The Differential Evolution (DE) algorithm, which is an efficient optimization algorithm, has been used to solve various optimization problems. In this paper, adaptive dimensional learning with a tolerance framework for DE is proposed. The population is divided into an elite subpopulation, an ordinary subpopulation, and an inferior subpopulation according to the fitness values. The ordinary and elite subpopulations are used to maintain the current evolution state and to guide the evolution direction of the population, respectively. The inferior subpopulation learns from the elite subpopulation through the dimensional learning strategy. If the global optimum is not improved in a specified number of iterations, a tolerance mechanism is applied. Under the tolerance mechanism, the inferior and elite subpopulations implement the restart strategy and the reverse dimensional learning strategy, respectively. In addition, the individual status and algorithm status are used to adaptively adjust the control parameters. To evaluate the performance of the proposed algorithm, six state-of-the-art DE algorithm variants are compared on the benchmark functions. The results of the simulation show that the proposed algorithm outperforms other variant algorithms regarding function convergence rate and solution accuracy.
{"title":"Adaptive Dimensional Learning with a Tolerance Framework for the Differential Evolution Algorithm","authors":"Wei Li;Xinqiang Ye;Ying Huang;Soroosh Mahmoodi","doi":"10.23919/CSMS.2022.0001","DOIUrl":"10.23919/CSMS.2022.0001","url":null,"abstract":"The Differential Evolution (DE) algorithm, which is an efficient optimization algorithm, has been used to solve various optimization problems. In this paper, adaptive dimensional learning with a tolerance framework for DE is proposed. The population is divided into an elite subpopulation, an ordinary subpopulation, and an inferior subpopulation according to the fitness values. The ordinary and elite subpopulations are used to maintain the current evolution state and to guide the evolution direction of the population, respectively. The inferior subpopulation learns from the elite subpopulation through the dimensional learning strategy. If the global optimum is not improved in a specified number of iterations, a tolerance mechanism is applied. Under the tolerance mechanism, the inferior and elite subpopulations implement the restart strategy and the reverse dimensional learning strategy, respectively. In addition, the individual status and algorithm status are used to adaptively adjust the control parameters. To evaluate the performance of the proposed algorithm, six state-of-the-art DE algorithm variants are compared on the benchmark functions. The results of the simulation show that the proposed algorithm outperforms other variant algorithms regarding function convergence rate and solution accuracy.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"2 1","pages":"59-77"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9770077/09770099.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42811654","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}
Kangjia Qiao;Jing Liang;Boyang Qu;Kunjie Yu;Caitong Yue;Hui Song
To address complex single objective global optimization problems, a new Level-Based Learning Differential Evolution (LBLDE) is developed in this study. In this approach, the whole population is sorted from the best to the worst at the beginning of each generation. Then, the population is partitioned into multiple levels, and different levels are used to exert different functions. In each level, a control parameter is used to select excellent exemplars from upper levels for learning. In this case, the poorer individuals can choose more learning exemplars to improve their exploration ability, and excellent individuals can directly learn from the several best individuals to improve the quality of solutions. To accelerate the convergence speed, a difference vector selection method based on the level is developed. Furthermore, specific crossover rates are assigned to individuals at the lowest level to guarantee that the population can continue to update during the later evolutionary process. A comprehensive experiment is organized and conducted to obtain a deep insight into LBLDE and demonstrates the superiority of LBLDE in comparison with seven peer DE variants.
