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}
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 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}
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}
Enhancing the adaptability of Unmanned Aerial Vehicle (UAV) swarm control models to cope with different complex working scenarios is an important issue in this research field. To achieve this goal, control model with tunable parameters is a widely adopted approach. In this article, an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking (MO O-Flocking) is proposed. The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters. To achieve multi-objective parameter tuning, a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2 (ISPEA2) is designed. Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives. Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.
提高无人飞行器（UAV）蜂群控制模型的适应性，以应对不同的复杂工作场景，是该研究领域的一个重要问题。为实现这一目标，参数可调的控制模型被广泛采用。本文提出了一种参数可调的改进型无人机蜂群控制模型，即多目标 O-Flocking（MO O-Flocking）。MO O-Flocking模型是多规则控制系统与基于虚拟物理定律的可调参数控制模型的结合。为实现多目标参数调整，设计了一种多目标参数调整方法，即改进强度帕累托进化算法 2（ISPEA2）。仿真实验场景包括六种不同目标的目标定向场景。实验结果表明，ISPEA2 算法和 MO O-Flocking 控制模型在各自的实验场景中都有良好的表现。
{"title":"Multi-Objective Rule System Based Control Model with Tunable Parameters for Swarm Robotic Control in Confined Environment","authors":"Yuan Wang;Lining Xing;Junde Wang;Tao Xie;Lidong Chen","doi":"10.23919/CSMS.2023.0022","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0022","url":null,"abstract":"Enhancing the adaptability of Unmanned Aerial Vehicle (UAV) swarm control models to cope with different complex working scenarios is an important issue in this research field. To achieve this goal, control model with tunable parameters is a widely adopted approach. In this article, an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking (MO O-Flocking) is proposed. The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters. To achieve multi-objective parameter tuning, a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2 (ISPEA2) is designed. Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives. Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 1","pages":"33-49"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10525674","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895069","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}
Power grids, due to their lack of network redundancy and structural interdependence, are particularly vulnerable to cascading failures, a phenomenon where a few failed nodes-having their loads exceeding their capacities—can trigger a widespread collapse of all nodes. Here, we extend the cascading failure (Motter-Lai) model to a more realistic perspective, where each node's load capacity is determined to be nonlinearly correlated with the node's centrality. Our analysis encompasses a range of synthetic networks featuring small-world or scale-free properties, as well as real-world network configurations like the IEEE bus systems and the US power grid. We find that fine-tuning this nonlinear relationship can significantly enhance a network's robustness against cascading failures when the network nodes are under attack. Additionally, the selection of initial nodes and the attack strategies also impact overall network robustness. Our findings offer valuable insights for improving the safety and resilience of power grids, bringing us closer to understanding cascading failures in a more realistic context.
{"title":"Cascading Failures in Power Grids: A Load Capacity Model with Node Centrality","authors":"Chaoyang Chen;Yao Hu;Xiangyi Meng;Jinzhu Yu","doi":"10.23919/CSMS.2023.0020","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0020","url":null,"abstract":"Power grids, due to their lack of network redundancy and structural interdependence, are particularly vulnerable to cascading failures, a phenomenon where a few failed nodes-having their loads exceeding their capacities—can trigger a widespread collapse of all nodes. Here, we extend the cascading failure (Motter-Lai) model to a more realistic perspective, where each node's load capacity is determined to be nonlinearly correlated with the node's centrality. Our analysis encompasses a range of synthetic networks featuring small-world or scale-free properties, as well as real-world network configurations like the IEEE bus systems and the US power grid. We find that fine-tuning this nonlinear relationship can significantly enhance a network's robustness against cascading failures when the network nodes are under attack. Additionally, the selection of initial nodes and the attack strategies also impact overall network robustness. Our findings offer valuable insights for improving the safety and resilience of power grids, bringing us closer to understanding cascading failures in a more realistic context.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 1","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10525231","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895070","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}
There are many studies about flexible job shop scheduling problem with fuzzy processing time and deteriorating scheduling, but most scholars neglect the connection between them, which means the purpose of both models is to simulate a more realistic factory environment. From this perspective, the solutions can be more precise and practical if both issues are considered simultaneously. Therefore, the deterioration effect is treated as a part of the fuzzy job shop scheduling problem in this paper, which means the linear increase of a certain processing time is transformed into an internal linear shift of a triangle fuzzy processing time. Apart from that, many other contributions can be stated as follows. A new algorithm called reinforcement learning based biased bi-population evolutionary algorithm (RB 2 EA) is proposed, which utilizes Q-learning algorithm to adjust the size of the two populations and the interaction frequency according to the quality of population. A local enhancement method which combimes multiple local search stratgies is presented. An interaction mechanism is designed to promote the convergence of the bi-population. Extensive experiments are designed to evaluate the efficacy of RB 2 EA, and the conclusion can be drew that RB 2 EA is able to solve energy-efficient fuzzy flexible job shop scheduling problem with deteriorating jobs (EFFJSPD) efficiently.
