Existing solutions for collaborative trajectory planning using multiple UAVs suffer from issues such as low accuracy, instability, and slow convergence. To address the aforementioned issues, this paper introduces a new method for multiple unmanned aerial vehicle (UAV) 3D terrain cooperative trajectory planning based on the cuckoo search golden jackal optimization (CS-GJO) algorithm. A model for single UAV trajectory planning and a model for multi-UAV collaborative trajectory planning have been developed, and the problem of solving the models is restructured into an optimization problem. Building upon the original golden jackal optimization, the use of tent chaotic mapping aids in the generation of the golden jackal's initial population, thereby promoting population diversity. Subsequently, the position update strategy of the cuckoo search algorithm is combined for purpose of update the position information of individual golden jackals, effectively preventing the algorithm from getting stuck in local minima. Finally, the corresponding nonlinear control parameter were developed. The new parameters expedite the decrease in the convergence factor during the pre-exploration stage, resulting in an improved overall search speed of the algorithm. Moreover, they attenuate the decrease in the convergence factor during the post-exploration stage, thereby enhancing the algorithm's global search. The experimental results demonstrate that the CS-GJO algorithm efficiently and accurately accomplishes multi-UAV cooperative trajectory planning in a 3D environment. Compared with other comparative algorithms, the CS-GJO algorithm also has better stability, higher optimization accuracy, and faster convergence speed.
{"title":"Multi-UAV Collaborative Trajectory Planning for 3D Terrain Based on CS-GJO Algorithm","authors":"Taishan Lou;Yu Wang;Zhepeng Yue;Liangyu Zhao","doi":"10.23919/CSMS.2024.0013","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0013","url":null,"abstract":"Existing solutions for collaborative trajectory planning using multiple UAVs suffer from issues such as low accuracy, instability, and slow convergence. To address the aforementioned issues, this paper introduces a new method for multiple unmanned aerial vehicle (UAV) 3D terrain cooperative trajectory planning based on the cuckoo search golden jackal optimization (CS-GJO) algorithm. A model for single UAV trajectory planning and a model for multi-UAV collaborative trajectory planning have been developed, and the problem of solving the models is restructured into an optimization problem. Building upon the original golden jackal optimization, the use of tent chaotic mapping aids in the generation of the golden jackal's initial population, thereby promoting population diversity. Subsequently, the position update strategy of the cuckoo search algorithm is combined for purpose of update the position information of individual golden jackals, effectively preventing the algorithm from getting stuck in local minima. Finally, the corresponding nonlinear control parameter were developed. The new parameters expedite the decrease in the convergence factor during the pre-exploration stage, resulting in an improved overall search speed of the algorithm. Moreover, they attenuate the decrease in the convergence factor during the post-exploration stage, thereby enhancing the algorithm's global search. The experimental results demonstrate that the CS-GJO algorithm efficiently and accurately accomplishes multi-UAV cooperative trajectory planning in a 3D environment. Compared with other comparative algorithms, the CS-GJO algorithm also has better stability, higher optimization accuracy, and faster convergence speed.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 3","pages":"274-291"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737157","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaokang Wu;Yong Zhang;Xiaotian Fei;Hejuan Hu;Xiaoyan Sun;Dunwei Gong;Xianfang Song
Mine integrated energy system (MIES) can promote the utilization of derived energy and achieve multi-energy complementation and ecological protection. Now it gradually becomes an important focus for scientific carbon reduction and carbon neutrality. To reduce the impact of uncertain prediction differences on the system during the process of using mine derived energy, a low-carbon economic operation strategy of MIES considering energy supply uncertainty is developed in this paper. Firstly, based on the basic structure of energy flow in MIES, the energy-carbon flow framework of MIES is established for the low-carbon operation requirements. Secondly, considering carbon emission constraints, the low-carbon economic operation optimization model (LEOOM) is built for MIES to minimize operation cost and carbon emission. Finally, multiple uncertainties of the system are modeled and analyzed by using the robust model under the risk aversion strategy of information gap decision theory (IGDT), and a model conversion method is designed to optimize the low-carbon economic operation model. The simulation results under three scenarios demonstrate that compared to the existed economic dispatching models, the proposed model achieves a 30% reduction in carbon emission while the operational cost of MIES only is increased by 2.1%. The model efficiently mitigates the carbon emission of the system, and the proposed uncertain treatment strategy can significantly improve the robustness of obtained operation plans.
