Pub Date : 2022-05-19DOI: 10.1142/s0219622022500213
Gianluca Bonifazi, Enrico Corradini, D. Ursino, L. Virgili
In the last two years, we have seen a huge number of debates and discussions on COVID-19 in social media. Many authors have analyzed these debates on Facebook and Twitter, while very few ones have considered Reddit. In this paper, we focus on this social network and propose three approaches to extract information from posts on COVID-19 published in it. The first performs a semi-automatic and dynamic classification of Reddit posts. The second automatically constructs virtual subreddits, each characterized by homogeneous themes. The third automatically identifies virtual communities of users with homogeneous themes. The three approaches represent an advance over the past literature. In fact, the latter lacks studies regarding classification algorithms capable of outlining the differences among the thousands of posts on COVID-19 in Reddit. Analogously, it lacks approaches able to build virtual subreddits with homogeneous topics or virtual communities of users with common interests.
{"title":"New Approaches to Extract Information From Posts on COVID-19 Published on Reddit","authors":"Gianluca Bonifazi, Enrico Corradini, D. Ursino, L. Virgili","doi":"10.1142/s0219622022500213","DOIUrl":"https://doi.org/10.1142/s0219622022500213","url":null,"abstract":"In the last two years, we have seen a huge number of debates and discussions on COVID-19 in social media. Many authors have analyzed these debates on Facebook and Twitter, while very few ones have considered Reddit. In this paper, we focus on this social network and propose three approaches to extract information from posts on COVID-19 published in it. The first performs a semi-automatic and dynamic classification of Reddit posts. The second automatically constructs virtual subreddits, each characterized by homogeneous themes. The third automatically identifies virtual communities of users with homogeneous themes. The three approaches represent an advance over the past literature. In fact, the latter lacks studies regarding classification algorithms capable of outlining the differences among the thousands of posts on COVID-19 in Reddit. Analogously, it lacks approaches able to build virtual subreddits with homogeneous topics or virtual communities of users with common interests.","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":"43 1","pages":"1385-1431"},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88489885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-13DOI: 10.1142/s0219622022500171
M. R. Bazargan-Lari, S. Taghipour
Manufacturing companies sometimes suffer from unexpected production disruptions/interruptions events (DIEs), affecting the production performance and cost. Since DIEs vary in type and cause, predicting the characteristics of their corresponding production downtimes is a challenging task. Although efforts have been devoted to forecast/prevent specific types of DIEs, such as machine-related events, it is still difficult to deal with the uncertainty caused by a combination of production DIEs of various types. Moreover, the absence of a realistic scenario generator incorporating DIEs has been a challenge in production scheduling under uncertainty. This study investigates the potential use of a hybrid data-driven approach in incorporating the uncertainties of a wide range of DIEs. In this approach, a random forest (RF) method and probability distributions are integrated to forecast the DIEs. The study was carried out based on the recorded DIEs in a Canadian company producing assembly parts for automotive industry. The performance of the proposed methodology for forecasting the production DIEs is evaluated by determining the predicted total downtime (TD) in percent of the expected processing time. The proposed hybrid model yields an overall accuracy of 92.82% in predicting the TD, compared to an overall accuracy of 75.64% when a single RF is used for prediction.
