structures and consumer preference for blockchain","authors":"Zhongmiao Sun, Qi Xu, Jinrong Liu","doi":"10.1080/03155986.2023.2287997","DOIUrl":"https://doi.org/10.1080/03155986.2023.2287997","url":null,"abstract":"Blockchain technology is very useful for combating counterfeits and verifying the authenticity of products for consumers. This paper studies blockchain adoption in a two-level supply chain consisti...","PeriodicalId":13645,"journal":{"name":"Infor","volume":"27 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138537544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.1080/03155986.2023.2264915
Behzad Minaei, Hadi Akbarzadeh Khorshidi, Uwe Aickelin, Arash Geramian
AbstractThis study aims to enhance computational and analytical aspects of multi-criteria group decision-making (MCGDM) under uncertainty. For this, we use the best-worst method (BWM) and cloud models to develop a more reliable MCGDM algorithm including three stages: first, collecting data through the BWM reference pairwise comparison; second, extracting interval-weights using the BWM bi-level optimisation models and aggregating different opinions via cloud models; and third, using the technique for order of preference by similarity to ideal solution (TOPSIS) to prioritise alternatives. We have also investigated the effectiveness of the proposed approach in a real-life problem of online learning platform selection within the context of the COVID-19 pandemic lockdown. The experiment results demonstrate the superiority of the proposed method over the Bayesian BWM in terms of computational time by 96%. Moreover, the proposed approach outperforms BWM and Bayesian BWM techniques by 33% and 25%, respectively, in terms of conformity to the decision-makers’ intuitive judgments. Our findings also bring important practical implications. Application of the proposed method led to robustness against the number of decision-makers and significantly increased time efficiency in group decision-making. Besides, the computations with the lower inconsistency enhanced the effectiveness of prioritisation in group decision-making.Keywords: Multiple criteria group decision-makinguncertaintycloud modelsinterval weights AcknowledgementsThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. No potential conflict of interest was reported by the authors. The data that support the findings of this study are available from the corresponding author.Disclosure statementNo potential conflict of interest was reported by the authors.
{"title":"Cloud model-based best-worst method for group decision making under uncertainty","authors":"Behzad Minaei, Hadi Akbarzadeh Khorshidi, Uwe Aickelin, Arash Geramian","doi":"10.1080/03155986.2023.2264915","DOIUrl":"https://doi.org/10.1080/03155986.2023.2264915","url":null,"abstract":"AbstractThis study aims to enhance computational and analytical aspects of multi-criteria group decision-making (MCGDM) under uncertainty. For this, we use the best-worst method (BWM) and cloud models to develop a more reliable MCGDM algorithm including three stages: first, collecting data through the BWM reference pairwise comparison; second, extracting interval-weights using the BWM bi-level optimisation models and aggregating different opinions via cloud models; and third, using the technique for order of preference by similarity to ideal solution (TOPSIS) to prioritise alternatives. We have also investigated the effectiveness of the proposed approach in a real-life problem of online learning platform selection within the context of the COVID-19 pandemic lockdown. The experiment results demonstrate the superiority of the proposed method over the Bayesian BWM in terms of computational time by 96%. Moreover, the proposed approach outperforms BWM and Bayesian BWM techniques by 33% and 25%, respectively, in terms of conformity to the decision-makers’ intuitive judgments. Our findings also bring important practical implications. Application of the proposed method led to robustness against the number of decision-makers and significantly increased time efficiency in group decision-making. Besides, the computations with the lower inconsistency enhanced the effectiveness of prioritisation in group decision-making.Keywords: Multiple criteria group decision-makinguncertaintycloud modelsinterval weights AcknowledgementsThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. No potential conflict of interest was reported by the authors. The data that support the findings of this study are available from the corresponding author.Disclosure statementNo potential conflict of interest was reported by the authors.","PeriodicalId":13645,"journal":{"name":"Infor","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135968147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-06DOI: 10.1080/03155986.2023.2264985
