Pub Date : 2023-05-24DOI: 10.1109/cscwd57460.2023.10152747
{"title":"Keynote 4 - The Good, The Bad, and The Ethical: Exploring Artificial Intelligence in Automatic Decision-Making","authors":"","doi":"10.1109/cscwd57460.2023.10152747","DOIUrl":"https://doi.org/10.1109/cscwd57460.2023.10152747","url":null,"abstract":"","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"37 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74658653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152565
Ramon Chaves, C. Motta, António Correia, Jano de Souza, D. Schneider
This paper explores the challenges and strategies used to design, build and evaluate a collective intelligence (CI) model to support discussions about cities. Through an autoethnography of ideas and discussion practices in both online and offline contexts, this work explores the tensions between the chosen methodological approaches, including design science research (DSR) and participatory action research (PAR). Moreover, we also examine the challenges and pitfalls observed during the practical conduction of this research when involving participants in the process of empirically evaluating the proposed model. Finally, aspects related to the autoethnographic process itself as a reflective method are discussed alongside the consequences of desiring and seeking participation in the research process.
{"title":"Tensions in design and participation processes: An ethnographic approach to the design, building and evaluation of a collective intelligence model","authors":"Ramon Chaves, C. Motta, António Correia, Jano de Souza, D. Schneider","doi":"10.1109/CSCWD57460.2023.10152565","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152565","url":null,"abstract":"This paper explores the challenges and strategies used to design, build and evaluate a collective intelligence (CI) model to support discussions about cities. Through an autoethnography of ideas and discussion practices in both online and offline contexts, this work explores the tensions between the chosen methodological approaches, including design science research (DSR) and participatory action research (PAR). Moreover, we also examine the challenges and pitfalls observed during the practical conduction of this research when involving participants in the process of empirically evaluating the proposed model. Finally, aspects related to the autoethnographic process itself as a reflective method are discussed alongside the consequences of desiring and seeking participation in the research process.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"304 1","pages":"462-467"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76557585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152654
Junnan Yin, Yuyan Sun, Lei Cui, Zhengyang Ai, Hongsong Zhu
Federated Learning (FL) is a promising machine learning paradigm for collaborative training on cross-soils in a privacy-protected manner. However, the existence of non-IID data causes problems such as performance degradation and thus becomes one of the key challenges in FL recently. To address this problem, we propose a clustered personalized federated learning method named as SynCPFL. SynCPFL groups clients sharing with the similar data distribution together, thereby facilitating collaboration and producing a better-personalized model for each client. In contrast to existing clustered federated learning methods, SynCPFL does not require multiple rounds of interaction between clients and server, so that the communication overhead is reduced a lot, thereby saving resources of clients. We evaluate SynCPFL on benchmark datasets, the experimental results demonstrate that SynCPFL outperforms existing methods.
{"title":"SynCPFL:Synthetic Distribution Aware Clustered Framework for Personalized Federated Learning","authors":"Junnan Yin, Yuyan Sun, Lei Cui, Zhengyang Ai, Hongsong Zhu","doi":"10.1109/CSCWD57460.2023.10152654","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152654","url":null,"abstract":"Federated Learning (FL) is a promising machine learning paradigm for collaborative training on cross-soils in a privacy-protected manner. However, the existence of non-IID data causes problems such as performance degradation and thus becomes one of the key challenges in FL recently. To address this problem, we propose a clustered personalized federated learning method named as SynCPFL. SynCPFL groups clients sharing with the similar data distribution together, thereby facilitating collaboration and producing a better-personalized model for each client. In contrast to existing clustered federated learning methods, SynCPFL does not require multiple rounds of interaction between clients and server, so that the communication overhead is reduced a lot, thereby saving resources of clients. We evaluate SynCPFL on benchmark datasets, the experimental results demonstrate that SynCPFL outperforms existing methods.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"13 5","pages":"438-443"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72545868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152852
Ning Zhang, Tianyue Qiu, Liuliu Du-Ikonen, Xiaojie Lin, Qichao Ye, Jiaying Chen
A smart energy platform for the large-space stadium based on Internet of Things (IoT) is proposed. The platform could realize the safe and stable operation of the energy system in various scenarios and promote low-carbon, efficient and sustainable development. In this paper, the smart energy platform is constructed based on IoT device data collection, time series data storage, front-end smart energy platform, and back-end optimization modules. The architecture and framework of the platform are explained in details. This study takes a large-space building in Hangzhou as an example to explain how the smart energy platform works in the real site. The real-time monitoring map of carbon emissions module and load prediction module of air-conditioning system are presented. The developed smart energy platform based on IoT could support the digital twin-based operation management of various types of low-carbon buildings in the future.
