Pub Date : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004055
Ling-feng Shi, Yaxuan Dong, Yifan Shi
Indoor Pedestrian Dead Reckoning (PDR) based on Inertial and Magnetic Measurement Unit (IMMU) can accurately provide the position of pedestrians, and gradually becomes popular research on indoor positioning. In this paper, a novel PDR algorithm based on low-cost IMMU is proposed, which implements PDR from four steps: step detection, gait detection, step size estimation and attitude solution. According to the pitch angle, it is judged whether a new step is generated, and gait detection algorithm based on the standard deviation of the acceleration modulus and the angular velocity threshold + the angular velocity standard deviation threshold is proposed, which is the basis of step length estimation and attitude solution. The performance of the algorithm is verified through indoor experiments. The results show that the average distance error in the indoor environment was 1.32% and the average end-to-end error was 1.21%. Therefore, this paper based on low-cost IMMU indoor PDR has great application value.
{"title":"Indoor PDR Method Based on Foot-Mounted Low-Cost IMMU","authors":"Ling-feng Shi, Yaxuan Dong, Yifan Shi","doi":"10.1109/ICNSC55942.2022.10004055","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004055","url":null,"abstract":"Indoor Pedestrian Dead Reckoning (PDR) based on Inertial and Magnetic Measurement Unit (IMMU) can accurately provide the position of pedestrians, and gradually becomes popular research on indoor positioning. In this paper, a novel PDR algorithm based on low-cost IMMU is proposed, which implements PDR from four steps: step detection, gait detection, step size estimation and attitude solution. According to the pitch angle, it is judged whether a new step is generated, and gait detection algorithm based on the standard deviation of the acceleration modulus and the angular velocity threshold + the angular velocity standard deviation threshold is proposed, which is the basis of step length estimation and attitude solution. The performance of the algorithm is verified through indoor experiments. The results show that the average distance error in the indoor environment was 1.32% and the average end-to-end error was 1.21%. Therefore, this paper based on low-cost IMMU indoor PDR has great application value.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124737542","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 : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004106
Chuyan Zhang, A. Ni, Ce Yu, Linjie Gao, Qinqin Chen
Lane changing has a great impact on traffic efficiency and safety. A reasonably designed lane-changing model for automated vehicles is of great significance for the improvement of traffic safety and efficiency. In most traditional lane changing models, the constrained optimization model needs to be established and solved in the whole process, while in reinforcement learning, only the current state is taken as the input and the action is directly output to the vehicle. Based on the deep deterministic gradient strategy in reinforcement learning algorithm, a new lane changing model of automatic driving vehicle is constructed, which can control the lateral and longitudinal movements of the vehicle at the same time. Safety, efficiency, clearance, headway and comfort characteristics are combined as reward functions for optimizing its performance. The safe modification model is proposed to prevent unsafe behavior at each time step. The proposed model quickly converges in training phase. Compared with the human drivers, it can make safe and efficient lane change in shorter headway and duration. In contrast to conventional dynamic lane-changing trajectory planning model, our model performs better at risk mitigation of collision.