{"title":"Differential Evolution with Level-Based Learning Mechanism","authors":"Kangjia Qiao;Jing Liang;Boyang Qu;Kunjie Yu;Caitong Yue;Hui Song","doi":"10.23919/CSMS.2022.0004","DOIUrl":"10.23919/CSMS.2022.0004","url":null,"abstract":"To address complex single objective global optimization problems, a new Level-Based Learning Differential Evolution (LBLDE) is developed in this study. In this approach, the whole population is sorted from the best to the worst at the beginning of each generation. Then, the population is partitioned into multiple levels, and different levels are used to exert different functions. In each level, a control parameter is used to select excellent exemplars from upper levels for learning. In this case, the poorer individuals can choose more learning exemplars to improve their exploration ability, and excellent individuals can directly learn from the several best individuals to improve the quality of solutions. To accelerate the convergence speed, a difference vector selection method based on the level is developed. Furthermore, specific crossover rates are assigned to individuals at the lowest level to guarantee that the population can continue to update during the later evolutionary process. A comprehensive experiment is organized and conducted to obtain a deep insight into LBLDE and demonstrates the superiority of LBLDE in comparison with seven peer DE variants.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"2 1","pages":"35-58"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9770077/09770098.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45087811","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}
To improve the accuracy of typhoon prediction, it is necessary to detect the internal structure of a typhoon. The motion model of a floating weather sensing node becomes the key to affect the channel frequency expansion performance and communication quality. This study proposes a floating weather sensing node motion modeling method based on the chaotic mapping. After the chaotic attractor is obtained by simulation, the position trajectory of the floating weather sensing node is obtained by space and coordinate conversion, and the three-dimensional velocity of each point on the position trajectory is obtained by multidimensional linear interpolation. On this basis, the established motion model is used to study the Doppler frequency shift, which is based on the software and physical platform. The software simulates the relative motion of the transceiver and calculates the Doppler frequency shift. The physical platform can add the Doppler frequency shift to the actual transmitted signal. The results show that this method can effectively reflect the influence of the floating weather sensing node motion on signal transmission. It is helpful to research the characteristics of the communication link and the design of a signal transceiver for typhoon detection to further improve the communication quality and to obtain more accurate interior structure characteristic data of a typhoon.
{"title":"Motion Model of Floating Weather Sensing Node for Typhoon Detection","authors":"Hui Lu;Xinyu Dong;Xianbin Cao","doi":"10.23919/CSMS.2021.0029","DOIUrl":"10.23919/CSMS.2021.0029","url":null,"abstract":"To improve the accuracy of typhoon prediction, it is necessary to detect the internal structure of a typhoon. The motion model of a floating weather sensing node becomes the key to affect the channel frequency expansion performance and communication quality. This study proposes a floating weather sensing node motion modeling method based on the chaotic mapping. After the chaotic attractor is obtained by simulation, the position trajectory of the floating weather sensing node is obtained by space and coordinate conversion, and the three-dimensional velocity of each point on the position trajectory is obtained by multidimensional linear interpolation. On this basis, the established motion model is used to study the Doppler frequency shift, which is based on the software and physical platform. The software simulates the relative motion of the transceiver and calculates the Doppler frequency shift. The physical platform can add the Doppler frequency shift to the actual transmitted signal. The results show that this method can effectively reflect the influence of the floating weather sensing node motion on signal transmission. It is helpful to research the characteristics of the communication link and the design of a signal transceiver for typhoon detection to further improve the communication quality and to obtain more accurate interior structure characteristic data of a typhoon.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"2 1","pages":"96-111"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9770077/09770101.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49090832","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}
Vehicle routing problem with time windows (VRPTW) is a core combinatorial optimization problem in distribution tasks. The electric vehicle routing problem with time windows under demand uncertainty and weight-related energy consumption is an extension of the VRPTW. Although some researchers have studied either the electric VRPTW with nonlinear energy consumption model or the impact of the uncertain customer demand on the conventional vehicles, the literature on the integration of uncertain demand and energy consumption of electric vehicles is still scarce. However, practically, it is usually not feasible to ignore the uncertainty of customer demand and the weight-related energy consumption of electronic vehicles (EVs) in actual operation. Hence, we propose the robust optimization model based on a route-related uncertain set to tackle this problem. Moreover, adaptive large neighbourhood search heuristic has been developed to solve the problem due to the NP-hard nature of the problem. The effectiveness of the method is verified by experiments, and the influence of uncertain demand and uncertain parameters on the solution is further explored.