{"title":"A Hybrid Data-Driven Approach for Forecasting the Characteristics of Production Disruptions and Interruptions","authors":"M. R. Bazargan-Lari, S. Taghipour","doi":"10.1142/s0219622022500171","DOIUrl":"https://doi.org/10.1142/s0219622022500171","url":null,"abstract":"Manufacturing companies sometimes suffer from unexpected production disruptions/interruptions events (DIEs), affecting the production performance and cost. Since DIEs vary in type and cause, predicting the characteristics of their corresponding production downtimes is a challenging task. Although efforts have been devoted to forecast/prevent specific types of DIEs, such as machine-related events, it is still difficult to deal with the uncertainty caused by a combination of production DIEs of various types. Moreover, the absence of a realistic scenario generator incorporating DIEs has been a challenge in production scheduling under uncertainty. This study investigates the potential use of a hybrid data-driven approach in incorporating the uncertainties of a wide range of DIEs. In this approach, a random forest (RF) method and probability distributions are integrated to forecast the DIEs. The study was carried out based on the recorded DIEs in a Canadian company producing assembly parts for automotive industry. The performance of the proposed methodology for forecasting the production DIEs is evaluated by determining the predicted total downtime (TD) in percent of the expected processing time. The proposed hybrid model yields an overall accuracy of 92.82% in predicting the TD, compared to an overall accuracy of 75.64% when a single RF is used for prediction.","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":"52 1","pages":"1127-1154"},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75784209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-13DOI: 10.1142/s0219622022500225
Yu-Jie Wang
To encompass uncertainty and vagueness of information, the analytic hierarchy process (AHP) was often extended into fuzzy multi-criteria decision-making (FMCDM) under an uncertain environment. However, the extension of AHP was rarely constructed on interval-valued fuzzy numbers. Recently, interval-valued fuzzy numbers were utilized for decision-making to obtain more messages than others. For AHP extended under a fuzzy environment into fuzzy AHP, fuzzy computations are critical to derive priorities of pairwise comparison matrices. Although AHP’s approximate computations including the normalization of row arithmetic averages may be adopted to the fuzzy environment, the fuzzy extension of AHP is still complicated for division and multiplication of fuzzy numbers, especially interval-valued fuzzy numbers. To resolve complicated ties, a utility representation function of interval-valued fuzzy numbers in fuzzy AHP is used for yielding vectors consisting of priority representations of fuzzy pairwise comparison matrices on evaluation criteria based on objective, alternatives based on evaluation criteria, and more hierarchies. Then, sum product of multiplying the priority representation vectors is derived to form the utility representations of alternative performance indices, and alternative performance indices are represented by their corresponding utility representations. Therefore, FMCDM problems are easily solved by fuzzy AHP, i.e., combining AHP with the utility representation function under an interval-valued fuzzy environment.
{"title":"Interval-Valued Fuzzy Multi-Criteria Decision-Making by Combining Analytic Hierarchy Process with Utility Representation Function","authors":"Yu-Jie Wang","doi":"10.1142/s0219622022500225","DOIUrl":"https://doi.org/10.1142/s0219622022500225","url":null,"abstract":"To encompass uncertainty and vagueness of information, the analytic hierarchy process (AHP) was often extended into fuzzy multi-criteria decision-making (FMCDM) under an uncertain environment. However, the extension of AHP was rarely constructed on interval-valued fuzzy numbers. Recently, interval-valued fuzzy numbers were utilized for decision-making to obtain more messages than others. For AHP extended under a fuzzy environment into fuzzy AHP, fuzzy computations are critical to derive priorities of pairwise comparison matrices. Although AHP’s approximate computations including the normalization of row arithmetic averages may be adopted to the fuzzy environment, the fuzzy extension of AHP is still complicated for division and multiplication of fuzzy numbers, especially interval-valued fuzzy numbers. To resolve complicated ties, a utility representation function of interval-valued fuzzy numbers in fuzzy AHP is used for yielding vectors consisting of priority representations of fuzzy pairwise comparison matrices on evaluation criteria based on objective, alternatives based on evaluation criteria, and more hierarchies. Then, sum product of multiplying the priority representation vectors is derived to form the utility representations of alternative performance indices, and alternative performance indices are represented by their corresponding utility representations. Therefore, FMCDM problems are easily solved by fuzzy AHP, i.e., combining AHP with the utility representation function under an interval-valued fuzzy environment.","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":"56 1","pages":"1433-1465"},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77436823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-07DOI: 10.1142/s0219622022500237