Maryam Akbari-Moghaddam, Na Li, Douglas G. Down, Donald M. Arnold, Jeannie Callum, Philippe Bégin, Nancy M. Heddle
AbstractEpidemics are a serious public health threat, and the resources for mitigating their effects are typically limited. Decision-makers face challenges in forecasting the supply and demand for these resources as prior information about the disease is often not available, the behaviour of the disease can periodically change (either naturally or as a result of public health policies) and can differ by geographical region. In this work, we discuss a model that is suitable for short-term real-time supply and demand forecasting during emerging outbreaks. We consider a case study of demand forecasting and allocating scarce quantities of COVID-19 Convalescent Plasma (CCP) in an international multi-site Randomized Controlled Trial (RCT) involving multiple hospital hubs across Canada (excluding Québec). We propose a data-driven mixed-integer programming (MIP) resource allocation model that assigns available resources to maximize a notion of fairness among the resource-demanding entities. Numerical results from applying our MIP model to the case study suggest that our approach can help balance the supply and demand of limited products such as CCP and minimize the unmet demand ratios of the demand entities. We analyse the sensitivity of our model to different allocation settings and show that our model assigns equitable allocations across the entities.Keywords: Resource allocationepidemicsCOVID-19 Convalescent Plasmadata-driven optimizationdemand forecasting AcknowledgmentsThe authors would like to thank Julie Carruthers, Erin Jamula, and Melanie St John at the McMaster Centre for Transfusion Research for their administrative support.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementDue to the ethically, legally, and commercially sensitive nature of the research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.Additional informationFundingThis work was supported by Mitacs Research Training Award (Award IT22358), the McMaster Centre for Transfusion Research, and the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant Program (RGPIN-2016-04518 and RGPIN-2022-02999).
流行病是一种严重的公共卫生威胁，用于减轻其影响的资源通常是有限的。决策者在预测这些资源的供应和需求方面面临挑战，因为通常无法获得有关该病的先前信息，该病的行为可能会周期性变化(自然变化或公共卫生政策的结果)，并且可能因地理区域而异。在这项工作中，我们讨论了一个适合于新爆发期间短期实时供需预测的模型。我们考虑了一项涉及加拿大多家医院中心(不包括qusamubec)的国际多地点随机对照试验(RCT)中需求预测和分配稀缺数量的COVID-19恢复期血浆(CCP)的案例研究。我们提出了一个数据驱动的混合整数规划(MIP)资源分配模型，该模型分配可用资源以最大限度地提高资源需求实体之间的公平性。将我们的MIP模型应用于案例研究的数值结果表明，我们的方法可以帮助平衡有限产品(如CCP)的供需，并最小化需求实体的未满足需求比率。我们分析了我们的模型对不同分配设置的敏感性，并表明我们的模型在实体之间分配公平分配。作者要感谢麦克马斯特输血研究中心的Julie Carruthers、Erin Jamula和Melanie St John对我们的行政支持。披露声明作者未报告潜在的利益冲突。数据可用性声明由于该研究的伦理、法律和商业敏感性，本研究的参与者不同意公开分享他们的数据，因此无法获得支持数据。本研究得到了Mitacs研究培训奖(Award IT22358)、麦克马斯特输血研究中心和加拿大自然科学与工程研究委员会(NSERC)发现资助计划(RGPIN-2016-04518和RGPIN-2022-02999)的支持。
{"title":"Data-driven fair resource allocation for novel emerging epidemics: a COVID-19 Convalescent Plasma case study","authors":"Maryam Akbari-Moghaddam, Na Li, Douglas G. Down, Donald M. Arnold, Jeannie Callum, Philippe Bégin, Nancy M. Heddle","doi":"10.1080/03155986.2023.2264985","DOIUrl":"https://doi.org/10.1080/03155986.2023.2264985","url":null,"abstract":"AbstractEpidemics are a serious public health threat, and the resources for mitigating their effects are typically limited. Decision-makers face challenges in forecasting the supply and demand for these resources as prior information about the disease is often not available, the behaviour of the disease can periodically change (either naturally or as a result of public health policies) and can differ by geographical region. In this work, we discuss a model that is suitable for short-term real-time supply and demand forecasting during emerging outbreaks. We consider a case study of demand forecasting and allocating scarce quantities of COVID-19 Convalescent Plasma (CCP) in an international multi-site Randomized Controlled Trial (RCT) involving multiple hospital hubs across Canada (excluding Québec). We propose a data-driven mixed-integer programming (MIP) resource allocation model that assigns available resources to maximize a notion of fairness among the resource-demanding entities. Numerical results from applying our MIP model to the case study suggest that our approach can help balance the supply and demand of limited products such as CCP and minimize the unmet demand ratios of the demand entities. We analyse the sensitivity of our model to different allocation settings and show that our model assigns equitable allocations across the entities.Keywords: Resource allocationepidemicsCOVID-19 Convalescent Plasmadata-driven optimizationdemand forecasting AcknowledgmentsThe authors would like to thank Julie Carruthers, Erin Jamula, and Melanie St John at the McMaster Centre for Transfusion Research for their administrative support.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementDue to the ethically, legally, and commercially sensitive nature of the research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.Additional informationFundingThis work was supported by Mitacs Research Training Award (Award IT22358), the McMaster Centre for Transfusion Research, and the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant Program (RGPIN-2016-04518 and RGPIN-2022-02999).","PeriodicalId":13645,"journal":{"name":"Infor","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135302594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-08DOI: 10.1080/03155986.2023.2241324