{"title":"Robust Electric Vehicle Routing Problem with Time Windows under Demand Uncertainty and Weight-Related Energy Consumption","authors":"Yindong Shen;Leqin Yu;Jingpeng Li","doi":"10.23919/CSMS.2022.0005","DOIUrl":"10.23919/CSMS.2022.0005","url":null,"abstract":"Vehicle routing problem with time windows (VRPTW) is a core combinatorial optimization problem in distribution tasks. The electric vehicle routing problem with time windows under demand uncertainty and weight-related energy consumption is an extension of the VRPTW. Although some researchers have studied either the electric VRPTW with nonlinear energy consumption model or the impact of the uncertain customer demand on the conventional vehicles, the literature on the integration of uncertain demand and energy consumption of electric vehicles is still scarce. However, practically, it is usually not feasible to ignore the uncertainty of customer demand and the weight-related energy consumption of electronic vehicles (EVs) in actual operation. Hence, we propose the robust optimization model based on a route-related uncertain set to tackle this problem. Moreover, adaptive large neighbourhood search heuristic has been developed to solve the problem due to the NP-hard nature of the problem. The effectiveness of the method is verified by experiments, and the influence of uncertain demand and uncertain parameters on the solution is further explored.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"2 1","pages":"18-34"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9770077/09770078.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42853017","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}
Nonlinear equations systems (NESs) arise in a wide range of domains. Solving NESs requires the algorithm to locate multiple roots simultaneously. To deal with NESs efficiently, this study presents an enhanced reinforcement learning based differential evolution with the following major characteristics: (1) the design of state function uses the information on the fitness alternation action; (2) different neighborhood sizes and mutation strategies are combined as optional actions; and (3) the unbalanced assignment method is adopted to change the reward value to select the optimal actions. To evaluate the performance of our approach, 30 NESs test problems and 18 test instances with different features are selected as the test suite. The experimental results indicate that the proposed approach can improve the performance in solving NESs, and outperform several state-of-the-art methods.
{"title":"Solving Nonlinear Equations Systems with an Enhanced Reinforcement Learning Based Differential Evolution","authors":"Zuowen Liao;Shuijia Li","doi":"10.23919/CSMS.2022.0003","DOIUrl":"10.23919/CSMS.2022.0003","url":null,"abstract":"Nonlinear equations systems (NESs) arise in a wide range of domains. Solving NESs requires the algorithm to locate multiple roots simultaneously. To deal with NESs efficiently, this study presents an enhanced reinforcement learning based differential evolution with the following major characteristics: (1) the design of state function uses the information on the fitness alternation action; (2) different neighborhood sizes and mutation strategies are combined as optional actions; and (3) the unbalanced assignment method is adopted to change the reward value to select the optimal actions. To evaluate the performance of our approach, 30 NESs test problems and 18 test instances with different features are selected as the test suite. The experimental results indicate that the proposed approach can improve the performance in solving NESs, and outperform several state-of-the-art methods.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"2 1","pages":"78-95"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9770077/09770100.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45986503","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}
Ning Zhao;Gabriel Lodewijks;Zhuorui Fu;Yu Sun;Yue Sun
Nowadays, more automated or robotic twin-crane systems (RTCSs) are employed in ports and factories to improve material handling efficiency. In a twin-crane system, cranes must travel with a minimum safety distance between them to prevent interference. The crane trajectory prediction is critical to interference handling and crane scheduling. Current trajectory predictions lack accuracy because many details are simplified. To enhance accuracy and lessen the trajectory prediction time, a trajectory prediction approach with details (crane acceleration/deceleration, different crane velocities when loading/unloading, and trolley movement) is proposed in this paper. Simulations on different details and their combinations are conducted on a container terminal case study. According to the simulation results, the accuracy of the trajectory prediction can be improved by 20%. The proposed trajectory prediction approach is helpful for building a digital twin of RTCSs and enhancing crane scheduling.
{"title":"Trajectory Predictions with Details in a Robotic Twin-Crane System","authors":"Ning Zhao;Gabriel Lodewijks;Zhuorui Fu;Yu Sun;Yue Sun","doi":"10.23919/CSMS.2021.0028","DOIUrl":"10.23919/CSMS.2021.0028","url":null,"abstract":"Nowadays, more automated or robotic twin-crane systems (RTCSs) are employed in ports and factories to improve material handling efficiency. In a twin-crane system, cranes must travel with a minimum safety distance between them to prevent interference. The crane trajectory prediction is critical to interference handling and crane scheduling. Current trajectory predictions lack accuracy because many details are simplified. To enhance accuracy and lessen the trajectory prediction time, a trajectory prediction approach with details (crane acceleration/deceleration, different crane velocities when loading/unloading, and trolley movement) is proposed in this paper. Simulations on different details and their combinations are conducted on a container terminal case study. According to the simulation results, the accuracy of the trajectory prediction can be improved by 20%. The proposed trajectory prediction approach is helpful for building a digital twin of RTCSs and enhancing crane scheduling.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"2 1","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9770077/09770079.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47801702","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}
Smart manufacturing in the “Industry 4.0” strategy promotes the deep integration of manufacturing and information technologies, which makes the manufacturing system a ubiquitous environment. However, the real-time scheduling of such a manufacturing system is a challenge faced by many decision makers. To deal with this challenge, this study focuses on the real-time hybrid flow shop scheduling problem (HFSP). First, the characteristic of the hybrid flow shop in a smart manufacturing environment is analyzed, and its scheduling problem is described. Second, a real-time scheduling approach for the HFSP is proposed. The core module is to employ gene expression programming to construct a new and efficient scheduling rule according to the realtime status in the hybrid flow shop. With the scheduling rule, the priorities of the waiting job are calculated, and the job with the highest priority will be scheduled at this decision time point. A group of experiments are performed to prove the performance of the proposed approach. The numerical experiments show that the realtime scheduling approach outperforms other single-scheduling rules and the back-propagation neural network method in optimizing most objectives for different size instances. Therefore, the contribution of this study is the proposal of a real-time scheduling approach, which is an effective approach for real-time hybrid flow shop scheduling in a smart manufacturing environment.