矿山综合能源系统（MIES）可以促进衍生能源的利用，实现多能互补和生态保护。目前，它逐渐成为科学减碳、碳中和的重要着力点。为减少矿井衍生能源利用过程中不确定预测差异对系统的影响，本文提出了考虑能源供应不确定性的矿井综合能源系统低碳经济运行策略。首先，基于 MIES 能源流的基本结构，针对低碳运行要求，建立 MIES 的能源碳流框架。其次，考虑碳排放约束条件，为 MIES 建立低碳经济运行优化模型（LEOOM），实现运行成本和碳排放最小化。最后，利用信息差距决策理论（IGDT）风险规避策略下的鲁棒模型对系统的多种不确定性进行建模和分析，并设计了模型转换方法来优化低碳经济运行模型。三种情景下的仿真结果表明，与现有的经济调度模型相比，所提出的模型实现了 30% 的碳减排，而 MIES 的运营成本仅增加了 2.1%。该模型有效地减少了系统的碳排放，所提出的不确定性处理策略可显著提高所获得的运行计划的鲁棒性。
{"title":"Low-Carbon Economic Operation Optimization of Mine Integrated Energy System Considering Energy Supply Uncertainty","authors":"Xiaokang Wu;Yong Zhang;Xiaotian Fei;Hejuan Hu;Xiaoyan Sun;Dunwei Gong;Xianfang Song","doi":"10.23919/CSMS.2024.0012","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0012","url":null,"abstract":"Mine integrated energy system (MIES) can promote the utilization of derived energy and achieve multi-energy complementation and ecological protection. Now it gradually becomes an important focus for scientific carbon reduction and carbon neutrality. To reduce the impact of uncertain prediction differences on the system during the process of using mine derived energy, a low-carbon economic operation strategy of MIES considering energy supply uncertainty is developed in this paper. Firstly, based on the basic structure of energy flow in MIES, the energy-carbon flow framework of MIES is established for the low-carbon operation requirements. Secondly, considering carbon emission constraints, the low-carbon economic operation optimization model (LEOOM) is built for MIES to minimize operation cost and carbon emission. Finally, multiple uncertainties of the system are modeled and analyzed by using the robust model under the risk aversion strategy of information gap decision theory (IGDT), and a model conversion method is designed to optimize the low-carbon economic operation model. The simulation results under three scenarios demonstrate that compared to the existed economic dispatching models, the proposed model achieves a 30% reduction in carbon emission while the operational cost of MIES only is increased by 2.1%. The model efficiently mitigates the carbon emission of the system, and the proposed uncertain treatment strategy can significantly improve the robustness of obtained operation plans.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 3","pages":"258-273"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hybrid flow shop scheduling problem (HFSP) has been extensively considered, however, some real-life conditions are seldom investigated. In this study, HFSP with no precedence between some stages is solved and an adaptive shuffled frog-leaping algorithm (ASFLA) is developed to optimize makespan. A new solution representation and a decoding procedure are presented, an adaptive memeplex search and dynamical population shuffling are implemented together. Many computational experiments are implemented. Computational results prove that the new strategies of ASFLA are effective and ASFLA is very competitive in solving HFSP with no precedence between some stages.
{"title":"An Adaptive Shuffled Frog-Leaping Algorithm for Hybrid-Flow Shop Scheduling with No Precedence Between Some Stages","authors":"Zhenghui Yin;Deming Lei;Bo Yang","doi":"10.23919/CSMS.2024.0014","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0014","url":null,"abstract":"Hybrid flow shop scheduling problem (HFSP) has been extensively considered, however, some real-life conditions are seldom investigated. In this study, HFSP with no precedence between some stages is solved and an adaptive shuffled frog-leaping algorithm (ASFLA) is developed to optimize makespan. A new solution representation and a decoding procedure are presented, an adaptive memeplex search and dynamical population shuffling are implemented together. Many computational experiments are implemented. Computational results prove that the new strategies of ASFLA are effective and ASFLA is very competitive in solving HFSP with no precedence between some stages.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 3","pages":"292-302"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As an emerging technology, digital twin is expected to bring novel application modes to the whole life cycle process of unmanned ground equipment, including research and development, design, control optimization, operation and maintenance, etc. The highly dynamic, complex, and uncertain characteristics of unmanned ground equipment and the battlefield environment also pose new challenges for digital twin technology. Starting from the new challenges faced by the digital twin of unmanned ground equipment, this paper designs a service-oriented cloud-edge-end collaborative platform architecture of the digital twin system of unmanned ground equipment, and further analyzes several key technologies supporting the implementation of the platform architecture.