{"title":"Biased Bi-Population Evolutionary Algorithm for Energy-Efficient Fuzzy Flexible Job Shop Scheduling with Deteriorating Jobs","authors":"Libao Deng;Yingjian Zhu;Yuanzhu Di;Lili Zhang","doi":"10.23919/CSMS.2023.0021","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0021","url":null,"abstract":"There are many studies about flexible job shop scheduling problem with fuzzy processing time and deteriorating scheduling, but most scholars neglect the connection between them, which means the purpose of both models is to simulate a more realistic factory environment. From this perspective, the solutions can be more precise and practical if both issues are considered simultaneously. Therefore, the deterioration effect is treated as a part of the fuzzy job shop scheduling problem in this paper, which means the linear increase of a certain processing time is transformed into an internal linear shift of a triangle fuzzy processing time. Apart from that, many other contributions can be stated as follows. A new algorithm called reinforcement learning based biased bi-population evolutionary algorithm (RB\u0000<sup>2</sup>\u0000EA) is proposed, which utilizes Q-learning algorithm to adjust the size of the two populations and the interaction frequency according to the quality of population. A local enhancement method which combimes multiple local search stratgies is presented. An interaction mechanism is designed to promote the convergence of the bi-population. Extensive experiments are designed to evaluate the efficacy of RB\u0000<sup>2</sup>\u0000EA, and the conclusion can be drew that RB\u0000<sup>2</sup>\u0000EA is able to solve energy-efficient fuzzy flexible job shop scheduling problem with deteriorating jobs (EFFJSPD) efficiently.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 1","pages":"15-32"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10525671","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895034","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}
This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop scheduling problem with sequence-dependent setup time (EADHFSP-ST) that simultaneously optimizes the makespan and the energy consumption. We develop a mixed integer linear programming model to describe this problem and present a two-stage adaptive memetic algorithm (TAMA) with a surprisingly popular mechanism. First, a hybrid initialization strategy is designed based on the two optimization objectives to ensure the convergence and diversity of solutions. Second, multiple population co-evolutionary approaches are proposed for global search to escape from traditional cross-randomization and to balance exploration and exploitation. Third, considering that the memetic algorithm (MA) framework is less efficient due to the randomness in the selection of local search operators, TAMA is proposed to balance the local and global searches. The first stage accumulates more experience for updating the surprisingly popular algorithm (SPA) model to guide the second stage operator selection and ensures population convergence. The second stage gets rid of local optimization and designs an elite archive to ensure population diversity. Fourth, five problem-specific operators are designed, and non-critical path deceleration and right-shift strategies are designed for energy efficiency. Finally, to evaluate the performance of the proposed algorithm, multiple experiments are performed on a benchmark with 45 instances. The experimental results show that the proposed TAMA can solve the problem effectively.