{"title":"Real-Time Hybrid Flow Shop Scheduling Approach in Smart Manufacturing Environment","authors":"Xiuli Wu;Zheng Cao;Shaomin Wu","doi":"10.23919/CSMS.2021.0024","DOIUrl":"https://doi.org/10.23919/CSMS.2021.0024","url":null,"abstract":"Smart manufacturing in the “Industry 4.0” strategy promotes the deep integration of manufacturing and information technologies, which makes the manufacturing system a ubiquitous environment. However, the real-time scheduling of such a manufacturing system is a challenge faced by many decision makers. To deal with this challenge, this study focuses on the real-time hybrid flow shop scheduling problem (HFSP). First, the characteristic of the hybrid flow shop in a smart manufacturing environment is analyzed, and its scheduling problem is described. Second, a real-time scheduling approach for the HFSP is proposed. The core module is to employ gene expression programming to construct a new and efficient scheduling rule according to the realtime status in the hybrid flow shop. With the scheduling rule, the priorities of the waiting job are calculated, and the job with the highest priority will be scheduled at this decision time point. A group of experiments are performed to prove the performance of the proposed approach. The numerical experiments show that the realtime scheduling approach outperforms other single-scheduling rules and the back-propagation neural network method in optimizing most objectives for different size instances. Therefore, the contribution of this study is the proposal of a real-time scheduling approach, which is an effective approach for real-time hybrid flow shop scheduling in a smart manufacturing environment.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"1 4","pages":"335-350"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9673697/09673700.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49945131","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}
With the increase of problem dimensions, most solutions of existing many-objective optimization algorithms are non-dominant. Therefore, the selection of individuals and the retention of elite individuals are important. Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems. Thus, this work proposes an improved many-objective pigeon-inspired optimization (ImMAPIO) algorithm with multiple selection strategies to solve many-objective optimization problems. Multiple selection strategies integrating hypervolume, knee point, and vector angles are utilized to increase selection pressure to the true Pareto Front. Thus, the accuracy, convergence, and diversity of solutions are improved. ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III, GrEA, MOEA/D, RVEA, and many-objective Pigeon-inspired optimization algorithm. Experimental results indicate the superiority of ImMAPIO on these test functions.
{"title":"Novel PIO Algorithm with Multiple Selection Strategies for Many-Objective Optimization Problems","authors":"Zhihua Cui;Lihong Zhao;Youqian Zeng;Yeqing Ren;Wensheng Zhang;Xiao-Zhi Gao","doi":"10.23919/CSMS.2021.0023","DOIUrl":"10.23919/CSMS.2021.0023","url":null,"abstract":"With the increase of problem dimensions, most solutions of existing many-objective optimization algorithms are non-dominant. Therefore, the selection of individuals and the retention of elite individuals are important. Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems. Thus, this work proposes an improved many-objective pigeon-inspired optimization (ImMAPIO) algorithm with multiple selection strategies to solve many-objective optimization problems. Multiple selection strategies integrating hypervolume, knee point, and vector angles are utilized to increase selection pressure to the true Pareto Front. Thus, the accuracy, convergence, and diversity of solutions are improved. ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III, GrEA, MOEA/D, RVEA, and many-objective Pigeon-inspired optimization algorithm. Experimental results indicate the superiority of ImMAPIO on these test functions.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"1 4","pages":"291-307"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9673697/09673692.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45683475","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 systems is different from that on general systems because the former must consider self-organization, emergence, uncertainty, predetermination, and evolution. As an important method to transform the world, a simulation is one of the most important skills to discover complex systems. In this study, we provide a survey on complex systems and their simulation methods. Initially, the development history of complex system research is summarized from two main lines. Then, the eight common characteristics of the most complex systems are presented. Furthermore, the simulation methods of complex systems are introduced in detail from four aspects, namely, meta-synthesis methods, complex networks, intelligent technologies, and other methods. From the overall point of view, intelligent technologies are the driving force, and complex networks are the advanced structure. Meta-synthesis methods are the integration strategy, and other methods are the supplements. In addition, we show three complex system simulation examples: digital reactor simulation, simulation of a logistics system in the industrial site, and crowd evacuation simulation. The examples show that a simulation is a useful means and an important method in complex system research. Finally, the future development prospects for complex systems and their simulation methods are suggested.