{"title":"Smart Energy Platform for Large-space Stadium Construction Based on Internet of Things","authors":"Ning Zhang, Tianyue Qiu, Liuliu Du-Ikonen, Xiaojie Lin, Qichao Ye, Jiaying Chen","doi":"10.1109/CSCWD57460.2023.10152852","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152852","url":null,"abstract":"A smart energy platform for the large-space stadium based on Internet of Things (IoT) is proposed. The platform could realize the safe and stable operation of the energy system in various scenarios and promote low-carbon, efficient and sustainable development. In this paper, the smart energy platform is constructed based on IoT device data collection, time series data storage, front-end smart energy platform, and back-end optimization modules. The architecture and framework of the platform are explained in details. This study takes a large-space building in Hangzhou as an example to explain how the smart energy platform works in the real site. The real-time monitoring map of carbon emissions module and load prediction module of air-conditioning system are presented. The developed smart energy platform based on IoT could support the digital twin-based operation management of various types of low-carbon buildings in the future.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"119 1","pages":"762-765"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76689294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152773
JingQi Yang, Cheng Xie, Peng Tang
Zero-shot learning (ZSL) is an important research area in computer-supported cooperative work in design, especially in the field of visual collaborative computing. ZSL normally uses transferable semantic features to represent the visual features to predict unseen classes without training the unseen samples. Existing ZSL models have attempted to learn region features in a single image, while the discriminative attribute localization of visual features is typically neglected. To handle the mentioned problem, we propose a pre-trained Masked Autoencoders(MAE) based Zero-Shot Learning model. It uses multi-head self-attention in Transformer blocks to capture the most discriminative local features from a partial perspective by considering both positional and contextual information of the entire sequence of patches, which is consistent with the human attention mechanism when recognizing objects. Further, it uses a Multilayer Perceptron(MLP) to map visual features to the semantic space for relating visual and semantic attributes, and predicts the semantic information, which is used to find out the class label during inference. Both quantitative and qualitative experimental results on three popular ZSL benchmarks show the proposed method achieves the new state-of-the-art in the field of generalized zero-shot learning and conventional zero-shot learning. The source code of the proposed method is available at https://github.com/yangjingqi99/MAE-ZSL
{"title":"Discriminative Feature Focus via Masked Autoencoder for Zero-Shot Learning","authors":"JingQi Yang, Cheng Xie, Peng Tang","doi":"10.1109/CSCWD57460.2023.10152773","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152773","url":null,"abstract":"Zero-shot learning (ZSL) is an important research area in computer-supported cooperative work in design, especially in the field of visual collaborative computing. ZSL normally uses transferable semantic features to represent the visual features to predict unseen classes without training the unseen samples. Existing ZSL models have attempted to learn region features in a single image, while the discriminative attribute localization of visual features is typically neglected. To handle the mentioned problem, we propose a pre-trained Masked Autoencoders(MAE) based Zero-Shot Learning model. It uses multi-head self-attention in Transformer blocks to capture the most discriminative local features from a partial perspective by considering both positional and contextual information of the entire sequence of patches, which is consistent with the human attention mechanism when recognizing objects. Further, it uses a Multilayer Perceptron(MLP) to map visual features to the semantic space for relating visual and semantic attributes, and predicts the semantic information, which is used to find out the class label during inference. Both quantitative and qualitative experimental results on three popular ZSL benchmarks show the proposed method achieves the new state-of-the-art in the field of generalized zero-shot learning and conventional zero-shot learning. The source code of the proposed method is available at https://github.com/yangjingqi99/MAE-ZSL","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"10 1","pages":"417-422"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81352734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152673
Chen Yang, Can Wang, Weidong Zhang, Huiyi Zhang, Xuangou Wu
With the development of blockchain technology, de-centralized applications (DApps) are increasingly being developed and deployed on blockchain platforms. However, the complex data validation mechanism and strict encryption protocol settings of blockchain often lead to sparse traffic behavior of DApps. This sparsity poses a challenge for existing encrypted traffic identification methods to extract distinguishable DApps traffic features. In this study, we propose a novel approach for identifying DApps traffic features by observing the differences in burst timing features of DApps. We introduce a continuous burst feature matrix (CBFM) method based on burst feature aggregation that can aggregate sparse features and express the burst timing differences of DApps encrypted traffic. Additionally, we design a deep learning classifier to automatically extract the features contained in the CBFM. Our experimental results on real datasets demonstrate that the proposed CBFM method achieves a classification accuracy of 94%, outperforming state-of-the-art methods.