{"title":"A lane changing model of automatic driving vehicle based on Reinforcement Learning","authors":"Chuyan Zhang, A. Ni, Ce Yu, Linjie Gao, Qinqin Chen","doi":"10.1109/ICNSC55942.2022.10004106","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004106","url":null,"abstract":"Lane changing has a great impact on traffic efficiency and safety. A reasonably designed lane-changing model for automated vehicles is of great significance for the improvement of traffic safety and efficiency. In most traditional lane changing models, the constrained optimization model needs to be established and solved in the whole process, while in reinforcement learning, only the current state is taken as the input and the action is directly output to the vehicle. Based on the deep deterministic gradient strategy in reinforcement learning algorithm, a new lane changing model of automatic driving vehicle is constructed, which can control the lateral and longitudinal movements of the vehicle at the same time. Safety, efficiency, clearance, headway and comfort characteristics are combined as reward functions for optimizing its performance. The safe modification model is proposed to prevent unsafe behavior at each time step. The proposed model quickly converges in training phase. Compared with the human drivers, it can make safe and efficient lane change in shorter headway and duration. In contrast to conventional dynamic lane-changing trajectory planning model, our model performs better at risk mitigation of collision.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125278062","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 : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004089
Shuai Zhang, Xinyi Zhang, Peng Wu
This work investigates a new dynamic bus lane reservation problem considering dynamic traffic demand. It is to optimally decide which road links in the bus transit network to set up reserved bus lanes and determine when they are implemented. The objective of the problem is to minimize the total cost of the transportation network, including construction costs and travel time costs for travelers. We formulate the problem as a new bi-level programming model. The upper level is to optimize where and when bus lanes are implemented, while the lower level contains the modal split, traffic assignment, and transit assignment. An improved genetic algorithm is proposed for solving the proposed model and converges to a satifactory solution. Computational results of a case study verify the effectiveness and efficiency of the proposed method.
{"title":"Optimal dynamic bus lane reservation via bi-level programming","authors":"Shuai Zhang, Xinyi Zhang, Peng Wu","doi":"10.1109/ICNSC55942.2022.10004089","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004089","url":null,"abstract":"This work investigates a new dynamic bus lane reservation problem considering dynamic traffic demand. It is to optimally decide which road links in the bus transit network to set up reserved bus lanes and determine when they are implemented. The objective of the problem is to minimize the total cost of the transportation network, including construction costs and travel time costs for travelers. We formulate the problem as a new bi-level programming model. The upper level is to optimize where and when bus lanes are implemented, while the lower level contains the modal split, traffic assignment, and transit assignment. An improved genetic algorithm is proposed for solving the proposed model and converges to a satifactory solution. Computational results of a case study verify the effectiveness and efficiency of the proposed method.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114491286","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 : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004109
Zihao Song, Lijuan Chen, Han Sun, Guozhao Kou
As a new topic of transfer learning, source data-free domain adaptation (SFDA) is currently receiving a lot of attention due to the increasing demands on the safety and privacy. Due to that the dependence on the labelled source data is cut off by mining auxiliary information to regulate the self-training, the self-supervised learning becomes a promising SFDA solution. Among these proposed self-supervisions, the pseudo-label is a widely used and fundamental means to provide the supervision signal of category. However, the existing methods do not have a special strategy to mitigate the noise in the pseudo-labels well. This paper propose a deep clustering with weighted self-labelling (DC-WSL) approach to address the SFDA problem. Specifically, we first develop a low-entropy k-means method to generate more robust and credible clustering centers. And then, the pseudo-labels are assigned to all target data based on the distance from these centers, along with adaptive confidence scores as the weighted parameters. After that, based on these pseudo-labels with credible evaluation, we perform a self-training on the target domain under the regulation of deep clustering. The experimental results on two domain adaptation benchmarks confirm the effectiveness of the proposed method.
