Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256137
Peng Lin, Xiang T. R. Kong, Ming Li, Jian Chen, G. Huang
Ecommerce has been an efficient way for manufacturing enterprises to receive customer orders. One typical characteristics of Ecommerce production orders is that they usually require several different types of products. Synchronous production of different products for one customer orders, referred to synchronization in this paper, plays a critical role in lowering inventory level and meeting customer delivery demand. To facilitate the synchronization, an advanced planning and scheduling (APS) system is developed by using the Physical Internet (PI) technology. Several innovations are significant. Firstly, execution-level activities are integrated with planning and scheduling decisions through PI to support real-time data collection for synchronization. Secondly, the production progresses of products and customer orders are monitored real-timely and fully considered in scheduling. Thirdly, scheduling is conducted by the joint efforts of schedulers and workshop supervisors to further guarantee the synchronization.
电子商务已经成为制造企业接收客户订单的有效方式。电子商务生产订单的一个典型特征是，它们通常需要几种不同类型的产品。针对一个客户的订单同步生产不同的产品，本文称之为同质化，对于降低库存水平，满足客户的交货期需求起着至关重要的作用。为了实现同步，利用物理互联网(Physical Internet, PI)技术开发了一种先进的APS (planning and scheduling)系统。有几项创新意义重大。首先，通过PI将执行级活动与计划和调度决策集成在一起，以支持实时数据收集以实现同步。其次，实时监控产品的生产进度和客户订单，并在调度中充分考虑。第三，调度由调度员和车间主管共同完成，进一步保证了同步。
{"title":"IoT-enabled manufacturing synchronization for ecommerce","authors":"Peng Lin, Xiang T. R. Kong, Ming Li, Jian Chen, G. Huang","doi":"10.1109/COASE.2017.8256137","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256137","url":null,"abstract":"Ecommerce has been an efficient way for manufacturing enterprises to receive customer orders. One typical characteristics of Ecommerce production orders is that they usually require several different types of products. Synchronous production of different products for one customer orders, referred to synchronization in this paper, plays a critical role in lowering inventory level and meeting customer delivery demand. To facilitate the synchronization, an advanced planning and scheduling (APS) system is developed by using the Physical Internet (PI) technology. Several innovations are significant. Firstly, execution-level activities are integrated with planning and scheduling decisions through PI to support real-time data collection for synchronization. Secondly, the production progresses of products and customer orders are monitored real-timely and fully considered in scheduling. Thirdly, scheduling is conducted by the joint efforts of schedulers and workshop supervisors to further guarantee the synchronization.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133939462","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256238
Fang-Yi Zhou, Cheng-Hung Wu, Cheng-Juei Yu
This study presents a dynamic dispatching method for re-entrant production systems by combing dynamic programming (DP) with deep learning. First, we use DP to derive optimal value functions and optimal dispatching policies in a small number of numerical cases. The optimal value functions are then applied to train a deep neural network (DNN). The DNN builds an efficient estimation engine for optimal value functions. Since optimal dispatching decisions can be considered a compressed feature of the optimal value function, the value function estimated by DNN can be quickly mapped to dynamic dispatching policies. The accuracy of DNN dispatching policies is validated by the k-fold cross-validation (k-cv) test in a wide variety of re-entrant systems. Our preliminary investigation shows the potential of DNN in instantaneously generating accurate dynamic dispatching policies.