{"title":"State-of-the-Art Development of Complex Systems and Their Simulation Methods","authors":"Yiming Tang;Lin Li;Xiaoping Liu","doi":"10.23919/CSMS.2021.0025","DOIUrl":"https://doi.org/10.23919/CSMS.2021.0025","url":null,"abstract":"The research on complex systems is different from that on general systems because the former must consider self-organization, emergence, uncertainty, predetermination, and evolution. As an important method to transform the world, a simulation is one of the most important skills to discover complex systems. In this study, we provide a survey on complex systems and their simulation methods. Initially, the development history of complex system research is summarized from two main lines. Then, the eight common characteristics of the most complex systems are presented. Furthermore, the simulation methods of complex systems are introduced in detail from four aspects, namely, meta-synthesis methods, complex networks, intelligent technologies, and other methods. From the overall point of view, intelligent technologies are the driving force, and complex networks are the advanced structure. Meta-synthesis methods are the integration strategy, and other methods are the supplements. In addition, we show three complex system simulation examples: digital reactor simulation, simulation of a logistics system in the industrial site, and crowd evacuation simulation. The examples show that a simulation is a useful means and an important method in complex system research. Finally, the future development prospects for complex systems and their simulation methods are suggested.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"1 4","pages":"271-290"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9673697/09673699.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49945129","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 the critical component of manufacturing systems, production scheduling aims to optimize objectives in terms of profit, efficiency, and energy consumption by reasonably determining the main factors including processing path, machine assignment, execute time and so on. Due to the large scale and strongly coupled constraints nature, as well as the real-time solving requirement in certain scenarios, it faces great challenges in solving the manufacturing scheduling problems. With the development of machine learning, Reinforcement Learning (RL) has made breakthroughs in a variety of decision-making problems. For manufacturing scheduling problems, in this paper we summarize the designs of state and action, tease out RL-based algorithm for scheduling, review the applications of RL for different types of scheduling problems, and then discuss the fusion modes of reinforcement learning and meta-heuristics. Finally, we analyze the existing problems in current research, and point out the future research direction and significant contents to promote the research and applications of RL-based scheduling optimization.
{"title":"A Review of Reinforcement Learning Based Intelligent Optimization for Manufacturing Scheduling","authors":"Ling Wang;Zixiao Pan;Jingjing Wang","doi":"10.23919/CSMS.2021.0027","DOIUrl":"https://doi.org/10.23919/CSMS.2021.0027","url":null,"abstract":"As the critical component of manufacturing systems, production scheduling aims to optimize objectives in terms of profit, efficiency, and energy consumption by reasonably determining the main factors including processing path, machine assignment, execute time and so on. Due to the large scale and strongly coupled constraints nature, as well as the real-time solving requirement in certain scenarios, it faces great challenges in solving the manufacturing scheduling problems. With the development of machine learning, Reinforcement Learning (RL) has made breakthroughs in a variety of decision-making problems. For manufacturing scheduling problems, in this paper we summarize the designs of state and action, tease out RL-based algorithm for scheduling, review the applications of RL for different types of scheduling problems, and then discuss the fusion modes of reinforcement learning and meta-heuristics. Finally, we analyze the existing problems in current research, and point out the future research direction and significant contents to promote the research and applications of RL-based scheduling optimization.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"1 4","pages":"257-270"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9673697/09673698.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49945135","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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