Yin Liu, Min Xue
{"title":"A Group Consensus Reaching Method Considering Satisfaction of Decision Makers with Distributed Preference Relations","authors":"Yin Liu, Min Xue","doi":"10.1142/s0219622022500237","DOIUrl":"https://doi.org/10.1142/s0219622022500237","url":null,"abstract":"","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":"14 1","pages":"1487-1553"},"PeriodicalIF":0.0,"publicationDate":"2022-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87647458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-07DOI: 10.1142/s0219622022500286
Xiong Xiong, Jinchi Liu, Zonghang Yang, Jiatong Han
{"title":"An Agent-Based Model for the Impact of Price Limit Changes on Market Quality","authors":"Xiong Xiong, Jinchi Liu, Zonghang Yang, Jiatong Han","doi":"10.1142/s0219622022500286","DOIUrl":"https://doi.org/10.1142/s0219622022500286","url":null,"abstract":"","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":"2 1","pages":"1777-1795"},"PeriodicalIF":0.0,"publicationDate":"2022-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88012772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-07DOI: 10.1142/s0219622022500274
Željko Stević, Selçuk Korucuk, Çağlar Karamaşa, Ezgi Demir, E. Zavadskas
During the pandemic period, smart logistics applications have rapidly changed the way organizations do business in order to provide competitive products and services while still remaining flexible. Smart logistics applications and demand forecasting, which have an important place in ensuring customer satisfaction and increasing competitive advantage, came to the fore even more in this period. However, smart logistics applications are often bogged down by several barriers, and then there is the need to choose the most ideal demand forecasting method despite these barriers. The main purpose of this study is to assess the barriers to the smart logistics applications in companies that receive and provide logistics services with corporate identity in Ordu Province, and to choose the most ideal demand forecasting method during the COVID-19 period. This study has the characteristic of a roadmap that helps the construction of smart logistics transformation applications by detecting barriers related to smart logistics applications and determining the most ideal demand forecasting alternative in logistics sector. Fuzzy FUCOM (FUll COnsistency Method)-based interval rough EDAS (Evaluation based on Distance from Average Solution) methodology was used to weight the barriers and to rank and choose the most ideal demand forecasting method during COVID-19 period, respectively.
在疫情期间，智能物流应用迅速改变了组织开展业务的方式，以便在提供有竞争力的产品和服务的同时保持灵活性。智能物流应用和需求预测在确保客户满意度和增加竞争优势方面发挥着重要作用，在这一时期更加突出。然而，智能物流的应用往往会受到一些障碍的阻碍，然后需要在这些障碍中选择最理想的需求预测方法。本研究的主要目的是评估在Ordu省接收和提供具有企业身份的物流服务的公司中智能物流应用的障碍，并选择最理想的COVID-19期间需求预测方法。本研究具有路线图的特点，通过检测与智能物流应用相关的障碍，确定物流领域最理想的需求预测替代方案，帮助构建智能物流转型应用。采用基于Fuzzy FUCOM (fully COnsistency Method)的区间粗糙EDAS (Evaluation based on Distance from Average Solution)方法对障碍进行加权，并对新冠肺炎期间最理想的需求预测方法进行排序和选择。
{"title":"A Novel Integrated Fuzzy-Rough MCDM Model for Assessment of Barriers Related to Smart Logistics Applications and Demand Forecasting Method in the COVID-19 Period","authors":"Željko Stević, Selçuk Korucuk, Çağlar Karamaşa, Ezgi Demir, E. Zavadskas","doi":"10.1142/s0219622022500274","DOIUrl":"https://doi.org/10.1142/s0219622022500274","url":null,"abstract":"During the pandemic period, smart logistics applications have rapidly changed the way organizations do business in order to provide competitive products and services while still remaining flexible. Smart logistics applications and demand forecasting, which have an important place in ensuring customer satisfaction and increasing competitive advantage, came to the fore even more in this period. However, smart logistics applications are often bogged down by several barriers, and then there is the need to choose the most ideal demand forecasting method despite these barriers. The main purpose of this study is to assess the barriers to the smart logistics applications in companies that receive and provide logistics services with corporate identity in Ordu Province, and to choose the most ideal demand forecasting method during the COVID-19 period. This study has the characteristic of a roadmap that helps the construction of smart logistics transformation applications by detecting barriers related to smart logistics applications and determining the most ideal demand forecasting alternative in logistics sector. Fuzzy FUCOM (FUll COnsistency Method)-based interval rough EDAS (Evaluation based on Distance from Average Solution) methodology was used to weight the barriers and to rank and choose the most ideal demand forecasting method during COVID-19 period, respectively.","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":"5 1","pages":"1647-1678"},"PeriodicalIF":0.0,"publicationDate":"2022-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87846846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-07DOI: 10.1142/s0219622022500249