{"title":"Dynamic dispatching for re-entrant production lines — A deep learning approach","authors":"Fang-Yi Zhou, Cheng-Hung Wu, Cheng-Juei Yu","doi":"10.1109/COASE.2017.8256238","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256238","url":null,"abstract":"This study presents a dynamic dispatching method for re-entrant production systems by combing dynamic programming (DP) with deep learning. First, we use DP to derive optimal value functions and optimal dispatching policies in a small number of numerical cases. The optimal value functions are then applied to train a deep neural network (DNN). The DNN builds an efficient estimation engine for optimal value functions. Since optimal dispatching decisions can be considered a compressed feature of the optimal value function, the value function estimated by DNN can be quickly mapped to dynamic dispatching policies. The accuracy of DNN dispatching policies is validated by the k-fold cross-validation (k-cv) test in a wide variety of re-entrant systems. Our preliminary investigation shows the potential of DNN in instantaneously generating accurate dynamic dispatching policies.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132683959","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256128
Ryotaro Yamazaki, T. Nishi, Soh Sakurai
We propose a decomposition method for simultaneous traffic signal control and route selection problem with first-order hybrid Petri Nets. The traffic signal control problem is formulated as an optimal firing sequence problem for first order hybrid Petri Nets where a passage of the vehicles is represented by the real number of vehicles and discrete states represent the traffic signal states. A simultaneous traffic signal control and route selection model is developed with the selection of the route for a specific vehicle with traffic flows with the same traffic signals. A discrete abstraction model is introduced to reduce the computational expense for the Lagrangian relaxation technique. Computational results show the superiority of the discrete abstraction model over existing methods.
{"title":"A decomposition method with discrete abstraction for simultaneous traffic signal control and route selection problem with first-order hybrid Petri Nets","authors":"Ryotaro Yamazaki, T. Nishi, Soh Sakurai","doi":"10.1109/COASE.2017.8256128","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256128","url":null,"abstract":"We propose a decomposition method for simultaneous traffic signal control and route selection problem with first-order hybrid Petri Nets. The traffic signal control problem is formulated as an optimal firing sequence problem for first order hybrid Petri Nets where a passage of the vehicles is represented by the real number of vehicles and discrete states represent the traffic signal states. A simultaneous traffic signal control and route selection model is developed with the selection of the route for a specific vehicle with traffic flows with the same traffic signals. A discrete abstraction model is introduced to reduce the computational expense for the Lagrangian relaxation technique. Computational results show the superiority of the discrete abstraction model over existing methods.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121056591","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256207
Zhiling Liu, Qing-Shan Jia, Hui Zhang
Evacuation plays an important role in the response process towards inevitable disasters and emergencies. Compared with the large number of evacuees, the number of evacuation guiders is much smaller. The evacuation guider location problem is of great practical importance, since the locations of guiders impact evacuation process and evacuation policy optimization. In general, it is difficult to identify the optimal locations of guiders due to the partial information and partial control, the complexity of evacuation process, and the large state and decision spaces. In this paper, we consider this important problem and make the following main contributions. First, we use the event-based optimization (EBO) theory to model the evacuation problem. Second, we develop an evacuation guider location optimization method based on road network centrality measures and use this method to optimize the evacuation process. Third, we evaluate the performance of our method through numerical results. We hope this work brings insight in evacuation guider location optimization problem.
{"title":"An evacuation guider location optimization method based on road network centrality measures","authors":"Zhiling Liu, Qing-Shan Jia, Hui Zhang","doi":"10.1109/COASE.2017.8256207","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256207","url":null,"abstract":"Evacuation plays an important role in the response process towards inevitable disasters and emergencies. Compared with the large number of evacuees, the number of evacuation guiders is much smaller. The evacuation guider location problem is of great practical importance, since the locations of guiders impact evacuation process and evacuation policy optimization. In general, it is difficult to identify the optimal locations of guiders due to the partial information and partial control, the complexity of evacuation process, and the large state and decision spaces. In this paper, we consider this important problem and make the following main contributions. First, we use the event-based optimization (EBO) theory to model the evacuation problem. Second, we develop an evacuation guider location optimization method based on road network centrality measures and use this method to optimize the evacuation process. Third, we evaluate the performance of our method through numerical results. We hope this work brings insight in evacuation guider location optimization problem.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116294288","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256329
X. Zhao, Y. Lu, Lizhu Han, Xia Wang, Qianchuan Zhao
This paper concentrates on attitude tracking control design for spacecrafts with actuator faults. By introducing a Nussbaum gain technique law to handle unknown healthy indicators, we propose a Lyapunov function-based feedback control design. It is proved that with the developed controller, the solution of the closed-loop systems is bounded, while the tracking issue can be realized. The efficiency of the developed control design is further shown by a numerical simulation.