{"title":"SS8: Source Data-free Domain Adaptation via Deep Clustering with Weighted Self-labelling","authors":"Zihao Song, Lijuan Chen, Han Sun, Guozhao Kou","doi":"10.1109/ICNSC55942.2022.10004109","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004109","url":null,"abstract":"As a new topic of transfer learning, source data-free domain adaptation (SFDA) is currently receiving a lot of attention due to the increasing demands on the safety and privacy. Due to that the dependence on the labelled source data is cut off by mining auxiliary information to regulate the self-training, the self-supervised learning becomes a promising SFDA solution. Among these proposed self-supervisions, the pseudo-label is a widely used and fundamental means to provide the supervision signal of category. However, the existing methods do not have a special strategy to mitigate the noise in the pseudo-labels well. This paper propose a deep clustering with weighted self-labelling (DC-WSL) approach to address the SFDA problem. Specifically, we first develop a low-entropy k-means method to generate more robust and credible clustering centers. And then, the pseudo-labels are assigned to all target data based on the distance from these centers, along with adaptive confidence scores as the weighted parameters. After that, based on these pseudo-labels with credible evaluation, we perform a self-training on the target domain under the regulation of deep clustering. The experimental results on two domain adaptation benchmarks confirm the effectiveness of the proposed method.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128301677","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 : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004135
Pengwen Xiong, Yifan Yin
Haptic perception is an important capability for robots to perform tasks in unknown environments, but existing haptic sensors focus on the fingertips of the robot, making the sensing area of the sensor limited, and only one haptic modality can be collected with one sensory layer. In this paper, we propose a multimodal tactile sensor, the FVSight, that is based on a distributed flexible tactile sensing array for daily objects. The sensor is fixed at the end of the robotic arm and touches the object under test by pressing, which can collect both tactile image and force-tactile array information of the object using one perceptual layer. Meanwhile, spatially, the visual contact area of the sensor when pressing the object is the same as the tactile contact area, and temporally, the two heterogeneous tactile modal information is collected simultaneously, which effectively solves the problem of weak pairing of visual-tactile information in conventional tactile sensors. We also use extensive experiments to demonstrate the great potential of the FVSight sensor in the direction of robotic object perception.
{"title":"FVSight: A Novel Multimodal Tactile Sensor for Robotic Object Perception","authors":"Pengwen Xiong, Yifan Yin","doi":"10.1109/ICNSC55942.2022.10004135","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004135","url":null,"abstract":"Haptic perception is an important capability for robots to perform tasks in unknown environments, but existing haptic sensors focus on the fingertips of the robot, making the sensing area of the sensor limited, and only one haptic modality can be collected with one sensory layer. In this paper, we propose a multimodal tactile sensor, the FVSight, that is based on a distributed flexible tactile sensing array for daily objects. The sensor is fixed at the end of the robotic arm and touches the object under test by pressing, which can collect both tactile image and force-tactile array information of the object using one perceptual layer. Meanwhile, spatially, the visual contact area of the sensor when pressing the object is the same as the tactile contact area, and temporally, the two heterogeneous tactile modal information is collected simultaneously, which effectively solves the problem of weak pairing of visual-tactile information in conventional tactile sensors. We also use extensive experiments to demonstrate the great potential of the FVSight sensor in the direction of robotic object perception.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129656498","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 : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004141
Shaoyong Jiang, Wen-Feng Li, Jinglong Zhou
Aiming at the problems of serious occlusion, deformation and rapid scale change in pedestrian tracking of mobile robot with vision, a pedestrian tracking algorithm with detection is proposed based on the effective convolution operators handcraft(ECO-HC), which solves the problems of target loss and inaccurate positioning caused by occlusion and background interference in the tracking process. Occlusion standard and model update threshold are set according to the peak value of confidence response. Furtherly, the position and scale of the target are corrected by using YOLO detection algorithm. The algorithm is verified on the pedestrian subset of OTB100 dataset. Experimental results show that the improved algorithm is optimal compared with other algorithms, and the overall accuracy and success rate are 93.50% and 91.80% respectively.
{"title":"Pedestrian Target Tracking Algorithm on Fusion Detection","authors":"Shaoyong Jiang, Wen-Feng Li, Jinglong Zhou","doi":"10.1109/ICNSC55942.2022.10004141","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004141","url":null,"abstract":"Aiming at the problems of serious occlusion, deformation and rapid scale change in pedestrian tracking of mobile robot with vision, a pedestrian tracking algorithm with detection is proposed based on the effective convolution operators handcraft(ECO-HC), which solves the problems of target loss and inaccurate positioning caused by occlusion and background interference in the tracking process. Occlusion standard and model update threshold are set according to the peak value of confidence response. Furtherly, the position and scale of the target are corrected by using YOLO detection algorithm. The algorithm is verified on the pedestrian subset of OTB100 dataset. Experimental results show that the improved algorithm is optimal compared with other algorithms, and the overall accuracy and success rate are 93.50% and 91.80% respectively.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129670705","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}
With the rapid development of the economy and technology, product upgrading is accelerated, resulting in many abandoned item. The disassembly of waste products can in-crease the utilization rate of resources, reduce environmental pollution, and promote the sustainable development of the economy. The combination of a U-shaped disassembly line and a single-row disassembly line offers unique advantages and application scenarios. Such a hybrid disassembly line balancing problem with the requirement of multi-skill workers is ad-dressed in this paper. A mathematical model is established to maximize the recovery profit according to the characteristics of the proposed problem. A hybrid neighborhood search algorithm is designed to solve the problem. Three types of neighborhood structures are designed to search for feasible solutions. The experimental results show that the proposed algorithm can obtain stable and high-quality solutions quickly, and the validity of the neighborhood structure and the correctness of the proposed model are verified.