{"title":"Research on Digital Twin System Platform Framework and Key Technologies of Unmanned Ground Equipment","authors":"Kunyu Wang;Lin Zhang;Cheng Xu;Han Lu;Zhen Chen;Hongbo Cheng;Rui Guo","doi":"10.23919/CSMS.2024.0009","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0009","url":null,"abstract":"As an emerging technology, digital twin is expected to bring novel application modes to the whole life cycle process of unmanned ground equipment, including research and development, design, control optimization, operation and maintenance, etc. The highly dynamic, complex, and uncertain characteristics of unmanned ground equipment and the battlefield environment also pose new challenges for digital twin technology. Starting from the new challenges faced by the digital twin of unmanned ground equipment, this paper designs a service-oriented cloud-edge-end collaborative platform architecture of the digital twin system of unmanned ground equipment, and further analyzes several key technologies supporting the implementation of the platform architecture.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 2","pages":"109-123"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10598211","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The practical engineering of satellite tracking telemetry and command (TT&C) is often disturbed by unpredictable external factors, including the temporary rise in a significant quantity of satellite TT&C tasks, temporary failures and failures of some TT&C resources, and so on. To improve the adaptability and robustness of satellite TT&C systems when faced with uncertain dynamic disturbances, a hierarchical disturbance propagation mechanism and an improved contract network dynamic scheduling method for satellite TT&C resources were designed to address the dynamic scheduling problem of satellite TT&C resources. Firstly, the characteristics of the dynamic scheduling problem of satellite TT&C resources are analyzed, and a mathematical model is established with the weighted optimization objectives of maximizing the revenue from task completion and minimizing the degree of plan disturbance. Then, a bottom-up distributed dynamic collaborative scheduling framework for satellite TT&C resources is proposed, which includes a task layer, a resource layer, a central internal collaboration layer, and a central external collaboration layer. Dynamic disturbances are propagated layer by layer from the task layer to the central external collaboration layer in a bottom-up manner, using efficient heuristic strategies in the task layer and the resource layer, respectively. We use improved contract network algorithms in the center internal collaboration layer and the center external collaboration layer, the original scheduling plan is quickly adjusted to minimize the impact of disturbances while effectively completing dynamic task requirements. Finally, a large number of simulation experiments were carried out and compared with various comparative algorithms. The results show that the proposed algorithm can effectively improve the solution effect of satellite TT&C resource dynamic scheduling problems, and has good application prospects.
{"title":"Hierarchical Disturbance Propagation Mechanism and Improved Contract Net Protocol for Satellite TT&C Resource Dynamic Scheduling","authors":"Zhiqing Xiang;Yi Gu;Xinwei Wang;Guohua Wu","doi":"10.23919/CSMS.2024.0004","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0004","url":null,"abstract":"The practical engineering of satellite tracking telemetry and command (TT&C) is often disturbed by unpredictable external factors, including the temporary rise in a significant quantity of satellite TT&C tasks, temporary failures and failures of some TT&C resources, and so on. To improve the adaptability and robustness of satellite TT&C systems when faced with uncertain dynamic disturbances, a hierarchical disturbance propagation mechanism and an improved contract network dynamic scheduling method for satellite TT&C resources were designed to address the dynamic scheduling problem of satellite TT&C resources. Firstly, the characteristics of the dynamic scheduling problem of satellite TT&C resources are analyzed, and a mathematical model is established with the weighted optimization objectives of maximizing the revenue from task completion and minimizing the degree of plan disturbance. Then, a bottom-up distributed dynamic collaborative scheduling framework for satellite TT&C resources is proposed, which includes a task layer, a resource layer, a central internal collaboration layer, and a central external collaboration layer. Dynamic disturbances are propagated layer by layer from the task layer to the central external collaboration layer in a bottom-up manner, using efficient heuristic strategies in the task layer and the resource layer, respectively. We use improved contract network algorithms in the center internal collaboration layer and the center external collaboration layer, the original scheduling plan is quickly adjusted to minimize the impact of disturbances while effectively completing dynamic task requirements. Finally, a large number of simulation experiments were carried out and compared with various comparative algorithms. The results show that the proposed algorithm can effectively improve the solution effect of satellite TT&C resource dynamic scheduling problems, and has good application prospects.