Khondker Mohammad Zobair, L. Sanzogni, L. Houghton, M. Islam
{"title":"Combining Deep Neural Network and PLS-SEM to Predict Patients' Continuity with Telemedicine","authors":"Khondker Mohammad Zobair, L. Sanzogni, L. Houghton, M. Islam","doi":"10.1142/s0219622022500249","DOIUrl":"https://doi.org/10.1142/s0219622022500249","url":null,"abstract":"","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":"19 1","pages":"1555-1589"},"PeriodicalIF":0.0,"publicationDate":"2022-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81050828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-07DOI: 10.1142/s0219622022500250
Sukran Seker
{"title":"A Novel Risk Assessment Approach Using a Hybrid Method Based On Fine-Kinney and Extended MCDM Methods Under Interval-Valued Intuitionistic Fuzzy Environment","authors":"Sukran Seker","doi":"10.1142/s0219622022500250","DOIUrl":"https://doi.org/10.1142/s0219622022500250","url":null,"abstract":"","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":"1 1","pages":"1591-1616"},"PeriodicalIF":0.0,"publicationDate":"2022-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86629073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-23DOI: 10.1142/s0219622022500158
E. Zanboori, S. Ghobadi
In the current world, dealing with some problems with interval data is inevitable. In this case, the methods applied for real data could not be employed. To solve these problems, the modified version of previous methods or new methods should be presented. In this paper, the two-stage ranking method that already has been proposed by the authors is modified to solve the mentioned problems. In each stage, two optimistic and pessimistic attitudes are considered and their corresponding models are presented. Then, an appropriate algorithm for classifying the units based on their obtained interval efficiency is proposed. To demonstrate the applicability of the proposed method, 30 branches of the social security insurance organization in Iran are classified. Also, the validity and consistency of the proposed method are confirmed. The main contributions of this paper are as follows: Decision-making units (DMUs) are ranked with interval inputs and outputs. Inefficiency of the first projection (obtained in the first stage) is applied in the unit rank score. All units are classified in separate classes and all units of each class are ranked. Pareto-efficient projections (practical benchmarks) are obtained for all inefficient units. The proposed model is always feasible and unit invariant.
{"title":"Ranking Decision-Making Units Using Interval Data Envelopment Analysis: Extension and Application","authors":"E. Zanboori, S. Ghobadi","doi":"10.1142/s0219622022500158","DOIUrl":"https://doi.org/10.1142/s0219622022500158","url":null,"abstract":"In the current world, dealing with some problems with interval data is inevitable. In this case, the methods applied for real data could not be employed. To solve these problems, the modified version of previous methods or new methods should be presented. In this paper, the two-stage ranking method that already has been proposed by the authors is modified to solve the mentioned problems. In each stage, two optimistic and pessimistic attitudes are considered and their corresponding models are presented. Then, an appropriate algorithm for classifying the units based on their obtained interval efficiency is proposed. To demonstrate the applicability of the proposed method, 30 branches of the social security insurance organization in Iran are classified. Also, the validity and consistency of the proposed method are confirmed. The main contributions of this paper are as follows: Decision-making units (DMUs) are ranked with interval inputs and outputs. Inefficiency of the first projection (obtained in the first stage) is applied in the unit rank score. All units are classified in separate classes and all units of each class are ranked. Pareto-efficient projections (practical benchmarks) are obtained for all inefficient units. The proposed model is always feasible and unit invariant.","PeriodicalId":13527,"journal":{"name":"Int. J. Inf. Technol. Decis. Mak.","volume":"142 1","pages":"1267-1296"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75993197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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