{"title":"Decentralized Application Identification via Burst Feature Aggregation","authors":"Chen Yang, Can Wang, Weidong Zhang, Huiyi Zhang, Xuangou Wu","doi":"10.1109/CSCWD57460.2023.10152673","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152673","url":null,"abstract":"With the development of blockchain technology, de-centralized applications (DApps) are increasingly being developed and deployed on blockchain platforms. However, the complex data validation mechanism and strict encryption protocol settings of blockchain often lead to sparse traffic behavior of DApps. This sparsity poses a challenge for existing encrypted traffic identification methods to extract distinguishable DApps traffic features. In this study, we propose a novel approach for identifying DApps traffic features by observing the differences in burst timing features of DApps. We introduce a continuous burst feature matrix (CBFM) method based on burst feature aggregation that can aggregate sparse features and express the burst timing differences of DApps encrypted traffic. Additionally, we design a deep learning classifier to automatically extract the features contained in the CBFM. Our experimental results on real datasets demonstrate that the proposed CBFM method achieves a classification accuracy of 94%, outperforming state-of-the-art methods.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"21 1","pages":"1551-1556"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81539522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid development of pornographic and gambling websites, fueled by the widespread abuse of information technology, has become a growing concern. They pose a serious threat to the physical and mental health of children and can also endanger personal property. Therefore, it is necessary to detect them. However, pornographic and gambling websites become more and more tricky, which shows fake-normal to evade censorship and challenges traditional content-based detection methods. Therefore, it is essential to rely on information about relationships between websites.We propose HMAN, one Multi-Attention Heterogeneous Graph Neural Network (HGNN) model to detect pornographic and gambling websites by integrating content features and structural information, even if they present fake-normal. By one multi-attention mechanism consisting of explicit weight, self-attention and attention mechanism, content features can be selectively utilized with the assistance of structural information. The experimental results show that our method achieves the best 95.1% Macro-Avg-F1 and outperforms all baselines. We also illustrate that all extracted metapaths do contribute to the detection, where the hyperlink, title/meta terms and IP address are relatively important.