{"title":"A nussbaum gain approach to attitude tracking control of spacecrafts with actuator faults","authors":"X. Zhao, Y. Lu, Lizhu Han, Xia Wang, Qianchuan Zhao","doi":"10.1109/COASE.2017.8256329","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256329","url":null,"abstract":"This paper concentrates on attitude tracking control design for spacecrafts with actuator faults. By introducing a Nussbaum gain technique law to handle unknown healthy indicators, we propose a Lyapunov function-based feedback control design. It is proved that with the developed controller, the solution of the closed-loop systems is bounded, while the tracking issue can be realized. The efficiency of the developed control design is further shown by a numerical simulation.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114594222","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256303
Bocheng Li, L. Xia, Qianchuan Zhao
Reinforcement learning (RL) is a widely adopted theory in machine learning, which aims to handle the optimal decision of intelligent agent interacting with the stochastic dynamic environment. Its origin may come from the motivation of phycological observations since 1960's [1]. It blooms recently as the emerging of large sample data and powerful computation facility, especially the AlphaGo's beat over the human top Go player in 2016 [2].
{"title":"Complexity analysis of reinforcement learning and its application to robotics","authors":"Bocheng Li, L. Xia, Qianchuan Zhao","doi":"10.1109/COASE.2017.8256303","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256303","url":null,"abstract":"Reinforcement learning (RL) is a widely adopted theory in machine learning, which aims to handle the optimal decision of intelligent agent interacting with the stochastic dynamic environment. Its origin may come from the motivation of phycological observations since 1960's [1]. It blooms recently as the emerging of large sample data and powerful computation facility, especially the AlphaGo's beat over the human top Go player in 2016 [2].","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123934440","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256268
Weiyong Yu, Zhenhua Deng, Hongbing Zhou, Yiguang Hong
In this paper, the problem of distributed resource allocation optimization is investigated for continuous-time multi-agent systems with discrete-time communication. A gradient-based continuous-time algorithm is proposed to solve this network resource allocation problem. A sufficient condition on the communication period is given to show that the proposed algorithm can achieve the exact optimization with exponential convergence rate. Finally, an example of economic dispatch in power grids is given to illustrate the effectiveness of the presented algorithm.
{"title":"Distributed resource allocation optimization with discrete-time communication and application to economic dispatch in power systems","authors":"Weiyong Yu, Zhenhua Deng, Hongbing Zhou, Yiguang Hong","doi":"10.1109/COASE.2017.8256268","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256268","url":null,"abstract":"In this paper, the problem of distributed resource allocation optimization is investigated for continuous-time multi-agent systems with discrete-time communication. A gradient-based continuous-time algorithm is proposed to solve this network resource allocation problem. A sufficient condition on the communication period is given to show that the proposed algorithm can achieve the exact optimization with exponential convergence rate. Finally, an example of economic dispatch in power grids is given to illustrate the effectiveness of the presented algorithm.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124004551","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256197
He Lyu, Xiangbao Song, Dan Dai, Jiangang Li, Zexiang Li
In this paper, tool path interpolation and redundancy optimization algorithms are designed for the industrial manipulator to perform tasks exhibiting 1-DoF redundancy such as the welding, cutting etc. B-spline is applied for the tool path interpolation and then by minimizing the energy consumption while avoiding singularity and respecting joint limits at the same time, the optimal trajectory can be obtained. The problem is formulated and solved by nonlinear optimization method. POE(Product of exponential) model is used for robotic kinematic and dynamic analysis to simplify the problem. Experiments were conducted to illustrate the feasibility of our method.