Yueran Zhang, Zhanwen Niu, Yaqing Zuo, Chaochao Liu
Abstract In the context of collaborative manufacturing, cyber risk caused by cyber attacks may lead to severe supply chain disruption. Currently, supplier selection and order allocation is regarded as effective means to mitigate the risks that might cause disruption. Thus, we propose a two-stage hybrid model for supplier selection and order allocation under cyber risk. The hybrid model consists of fuzzy analytical hierarchy process (Fuzzy AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and two-stage mixed integer linear programming (MIP). Based on the extracted cyber risk indicators, a Fuzzy AHP is used to calculate the level of cyber risk of suppliers. TOPSIS is utilized to quantitatively evaluate the cyber risk of suppliers and determine the ranking of suppliers. Then, a two-stage MIP model is developed to support decision-making on order allocation. The first-stage decisions are determined without emergencies, and the second-stage decisions are determined under emergencies. The results reveal that application of the proposed two-stage hybrid model could mitigate the negative impacts of cyber risks. By providing a theoretical basis and quantitative method for cyber risk evaluation, this research is of theoretical and practical significance to the field of supply chain management.
{"title":"Two-stage hybrid model for supplier selection and order allocation considering cyber risk","authors":"Yueran Zhang, Zhanwen Niu, Yaqing Zuo, Chaochao Liu","doi":"10.1080/03155986.2023.2241324","DOIUrl":"https://doi.org/10.1080/03155986.2023.2241324","url":null,"abstract":"Abstract In the context of collaborative manufacturing, cyber risk caused by cyber attacks may lead to severe supply chain disruption. Currently, supplier selection and order allocation is regarded as effective means to mitigate the risks that might cause disruption. Thus, we propose a two-stage hybrid model for supplier selection and order allocation under cyber risk. The hybrid model consists of fuzzy analytical hierarchy process (Fuzzy AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and two-stage mixed integer linear programming (MIP). Based on the extracted cyber risk indicators, a Fuzzy AHP is used to calculate the level of cyber risk of suppliers. TOPSIS is utilized to quantitatively evaluate the cyber risk of suppliers and determine the ranking of suppliers. Then, a two-stage MIP model is developed to support decision-making on order allocation. The first-stage decisions are determined without emergencies, and the second-stage decisions are determined under emergencies. The results reveal that application of the proposed two-stage hybrid model could mitigate the negative impacts of cyber risks. By providing a theoretical basis and quantitative method for cyber risk evaluation, this research is of theoretical and practical significance to the field of supply chain management.","PeriodicalId":13645,"journal":{"name":"Infor","volume":"21 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87200730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-14DOI: 10.1080/03155986.2023.2245304
Y. H. Chun, E. Watson
Abstract Survey research, such as telephone, mail, or online questionnaires, is one of the most widely used tools for collecting sample data. We are often interested in the total number of replies that would be received during a given time period. Many researchers have developed a wide variety of curve-fitting methods to predict the response rate of recipients over time. However, previous models are based on some assumptions that are hardly justified in practice. In this paper, a new response model is proposed that is based on meaningful parameters such as the ultimate response rate of questionnaire recipients, delay rate of respondents, and average delivery time of responses. To estimate those model parameters, we use the Markov chain Monte Carlo (MCMC) method, which is increasingly popular in the operational research community. With mail survey data in marketing research, we test our Bayesian response model and compare its performance with those of traditional curve-fitting models.