{"title":"Detecting Fake-Normal Pornographic and Gambling Websites through one Multi-Attention HGNN","authors":"Xiaoqing Ma, Chao Zheng, Zhao Li, Jiang Yin, Qingyun Liu, Xunxun Chen","doi":"10.1109/CSCWD57460.2023.10152775","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152775","url":null,"abstract":"The rapid development of pornographic and gambling websites, fueled by the widespread abuse of information technology, has become a growing concern. They pose a serious threat to the physical and mental health of children and can also endanger personal property. Therefore, it is necessary to detect them. However, pornographic and gambling websites become more and more tricky, which shows fake-normal to evade censorship and challenges traditional content-based detection methods. Therefore, it is essential to rely on information about relationships between websites.We propose HMAN, one Multi-Attention Heterogeneous Graph Neural Network (HGNN) model to detect pornographic and gambling websites by integrating content features and structural information, even if they present fake-normal. By one multi-attention mechanism consisting of explicit weight, self-attention and attention mechanism, content features can be selectively utilized with the assistance of structural information. The experimental results show that our method achieves the best 95.1% Macro-Avg-F1 and outperforms all baselines. We also illustrate that all extracted metapaths do contribute to the detection, where the hyperlink, title/meta terms and IP address are relatively important.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"1 1","pages":"1741-1747"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83085291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-05-24DOI: 10.1109/cscwd57460.2023.10152811
Qun Ding, Xiaolong Xu, Wenming Gui
As the emerging technology of Unmanned Aerial Vehicles (UAVs) becomes mature, UAVs are widely used in environmental monitoring, communication and other fields. In view of this, this paper analyzed the task of synergistic multi-UAVs exploration of unknown environments, and proposed a path planning method for them based on reinforcement learning. Firstly, the path planning task of the UAVs was divided into two parts: the path travel strategy module and the information exploration strategy module. Models of the two modules were based on the Deep Deterministic Policy Gradient algorithm (DDPG), and an improved Artificial Potential Field (APF) force traction mechanism was introduced in the path travel strategy module. Its aim was to assist in guiding the generation of UAV flight path trajectories. Also it could enhance the learning capability of the model. The path travel strategy module would generate the complete flight path of the whole cluster in a distributed manner. A series of temporary target points provided by the information exploration strategy module helped. In maps with 21.5%, 25.3% and 29.6% of obstacles, multi-UAVs could achieve 84.2%, 76.7% and 69.9% of environmental exploration by the designed method. Compared with the APF method, the A star method and the Breath First Search (BFS) method, the proposed method is not only able to plan feasible paths in a more complex map model, but also the curvature of the planned paths is smoother, thus achieving the goal of reducing the energy cost of UAVs.
{"title":"Path Planning based on Reinforcement Learning with Improved APF model for Synergistic Multi-UAVs","authors":"Qun Ding, Xiaolong Xu, Wenming Gui","doi":"10.1109/cscwd57460.2023.10152811","DOIUrl":"https://doi.org/10.1109/cscwd57460.2023.10152811","url":null,"abstract":"As the emerging technology of Unmanned Aerial Vehicles (UAVs) becomes mature, UAVs are widely used in environmental monitoring, communication and other fields. In view of this, this paper analyzed the task of synergistic multi-UAVs exploration of unknown environments, and proposed a path planning method for them based on reinforcement learning. Firstly, the path planning task of the UAVs was divided into two parts: the path travel strategy module and the information exploration strategy module. Models of the two modules were based on the Deep Deterministic Policy Gradient algorithm (DDPG), and an improved Artificial Potential Field (APF) force traction mechanism was introduced in the path travel strategy module. Its aim was to assist in guiding the generation of UAV flight path trajectories. Also it could enhance the learning capability of the model. The path travel strategy module would generate the complete flight path of the whole cluster in a distributed manner. A series of temporary target points provided by the information exploration strategy module helped. In maps with 21.5%, 25.3% and 29.6% of obstacles, multi-UAVs could achieve 84.2%, 76.7% and 69.9% of environmental exploration by the designed method. Compared with the APF method, the A star method and the Breath First Search (BFS) method, the proposed method is not only able to plan feasible paths in a more complex map model, but also the curvature of the planned paths is smoother, thus achieving the goal of reducing the energy cost of UAVs.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"5 1","pages":"432-437"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86576428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a bi-level Multi-Start Variable Neighborhood Search-Genetic Algorithm (MSVNS-GA) for the heat pipe-constrained component layout optimization (HCLO) problems. The proposed algorithm has won the first place in the CEC’2022 Competition on the Heat Pipe-Constrained Component Layout Optimization. First, the HCLO problem is divided into two sub-problems, heat pipe assignment (HA) and component location (CL). In the HA problem, components are assigned to different heat pipes. The best assignment scheme is taken as the input of the CL problem. In the CL problem, the specific coordinates of components are determined to meet practical engineering constraints. In this way, the complexity of the problem is lowered, and a part of the infeasible solution is cropped. Second, to address the HA problem, a multi-start variable neighborhood search algorithm is proposed and five efficient bottleneck-aware neighborhood structures are designed. And the genetic algorithm is used for CL problem. Finally, 30 independent experiments are carried out on the calculation examples with sizes of 6×4, 15×6, 40×16, and 90×32. The best result obtained by MSVNS-GA is 0.0%, 1.0%, 0.8%, and 1.1% different from the estimated lower bounds.