{"title":"Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time","authors":"Feng Chen;Cong Luo;Wenyin Gong;Chao Lu","doi":"10.23919/CSMS.2024.0003","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0003","url":null,"abstract":"This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop scheduling problem with sequence-dependent setup time (EADHFSP-ST) that simultaneously optimizes the makespan and the energy consumption. We develop a mixed integer linear programming model to describe this problem and present a two-stage adaptive memetic algorithm (TAMA) with a surprisingly popular mechanism. First, a hybrid initialization strategy is designed based on the two optimization objectives to ensure the convergence and diversity of solutions. Second, multiple population co-evolutionary approaches are proposed for global search to escape from traditional cross-randomization and to balance exploration and exploitation. Third, considering that the memetic algorithm (MA) framework is less efficient due to the randomness in the selection of local search operators, TAMA is proposed to balance the local and global searches. The first stage accumulates more experience for updating the surprisingly popular algorithm (SPA) model to guide the second stage operator selection and ensures population convergence. The second stage gets rid of local optimization and designs an elite archive to ensure population diversity. Fourth, five problem-specific operators are designed, and non-critical path deceleration and right-shift strategies are designed for energy efficiency. Finally, to evaluate the performance of the proposed algorithm, multiple experiments are performed on a benchmark with 45 instances. The experimental results show that the proposed TAMA can solve the problem effectively.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 1","pages":"82-108"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10525672","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895035","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}
Lin Wang;Cong Wang;Hongli Zhang;Ping Ma;Shaohua Zhang
A fractional-order memristor load Buck-Boost converter causes periodic system oscillation, electromagnetic noise, and other phenomena due to the frequent switching of the switch in actual operation, which is detrimental to the stable operation of the power electronic converter. It is of great significance to the study of the modeling method and chaos control strategy to suppress the nonlinear behavior of the Buck-Boost converter and expand the safe and stable operation range of the power system. An estimation-correction modeling method based on a fractional active voltage-controlled memristor load peak current Buck-Boost converter is proposed. The discrete numerical solution of the state variables in the continuous mode of the inductor current is derived. The bursting oscillation phenomenon when the system introduces external excitation is analyzed. Using bifurcation, Lyapunov exponent, and phase diagrams, a large number of numerical simulations are performed. The results show that the Buck-Boost converter is chaotic for certain selected parameters, which is the prerequisite for the introduction of the controller. Based on the idea of parameter perturbation and state association, a three-dimensional hybrid control strategy for a fractional memristor Buck-Boost converter is designed. The effectiveness of the control strategy is verified by simulations, and it is confirmed that the system is controlled in a stable periodic state when the external tunable parameter $s$ , which represents the coupling strength between the state variables in the system, gradually decreases in [−0.4, 0]. Compared with integer-order controlled systems, the stable operating range of fractional-order controlled systems is much larger.
{"title":"Estimation-Correction Modeling and Chaos Control of Fractional-Order Memristor Load Buck-Boost Converter","authors":"Lin Wang;Cong Wang;Hongli Zhang;Ping Ma;Shaohua Zhang","doi":"10.23919/CSMS.2024.0002","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0002","url":null,"abstract":"A fractional-order memristor load Buck-Boost converter causes periodic system oscillation, electromagnetic noise, and other phenomena due to the frequent switching of the switch in actual operation, which is detrimental to the stable operation of the power electronic converter. It is of great significance to the study of the modeling method and chaos control strategy to suppress the nonlinear behavior of the Buck-Boost converter and expand the safe and stable operation range of the power system. An estimation-correction modeling method based on a fractional active voltage-controlled memristor load peak current Buck-Boost converter is proposed. The discrete numerical solution of the state variables in the continuous mode of the inductor current is derived. The bursting oscillation phenomenon when the system introduces external excitation is analyzed. Using bifurcation, Lyapunov exponent, and phase diagrams, a large number of numerical simulations are performed. The results show that the Buck-Boost converter is chaotic for certain selected parameters, which is the prerequisite for the introduction of the controller. Based on the idea of parameter perturbation and state association, a three-dimensional hybrid control strategy for a fractional memristor Buck-Boost converter is designed. The effectiveness of the control strategy is verified by simulations, and it is confirmed that the system is controlled in a stable periodic state when the external tunable parameter \u0000<tex>$s$</tex>\u0000, which represents the coupling strength between the state variables in the system, gradually decreases in [−0.4, 0]. Compared with integer-order controlled systems, the stable operating range of fractional-order controlled systems is much larger.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 1","pages":"67-81"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10525676","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895054","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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