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 2","pages":"166-183"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10598215","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Remanufacturing is regarded as a sustainable manufacturing paradigm of energy conservation and environment protection. To improve the efficiency of the remanufacturing process, this work investigates an integrated scheduling problem for disassembly and reprocessing in a remanufacturing process, where product structures and uncertainty are taken into account. First, a stochastic programming model is developed to minimize the maximum completion time (makespan). Second, a Q-learning based hybrid meta-heuristic (Q-HMH) is specially devised. In each iteration, a Q-learning method is employed to adaptively choose a premium algorithm from four candidate ones, including genetic algorithm (GA), artificial bee colony (ABC), shuffled frog-leaping algorithm (SFLA), and simulated annealing (SA) methods. At last, simulation experiments are carried out by using sixteen instances with different scales, and three state-of-the-art algorithms in literature and an exact solver CPLEX are chosen for comparisons. By analyzing the results with the average relative percentage deviation (RPD) metric, we find that Q-HMH outperforms its rivals by 9.79%-26.76%. The results and comparisons verify the excellent competitiveness of Q-HMH for solving the concerned problems.
{"title":"A Q-Learning Based Hybrid Meta-Heuristic for Integrated Scheduling of Disassembly and Reprocessing Processes Considering Product Structures and Stochasticity","authors":"Fuquan Wang;Yaping Fu;Kaizhou Gao;Yaoxin Wu;Song Gao","doi":"10.23919/CSMS.2024.0007","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0007","url":null,"abstract":"Remanufacturing is regarded as a sustainable manufacturing paradigm of energy conservation and environment protection. To improve the efficiency of the remanufacturing process, this work investigates an integrated scheduling problem for disassembly and reprocessing in a remanufacturing process, where product structures and uncertainty are taken into account. First, a stochastic programming model is developed to minimize the maximum completion time (makespan). Second, a Q-learning based hybrid meta-heuristic (Q-HMH) is specially devised. In each iteration, a Q-learning method is employed to adaptively choose a premium algorithm from four candidate ones, including genetic algorithm (GA), artificial bee colony (ABC), shuffled frog-leaping algorithm (SFLA), and simulated annealing (SA) methods. At last, simulation experiments are carried out by using sixteen instances with different scales, and three state-of-the-art algorithms in literature and an exact solver CPLEX are chosen for comparisons. By analyzing the results with the average relative percentage deviation (RPD) metric, we find that Q-HMH outperforms its rivals by 9.79%-26.76%. The results and comparisons verify the excellent competitiveness of Q-HMH for solving the concerned problems.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 2","pages":"184-209"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10598210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The heightened autonomy and robust adaptability inherent in a multi-robot system have proven pivotal in disaster search and rescue, agricultural irrigation, and environmental monitoring. This study addresses the coordination of multiple robots for the surveillance of various key target positions within an area. This involves the allocation of target positions among robots and the concurrent planning of routes for each robot. To tackle these challenges, we formulate a unified optimization model addressing both target allocation and route planning. Subsequently, we introduce an adaptive memetic algorithm featuring dual-level local search strategies. This algorithm operates independently among and within robots to effectively solve the optimization problem associated with surveillance. The proposed method's efficacy is substantiated through comparative numerical experiments and simulated experiments involving diverse scales of robot teams and different target positions.