{"title":"Markov chain Monte Carlo approach to the analysis of response patterns in data collection process","authors":"Y. H. Chun, E. Watson","doi":"10.1080/03155986.2023.2245304","DOIUrl":"https://doi.org/10.1080/03155986.2023.2245304","url":null,"abstract":"Abstract Survey research, such as telephone, mail, or online questionnaires, is one of the most widely used tools for collecting sample data. We are often interested in the total number of replies that would be received during a given time period. Many researchers have developed a wide variety of curve-fitting methods to predict the response rate of recipients over time. However, previous models are based on some assumptions that are hardly justified in practice. In this paper, a new response model is proposed that is based on meaningful parameters such as the ultimate response rate of questionnaire recipients, delay rate of respondents, and average delivery time of responses. To estimate those model parameters, we use the Markov chain Monte Carlo (MCMC) method, which is increasingly popular in the operational research community. With mail survey data in marketing research, we test our Bayesian response model and compare its performance with those of traditional curve-fitting models.","PeriodicalId":13645,"journal":{"name":"Infor","volume":"51 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78184832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-26DOI: 10.1080/03155986.2023.2229208
Warley Almeida Silva, S. D. Jena, Karan Jeswani
Abstract Network design problems are at the heart of several applications in domains such as transportation, telecommunication, energy and natural resources. This paper proposes a new multi-period network design problem variant, in which modular capacities can be added or reduced along the planning horizon in order to adapt to demand changes. The problem further allows to represent economies of scales in function of the total arc capacity, a detail that has typically been overlooked in the literature. This paper particularly emphasizes the different alternatives to formulate the problem. We propose two different mixed-integer programming formulations and analyze further modeling alternatives. We theoretically compare the strength of all formulations and evaluate their computational performance in extensive experiments. The results suggest that a recent modeling technique using more precise decision variables yields the strongest formulation, which also results in significantly faster solution times. The use of this formulation may therefore be beneficial when considering similar problem variants. Finally, we also evaluate the economical benefits of the features introduced in this new problem variant, indicating that both the selection of the capacity levels and the capacity adjustment along time are likely to result in significant cost savings.
{"title":"Mathematical formulations for multi-period network design with modular capacity adjustments","authors":"Warley Almeida Silva, S. D. Jena, Karan Jeswani","doi":"10.1080/03155986.2023.2229208","DOIUrl":"https://doi.org/10.1080/03155986.2023.2229208","url":null,"abstract":"Abstract Network design problems are at the heart of several applications in domains such as transportation, telecommunication, energy and natural resources. This paper proposes a new multi-period network design problem variant, in which modular capacities can be added or reduced along the planning horizon in order to adapt to demand changes. The problem further allows to represent economies of scales in function of the total arc capacity, a detail that has typically been overlooked in the literature. This paper particularly emphasizes the different alternatives to formulate the problem. We propose two different mixed-integer programming formulations and analyze further modeling alternatives. We theoretically compare the strength of all formulations and evaluate their computational performance in extensive experiments. The results suggest that a recent modeling technique using more precise decision variables yields the strongest formulation, which also results in significantly faster solution times. The use of this formulation may therefore be beneficial when considering similar problem variants. Finally, we also evaluate the economical benefits of the features introduced in this new problem variant, indicating that both the selection of the capacity levels and the capacity adjustment along time are likely to result in significant cost savings.","PeriodicalId":13645,"journal":{"name":"Infor","volume":"77 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81912384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A reinforcement learning based dynamic room pricing model for hotel industry","authors":"Gamze Tuncay, Kıymet Kaya, Yaren Yilmaz, Y. Yaslan, Şule Gündüz Öğüdücü","doi":"10.1080/03155986.2023.2235223","DOIUrl":"https://doi.org/10.1080/03155986.2023.2235223","url":null,"abstract":"","PeriodicalId":13645,"journal":{"name":"Infor","volume":"1 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85331389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-19DOI: 10.1080/03155986.2023.2229603
R. Jaballah, Rodrigo Ramalho, J. Renaud, Leandro C. Coelho
Abstract Traffic and congestion have a big impact on the performance of transportation systems. Travel time models are required to calculate trip durations and arrival times when traffic information is available. These models rely on the availability of detailed information about the traffic state. With the growing availability of onboard devices, we can now capture very precise data with a very high frequency. The challenge is now to efficiently use these data to solve routing problems and evaluate routing solutions. A key question that emerges is how to determine the best compromise between a huge amount of very precise data and a smaller volume of aggregated data. In this article, we analyze the impact of time aggregation on the performance of the main travel time models, namely the link travel model (LTM), the flow speed model (FSM) and the smoothed travel time model (STTM). We also analyze the impact of different time aggregation levels on these models. Our results show that all models share similar performance, particularly with large intervals. Finally, we show that the LTM largely respects the FIFO property, which is an important hypothesis for routing algorithms.