{"title":"Hybrid Neighborhood Search Algorithm for Multi-product Hybrid Disassembly Line Balancing Problem Considering Multi-skill Workers","authors":"GuiPeng Xi, Peisheng Liu, Xiwang Guo, Jiacun Wang, Shujin Qin, Jian Zhao, Ying Tang","doi":"10.1109/ICNSC55942.2022.10004093","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004093","url":null,"abstract":"With the rapid development of the economy and technology, product upgrading is accelerated, resulting in many abandoned item. The disassembly of waste products can in-crease the utilization rate of resources, reduce environmental pollution, and promote the sustainable development of the economy. The combination of a U-shaped disassembly line and a single-row disassembly line offers unique advantages and application scenarios. Such a hybrid disassembly line balancing problem with the requirement of multi-skill workers is ad-dressed in this paper. A mathematical model is established to maximize the recovery profit according to the characteristics of the proposed problem. A hybrid neighborhood search algorithm is designed to solve the problem. Three types of neighborhood structures are designed to search for feasible solutions. The experimental results show that the proposed algorithm can obtain stable and high-quality solutions quickly, and the validity of the neighborhood structure and the correctness of the proposed model are verified.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130549957","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 : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004083
Xingping Dai, Xiaoyu Zhao, F. Cen, F. Zhu
Deep convolutional neural networks show excellent performance on computer vision tasks. However, these networks rely heavily on large-scale datasets to avoid overfitting. Un-fortunately, except for some datasets used for classical tasks, only small-scale datasets can be acquired in many applications. Data augmentation is a commonly used approach to extend the dataset scale and take advantage of the capabilities of large-scale datasets. Based on Mixup and random erasing, this paper proposes two different combinations of these two methods, namely RSM and RDM, to compensate their respective shortcomings. The RSM method mix up two original images before erasing randomly selected region, while the RDM method performs in opposite order. The two proposed methods are evaluated extensively for object detection and image classification on various datasets. The experimental results show that RSM and RDM achieve over 1% and 1.5% improvements for the detection of small-scale objects and the image classification, respectively, compared to Mixup and random erasing.
{"title":"Data Augmentation Using Mixup and Random Erasing","authors":"Xingping Dai, Xiaoyu Zhao, F. Cen, F. Zhu","doi":"10.1109/ICNSC55942.2022.10004083","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004083","url":null,"abstract":"Deep convolutional neural networks show excellent performance on computer vision tasks. However, these networks rely heavily on large-scale datasets to avoid overfitting. Un-fortunately, except for some datasets used for classical tasks, only small-scale datasets can be acquired in many applications. Data augmentation is a commonly used approach to extend the dataset scale and take advantage of the capabilities of large-scale datasets. Based on Mixup and random erasing, this paper proposes two different combinations of these two methods, namely RSM and RDM, to compensate their respective shortcomings. The RSM method mix up two original images before erasing randomly selected region, while the RDM method performs in opposite order. The two proposed methods are evaluated extensively for object detection and image classification on various datasets. The experimental results show that RSM and RDM achieve over 1% and 1.5% improvements for the detection of small-scale objects and the image classification, respectively, compared to Mixup and random erasing.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129181814","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 : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004099
Sijia Yi, Jiliang Luo, Xuhang Li, Jun Li, Chunrong Pan
As for the optimal scheduling issue of closely coupled the path planning of automated guided vehicles (AGVs) and task allocation in flexible manufacturing systems (FMSs), a heuristic optimization method is proposed based on Petri nets and an artificial potential field (APF). First, a manufacturing system with AGVs is described as a task Petri net and a path one, and then its Petri net model is obtained by composing the two nets together. Second, the topology of the Petri net model is used to design the potential energy parameters for the network junctions, and, consequently, the APF is designed for the Petri net model. Third, a heuristic functions is proposed to estimate the minimal terminate times of the Petri net model by means of its APF. Further, a heuristic beam search algorithm is presented to calculate a near optimal schedule by the Petri net model and its APF. Finally, numerical experiments are carried out, and the results show that optimal or near-optimal schedules can be calculated in the reasonable times by our algorithm.