{"title":"A Variable Neighborhood Search Algorithm for Heat Pipe-Constrained Component Layout Optimization","authors":"Shichen Tian, Zhi-Guo Deng, Jia-xu Fan, Chunjiang Zhang, Weiming Shen, Liang Gao","doi":"10.1109/CSCWD57460.2023.10152572","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152572","url":null,"abstract":"This paper proposes a bi-level Multi-Start Variable Neighborhood Search-Genetic Algorithm (MSVNS-GA) for the heat pipe-constrained component layout optimization (HCLO) problems. The proposed algorithm has won the first place in the CEC’2022 Competition on the Heat Pipe-Constrained Component Layout Optimization. First, the HCLO problem is divided into two sub-problems, heat pipe assignment (HA) and component location (CL). In the HA problem, components are assigned to different heat pipes. The best assignment scheme is taken as the input of the CL problem. In the CL problem, the specific coordinates of components are determined to meet practical engineering constraints. In this way, the complexity of the problem is lowered, and a part of the infeasible solution is cropped. Second, to address the HA problem, a multi-start variable neighborhood search algorithm is proposed and five efficient bottleneck-aware neighborhood structures are designed. And the genetic algorithm is used for CL problem. Finally, 30 independent experiments are carried out on the calculation examples with sizes of 6×4, 15×6, 40×16, and 90×32. The best result obtained by MSVNS-GA is 0.0%, 1.0%, 0.8%, and 1.1% different from the estimated lower bounds.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"13 1","pages":"1452-1457"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89521941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152589
Jia-xu Fan, Chunjiang Zhang, Weiming Shen
This paper studies a flexible job shop rescheduling problem with lot-streaming and machine reconfigurations (FJRP-LSMR) to minimize the sum of the instability and total weighted tardiness, where machine reconfigurations are performed by assembling selected auxiliary modules for processing different batches of products. In this case, a rescheduling process is triggered by dynamic events, and requires to determine the lot-sizing plan, machine assignment, and sublot sequencing simultaneously. To address the intractable problem with multiple decision-making processes, a matheuristic integrating the genetic algorithm (GA) and the mixed integer linear programming (MILP) technique is proposed, where an MILP model is developed for optimally solving the lot-sizing sub-problem, and is embedded to the GA as a local search function. The proposed matheuristic is tested on randomly-generated instances to investigate the performance of all the algorithmic components. Experimental results demonstrate that the GA representation is effective in the complicated dynamic scheduling problem, and the lot-sizing sub-problem can be well addressed by the proposed MILP-based local search.
{"title":"A Matheuristic-based Rescheduling Method for Flexible Job Shops with Lot-streaming and Machine Reconfigurations","authors":"Jia-xu Fan, Chunjiang Zhang, Weiming Shen","doi":"10.1109/CSCWD57460.2023.10152589","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152589","url":null,"abstract":"This paper studies a flexible job shop rescheduling problem with lot-streaming and machine reconfigurations (FJRP-LSMR) to minimize the sum of the instability and total weighted tardiness, where machine reconfigurations are performed by assembling selected auxiliary modules for processing different batches of products. In this case, a rescheduling process is triggered by dynamic events, and requires to determine the lot-sizing plan, machine assignment, and sublot sequencing simultaneously. To address the intractable problem with multiple decision-making processes, a matheuristic integrating the genetic algorithm (GA) and the mixed integer linear programming (MILP) technique is proposed, where an MILP model is developed for optimally solving the lot-sizing sub-problem, and is embedded to the GA as a local search function. The proposed matheuristic is tested on randomly-generated instances to investigate the performance of all the algorithmic components. Experimental results demonstrate that the GA representation is effective in the complicated dynamic scheduling problem, and the lot-sizing sub-problem can be well addressed by the proposed MILP-based local search.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"1 1","pages":"1950-1955"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89643562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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