{"title":"Adaptive Memetic Algorithm with Dual-Level Local Search for Cooperative Route Planning of Multi-Robot Surveillance Systems","authors":"Hao Cheng;Jin Yi;Wei Xia;Huayan Pu;Jun Luo","doi":"10.23919/CSMS.2024.0006","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0006","url":null,"abstract":"The heightened autonomy and robust adaptability inherent in a multi-robot system have proven pivotal in disaster search and rescue, agricultural irrigation, and environmental monitoring. This study addresses the coordination of multiple robots for the surveillance of various key target positions within an area. This involves the allocation of target positions among robots and the concurrent planning of routes for each robot. To tackle these challenges, we formulate a unified optimization model addressing both target allocation and route planning. Subsequently, we introduce an adaptive memetic algorithm featuring dual-level local search strategies. This algorithm operates independently among and within robots to effectively solve the optimization problem associated with surveillance. The proposed method's efficacy is substantiated through comparative numerical experiments and simulated experiments involving diverse scales of robot teams and different target positions.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 2","pages":"210-221"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10598213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dongqi Liu;Qiong Zhang;Haolan Liang;Tao Zhang;Rui Wang
The modern power system has evolved into a cyber-physical system with deep coupling of physical and information domains, which brings new security risks. Aiming at the problem that the “information-physical” cross-domain attacks with key nodes as springboards seriously threaten the safe and stable operation of power grids, a risk propagation model considering key nodes of power communication coupling networks is proposed to study the risk propagation characteristics of malicious attacks on key nodes and the impact on the system. First, combined with the complex network theory, a topological model of the power communication coupling network is established, and the key nodes of the coupling network are screened out by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method under the comprehensive evaluation index based on topological characteristics and physical characteristics. Second, a risk propagation model is established for malicious attacks on key nodes to study its propagation characteristics and analyze the state changes of each node in the coupled network. Then, two loss-causing factors: the minimum load loss ratio and transmission delay factor are constructed to quantify the impact of risk propagation on the coupled network. Finally, simulation analysis based on the IEEE 39-node system shows that the probability of node being breached $(alpha)$ and the security tolerance of the system $(beta)$ are the key factors affecting the risk propagation characteristics of the coupled network, as well as the criticality of the node is positively correlated with the damage-causing factor. The proposed methodological model can provide an effective exploration of the diffusion of security risks in control systems on a macro level.
{"title":"Modeling and Analysis of Risk Propagation and Loss Causing Capacity for Key Nodes in Cyber-Physical Coupled Power Network","authors":"Dongqi Liu;Qiong Zhang;Haolan Liang;Tao Zhang;Rui Wang","doi":"10.23919/CSMS.2024.0008","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0008","url":null,"abstract":"The modern power system has evolved into a cyber-physical system with deep coupling of physical and information domains, which brings new security risks. Aiming at the problem that the “information-physical” cross-domain attacks with key nodes as springboards seriously threaten the safe and stable operation of power grids, a risk propagation model considering key nodes of power communication coupling networks is proposed to study the risk propagation characteristics of malicious attacks on key nodes and the impact on the system. First, combined with the complex network theory, a topological model of the power communication coupling network is established, and the key nodes of the coupling network are screened out by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method under the comprehensive evaluation index based on topological characteristics and physical characteristics. Second, a risk propagation model is established for malicious attacks on key nodes to study its propagation characteristics and analyze the state changes of each node in the coupled network. Then, two loss-causing factors: the minimum load loss ratio and transmission delay factor are constructed to quantify the impact of risk propagation on the coupled network. Finally, simulation analysis based on the IEEE 39-node system shows that the probability of node being breached \u0000<tex>$(alpha)$</tex>\u0000 and the security tolerance of the system \u0000<tex>$(beta)$</tex>\u0000 are the key factors affecting the risk propagation characteristics of the coupled network, as well as the criticality of the node is positively correlated with the damage-causing factor. The proposed methodological model can provide an effective exploration of the diffusion of security risks in control systems on a macro level.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 2","pages":"124-136"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10598212","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The hybrid flow shop group scheduling problem (HFGSP) with the delivery time windows has been widely studied owing to its better flexibility and suitability for the current just-in-time production mode. However, there are several unresolved challenges in problem modeling and algorithmic design tailored for HFGSP. In our study, we place emphasis on the constraint of timeliness. Therefore, this paper first constructs a mixed integer linear programming model of HFGSP with sequence-dependent setup time and delivery time windows to minimize the total weighted earliness and tardiness (TWET). Then a penalty groups-assisted iterated greedy integrating idle time insertion (PG_IG_ITI) is proposed to solve the above problem. In the PG_IG_ITI, a double decoding strategy is proposed based on the earliest available machine rule and the idle time insertion rule to calculate the TWET value. Subsequently, to reduce the amount of computation, a skip-based destruction and reconstruction strategy is designed, and a penalty groups-assisted local search is proposed to further improve the quality of the solution by disturbing the penalized groups, i.e., early and tardy groups. Finally, through comprehensive statistical experiments on 270 test instances, the results prove that the proposed algorithm is effective compared to four state-of-the-art algorithms.