{"title":"Heuristic Scheduling Method of Flexible Manufacturing Based on Petri Nets and Artificial Potential Field","authors":"Sijia Yi, Jiliang Luo, Xuhang Li, Jun Li, Chunrong Pan","doi":"10.1109/ICNSC55942.2022.10004099","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004099","url":null,"abstract":"As for the optimal scheduling issue of closely coupled the path planning of automated guided vehicles (AGVs) and task allocation in flexible manufacturing systems (FMSs), a heuristic optimization method is proposed based on Petri nets and an artificial potential field (APF). First, a manufacturing system with AGVs is described as a task Petri net and a path one, and then its Petri net model is obtained by composing the two nets together. Second, the topology of the Petri net model is used to design the potential energy parameters for the network junctions, and, consequently, the APF is designed for the Petri net model. Third, a heuristic functions is proposed to estimate the minimal terminate times of the Petri net model by means of its APF. Further, a heuristic beam search algorithm is presented to calculate a near optimal schedule by the Petri net model and its APF. Finally, numerical experiments are carried out, and the results show that optimal or near-optimal schedules can be calculated in the reasonable times by our algorithm.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"283 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123726988","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 : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004129
Junheng Cheng, Jingya Cheng, Feng Chu
Time-of-Use (ToU) electricity pricing scheme has been widely implemented to alleviate the grid's peak load, under which manufacturing companies obtain a good opportunity to save energy cost through more reasonable production scheduling. As a typical production system, batch processing machine manufacturing system has been widely used in modern manufacturing industry because of its advantages in improving production efficiency and reducing production costs. In this work, a new bi-objective uniform parallel batch machine scheduling problem with different job sizes under ToU tariffs is explored, with the goal of minimizing the total electricity cost and the number of enabled machines. We first establish a mixed integer linear programming model, and then propose an improved model. Both models are solved by CPLEX using the $varepsilon$-constraint method. The calculation results of randomly generated instances prove the effectiveness of the proposed model. At the same time, the calculation results show that the improved model is more effective than the original one.
{"title":"Bi-Objective Optimization for Uniform Parallel Batch Machine Scheduling under Time-of-Use Tariffs","authors":"Junheng Cheng, Jingya Cheng, Feng Chu","doi":"10.1109/ICNSC55942.2022.10004129","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004129","url":null,"abstract":"Time-of-Use (ToU) electricity pricing scheme has been widely implemented to alleviate the grid's peak load, under which manufacturing companies obtain a good opportunity to save energy cost through more reasonable production scheduling. As a typical production system, batch processing machine manufacturing system has been widely used in modern manufacturing industry because of its advantages in improving production efficiency and reducing production costs. In this work, a new bi-objective uniform parallel batch machine scheduling problem with different job sizes under ToU tariffs is explored, with the goal of minimizing the total electricity cost and the number of enabled machines. We first establish a mixed integer linear programming model, and then propose an improved model. Both models are solved by CPLEX using the $varepsilon$-constraint method. The calculation results of randomly generated instances prove the effectiveness of the proposed model. At the same time, the calculation results show that the improved model is more effective than the original one.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124321242","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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