{"title":"Tool path interpolation and redundancy optimization of manipulator","authors":"He Lyu, Xiangbao Song, Dan Dai, Jiangang Li, Zexiang Li","doi":"10.1109/COASE.2017.8256197","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256197","url":null,"abstract":"In this paper, tool path interpolation and redundancy optimization algorithms are designed for the industrial manipulator to perform tasks exhibiting 1-DoF redundancy such as the welding, cutting etc. B-spline is applied for the tool path interpolation and then by minimizing the energy consumption while avoiding singularity and respecting joint limits at the same time, the optimal trajectory can be obtained. The problem is formulated and solved by nonlinear optimization method. POE(Product of exponential) model is used for robotic kinematic and dynamic analysis to simplify the problem. Experiments were conducted to illustrate the feasibility of our method.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125342282","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256246
Chun-Ming Chang, Shi-Chung Chang, Chun-Hung Chen
Ordinal transformation is a technique of ordinal optimization that utilizes a simplified model for performance evaluation and ranking to further reduce computational effort. This presentation-only paper will be focused on investigating how simplified models of different variability levels affect ranking. The simulation-based study investigates capacity allocation of a re-entrant line in the context of semiconductor manufacturing by using two queuing network approximation models, Jackson network approximation (JNA) and queuing network analyzer (QNA). Both are based on parametric decomposition method and JNA is a special case of QNA with a unity squared coefficient of variation because of the exponential assumptions. Mean cycle time (MCT) is the performance index. Simulation studies of a five-station re-entrant line demonstrate that QNA capture of heterogeneous variability greatly improves the MCT ranking correlation of top-10 allocations out of 415 designs by almost 8 times over JNA at the cost of less than 3% computation time increase, i.e., the value of keeping a good model of variability from simplification.
{"title":"How simplified models of different variability affects performance of ordinal transformation","authors":"Chun-Ming Chang, Shi-Chung Chang, Chun-Hung Chen","doi":"10.1109/COASE.2017.8256246","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256246","url":null,"abstract":"Ordinal transformation is a technique of ordinal optimization that utilizes a simplified model for performance evaluation and ranking to further reduce computational effort. This presentation-only paper will be focused on investigating how simplified models of different variability levels affect ranking. The simulation-based study investigates capacity allocation of a re-entrant line in the context of semiconductor manufacturing by using two queuing network approximation models, Jackson network approximation (JNA) and queuing network analyzer (QNA). Both are based on parametric decomposition method and JNA is a special case of QNA with a unity squared coefficient of variation because of the exponential assumptions. Mean cycle time (MCT) is the performance index. Simulation studies of a five-station re-entrant line demonstrate that QNA capture of heterogeneous variability greatly improves the MCT ranking correlation of top-10 allocations out of 415 designs by almost 8 times over JNA at the cost of less than 3% computation time increase, i.e., the value of keeping a good model of variability from simplification.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124290288","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256254
Kuo Chen, J. Yi, Tao Liu
Underactuated balance robots represent a broad class of mechanical systems, ranging from Furuta pendulum, autonomous motorcycles, and robotic bipedal walkers, etc. The control tasks of these systems include trajectory tracking and balancing requirements. We present a data-driven modeling and control framework of the underactuated balance robots. A machine-learning method is used to capture the dynamics and the balance equilibrium manifold that represents balancing task target. We combine the learning-based models with the structural properties of the external/internal convertible form of these underactuated systems. Applications of the proposed learning-based models and control design are applied to the Furuta pendulum by simulation and experiments.
{"title":"Learning-based modeling and control of underactuated balance robotic systems","authors":"Kuo Chen, J. Yi, Tao Liu","doi":"10.1109/COASE.2017.8256254","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256254","url":null,"abstract":"Underactuated balance robots represent a broad class of mechanical systems, ranging from Furuta pendulum, autonomous motorcycles, and robotic bipedal walkers, etc. The control tasks of these systems include trajectory tracking and balancing requirements. We present a data-driven modeling and control framework of the underactuated balance robots. A machine-learning method is used to capture the dynamics and the balance equilibrium manifold that represents balancing task target. We combine the learning-based models with the structural properties of the external/internal convertible form of these underactuated systems. Applications of the proposed learning-based models and control design are applied to the Furuta pendulum by simulation and experiments.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122360854","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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