{"title":"A Penalty Groups-Assisted Iterated Greedy Integrating Idle Time Insertion: Solving the Hybrid Flow Shop Group Scheduling with Delivery Time Windows","authors":"Qianhui Ji;Yuyan Han;Yuting Wang;Biao Zhang;Kaizhou Gao","doi":"10.23919/CSMS.2024.0005","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0005","url":null,"abstract":"The hybrid flow shop group scheduling problem (HFGSP) with the delivery time windows has been widely studied owing to its better flexibility and suitability for the current just-in-time production mode. However, there are several unresolved challenges in problem modeling and algorithmic design tailored for HFGSP. In our study, we place emphasis on the constraint of timeliness. Therefore, this paper first constructs a mixed integer linear programming model of HFGSP with sequence-dependent setup time and delivery time windows to minimize the total weighted earliness and tardiness (TWET). Then a penalty groups-assisted iterated greedy integrating idle time insertion (PG_IG_ITI) is proposed to solve the above problem. In the PG_IG_ITI, a double decoding strategy is proposed based on the earliest available machine rule and the idle time insertion rule to calculate the TWET value. Subsequently, to reduce the amount of computation, a skip-based destruction and reconstruction strategy is designed, and a penalty groups-assisted local search is proposed to further improve the quality of the solution by disturbing the penalized groups, i.e., early and tardy groups. Finally, through comprehensive statistical experiments on 270 test instances, the results prove that the proposed algorithm is effective compared to four state-of-the-art algorithms.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 2","pages":"137-165"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10598214","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The research on complex workshop scheduling methods has important academic significance and has wide applications in industrial manufacturing. Aiming at the job shop scheduling problem, a hybrid algorithm based on comprehensive search mechanisms (HACSM) is proposed to optimize the maximum completion time. HACSM combines three search methods with different optimization scales, including fireworks algorithm (FW), extended Akers graphical method (LS1+_AKERS_EXT), and tabu search algorithm (TS). FW realizes global search through information interaction and resource allocation, ensuring the diversity of the population. LS1+_AKERS_EXT realizes compound movement with Akers graphical method, so it has advanced global and local search capabilities. In LS1+_AKERS_EXT, the shortest path is the core of the algorithm, which directly affects the encoding and decoding of scheduling. In order to find the shortest path, an effective node expansion method is designed to improve the node expansion efficiency. In the part of centralized search, TS based on the neighborhood structure is used. Finally, the effectiveness and superiority of HACSM are verified by testing the relevant instances in the literature.
{"title":"A Hybrid Algorithm Based on Comprehensive Search Mechanisms for Job Shop Scheduling Problem","authors":"Lin Huang;Shikui Zhao;Yingjie Xiong","doi":"10.23919/CSMS.2024.0001","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0001","url":null,"abstract":"The research on complex workshop scheduling methods has important academic significance and has wide applications in industrial manufacturing. Aiming at the job shop scheduling problem, a hybrid algorithm based on comprehensive search mechanisms (HACSM) is proposed to optimize the maximum completion time. HACSM combines three search methods with different optimization scales, including fireworks algorithm (FW), extended Akers graphical method (LS1+_AKERS_EXT), and tabu search algorithm (TS). FW realizes global search through information interaction and resource allocation, ensuring the diversity of the population. LS1+_AKERS_EXT realizes compound movement with Akers graphical method, so it has advanced global and local search capabilities. In LS1+_AKERS_EXT, the shortest path is the core of the algorithm, which directly affects the encoding and decoding of scheduling. In order to find the shortest path, an effective node expansion method is designed to improve the node expansion efficiency. In the part of centralized search, TS based on the neighborhood structure is used. Finally, the effectiveness and superiority of HACSM are verified by testing the relevant instances in the literature.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 1","pages":"50-66"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10525670","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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