{"title":"The impact of time aggregation and travel time models on time-dependent routing solutions","authors":"R. Jaballah, Rodrigo Ramalho, J. Renaud, Leandro C. Coelho","doi":"10.1080/03155986.2023.2229603","DOIUrl":"https://doi.org/10.1080/03155986.2023.2229603","url":null,"abstract":"Abstract Traffic and congestion have a big impact on the performance of transportation systems. Travel time models are required to calculate trip durations and arrival times when traffic information is available. These models rely on the availability of detailed information about the traffic state. With the growing availability of onboard devices, we can now capture very precise data with a very high frequency. The challenge is now to efficiently use these data to solve routing problems and evaluate routing solutions. A key question that emerges is how to determine the best compromise between a huge amount of very precise data and a smaller volume of aggregated data. In this article, we analyze the impact of time aggregation on the performance of the main travel time models, namely the link travel model (LTM), the flow speed model (FSM) and the smoothed travel time model (STTM). We also analyze the impact of different time aggregation levels on these models. Our results show that all models share similar performance, particularly with large intervals. Finally, we show that the LTM largely respects the FIFO property, which is an important hypothesis for routing algorithms.","PeriodicalId":13645,"journal":{"name":"Infor","volume":"100 6 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87728618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-11DOI: 10.1080/03155986.2023.2228021
J. Craveirinha, M. Pascoal, J. Clímaco
Abstract The paper addresses a bicriteria optimisation problem in telecommunication networks that aims at finding Pareto efficient pairs of paths between two given nodes, seeking to minimise the number of SRLGs (Shared Risk Link Groups) common to both paths and the path pair cost. This problem is of particular importance in telecommunication routing design, namely concerning resilient routing models where both a primary and a backup paths have to be calculated to minimise the risk of failure of a connection between origin and terminal nodes, in case of failure in the primary path. An exact resolution method is applied for solving this problem, enabling the calculation of the whole set of Pareto optimal solutions, which combines a transformation of the network representation with a path ranking algorithm. A comprehensive experimental study on the application of this approach, using reference network topologies, considering random SRLG assignments to the links and random link bandwidth occupations, together with the discussion on typical examples of solution selection and potential advantages of the method, are presented.
{"title":"An exact approach for finding bicriteria maximally SRLG-disjoint/shortest path pairs in telecommunication networks","authors":"J. Craveirinha, M. Pascoal, J. Clímaco","doi":"10.1080/03155986.2023.2228021","DOIUrl":"https://doi.org/10.1080/03155986.2023.2228021","url":null,"abstract":"Abstract The paper addresses a bicriteria optimisation problem in telecommunication networks that aims at finding Pareto efficient pairs of paths between two given nodes, seeking to minimise the number of SRLGs (Shared Risk Link Groups) common to both paths and the path pair cost. This problem is of particular importance in telecommunication routing design, namely concerning resilient routing models where both a primary and a backup paths have to be calculated to minimise the risk of failure of a connection between origin and terminal nodes, in case of failure in the primary path. An exact resolution method is applied for solving this problem, enabling the calculation of the whole set of Pareto optimal solutions, which combines a transformation of the network representation with a path ranking algorithm. A comprehensive experimental study on the application of this approach, using reference network topologies, considering random SRLG assignments to the links and random link bandwidth occupations, together with the discussion on typical examples of solution selection and potential advantages of the method, are presented.","PeriodicalId":13645,"journal":{"name":"Infor","volume":"6 3 1","pages":"399 - 418"},"PeriodicalIF":1.3,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90349336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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