In order to reduce average arterial vehicle delay, a novel distributed and coordinated traffic control algorithm is developed using the multiple agent system and the reinforce learning （RL）. The RL is used to minimi...In order to reduce average arterial vehicle delay, a novel distributed and coordinated traffic control algorithm is developed using the multiple agent system and the reinforce learning （RL）. The RL is used to minimize average delay of arterial vehicles by training the interaction ability between agents and exterior environments. The Robertson platoon dispersion model is embedded in the RL algorithm to precisely predict platoon movements on arteries and then the reward function is developed based on the dispersion model and delay equations cited by HCM2000. The performance of the algorithm is evaluated in a Matlab environment and comparisons between the algorithm and the conventional coordination algorithm are conducted in three different traffic load scenarios. Results show that the proposed algorithm outperforms the conventional algorithm in all the scenarios. Moreover, with the increase in saturation degree, the performance is improved more significantly. The results verify the feasibility and efficiency of the established algorithm.展开更多
It is urgent to effectively improve the production efficiency in the running process of manufacturing systems through a new generation of information technology.According to the current growing trend of the internet o...It is urgent to effectively improve the production efficiency in the running process of manufacturing systems through a new generation of information technology.According to the current growing trend of the internet of things(IOT)in the manufacturing industry,aiming at the capacitor manufacturing plant,a multi-level architecture oriented to IOT-based manufacturing environment is established for a flexible flow-shop scheduling system.Next,according to multi-source manufacturing information driven in the manufacturing execution process,a scheduling optimization model based on the lot-streaming strategy is proposed under the framework.An improved distribution estimation algorithm is developed to obtain the optimal solution of the problem by balancing local search and global search.Finally,experiments are carried out and the results verify the feasibility and effectiveness of the proposed approach.展开更多
With today＇s global economic downturn and the increasingly fierce market competition, manufacturing enterprises must guarantee the efficient operation of production system, in order to get ahead in the competition, s...With today＇s global economic downturn and the increasingly fierce market competition, manufacturing enterprises must guarantee the efficient operation of production system, in order to get ahead in the competition, scheduling reasonable flow shop production systems can improve productivity and equipment utilization rate, reduce production costs. So the production system of flow shop scheduling problem has become one of the core problems ofmanufactaring enterprises the use of more and more.展开更多
Batteries transfer management is one important aspect in electric vehicle(EV)network's intelligent operation management system.Batteries transfer is a special and much more complex VRP(Vehicle Routing Problem) whi...Batteries transfer management is one important aspect in electric vehicle(EV)network's intelligent operation management system.Batteries transfer is a special and much more complex VRP(Vehicle Routing Problem) which takes the multiple constraints such as dynamic multi-depots,time windows,simultaneous pickups and deliveries,distance minimization,etc.into account.We call it VRPEVB(VRP with EV Batteries).This paper,based on the intelligent management model of EV's battery power,puts forward a battery transfer algorithm for the EV network which considers the traffic congestion that changes dynamically and uses improved Ant Colony Optimization.By setting a reasonable tabv range,special update rules of the pheromone and path list memory functions,the algorithm can have a better convergence,and its feasibility is proved by the experiment in an EV's demonstration operation system.展开更多
In order to expand the natural energy and the energy conservation, ＂the smart PV （photovoltaic power generation） ＆ EV （electric vehicle） system＂ has been proposed and the effect has been clarified. In the smart...In order to expand the natural energy and the energy conservation, ＂the smart PV （photovoltaic power generation） ＆ EV （electric vehicle） system＂ has been proposed and the effect has been clarified. In the smart PV ＆ EV system, it is important that electric vehicles become popular. Therefore, the AI-EV （air-conditioner integrated electric vehicle） has been proposed. In this paper, the AI-EV is designed based on the required car air-conditioner capacity. And, the value of AI-EV is compared with a gasoline vehicle, HV （hybrid vehicle） and EV using the mathematical simulation model As a result, it is clarified that the minimum displacement of the small-engine is 120 cc for AI-EV. In the smart PV ＆ EV system, AI-EV can reduce CO_2 emissions by 20% almost the same as EV. Additionally, AI-EV is able to gain the cruising range more than twice as long as EV.展开更多
The technology of production planning and scheduling is one of the critical technologies that decide whether the automated manufacturing systems can get the expected economy. Job shop scheduling belongs to the special...The technology of production planning and scheduling is one of the critical technologies that decide whether the automated manufacturing systems can get the expected economy. Job shop scheduling belongs to the special class of NP-hard problems. Most of the algorithms used to optimize this class of problems have an exponential time; that is, the computation time increases exponentially with problem size. In scheduling study, makespan is often considered as the main objective. In this paper, makespan, the due date request of the key jobs, the availability of the key machine, the average wait-time of the jobs, and the similarities between the jobs and so on are taken into account based on the application of mechanical engineering. The job shop scheduling problem with multi-objectives is analyzed and studied by using genetic algorithms based on the mechanics of genetics and natural selection. In this research, the tactics of the coding and decoding and the design of the genetic operators, along with the description of the mathematic model of the multi-objective functions, are presented. Finally an illu-strative example is given to testify the validity of this algorithm.展开更多
基金The National Key Technology R&D Program during the 11th Five-Year Plan Period of China (No. 2009BAG17B02)the National High Technology Research and Development Program of China (863 Program) (No. 2011AA110304)the National Natural Science Foundation of China (No. 50908100)
文摘In order to reduce average arterial vehicle delay, a novel distributed and coordinated traffic control algorithm is developed using the multiple agent system and the reinforce learning （RL）. The RL is used to minimize average delay of arterial vehicles by training the interaction ability between agents and exterior environments. The Robertson platoon dispersion model is embedded in the RL algorithm to precisely predict platoon movements on arteries and then the reward function is developed based on the dispersion model and delay equations cited by HCM2000. The performance of the algorithm is evaluated in a Matlab environment and comparisons between the algorithm and the conventional coordination algorithm are conducted in three different traffic load scenarios. Results show that the proposed algorithm outperforms the conventional algorithm in all the scenarios. Moreover, with the increase in saturation degree, the performance is improved more significantly. The results verify the feasibility and efficiency of the established algorithm.
基金supported by the National Natural Science Foundations of China(No. 51875171)
文摘It is urgent to effectively improve the production efficiency in the running process of manufacturing systems through a new generation of information technology.According to the current growing trend of the internet of things(IOT)in the manufacturing industry,aiming at the capacitor manufacturing plant,a multi-level architecture oriented to IOT-based manufacturing environment is established for a flexible flow-shop scheduling system.Next,according to multi-source manufacturing information driven in the manufacturing execution process,a scheduling optimization model based on the lot-streaming strategy is proposed under the framework.An improved distribution estimation algorithm is developed to obtain the optimal solution of the problem by balancing local search and global search.Finally,experiments are carried out and the results verify the feasibility and effectiveness of the proposed approach.
文摘With today＇s global economic downturn and the increasingly fierce market competition, manufacturing enterprises must guarantee the efficient operation of production system, in order to get ahead in the competition, scheduling reasonable flow shop production systems can improve productivity and equipment utilization rate, reduce production costs. So the production system of flow shop scheduling problem has become one of the core problems ofmanufactaring enterprises the use of more and more.
基金supported by the 973 Program under Grant No.2011CB302506, 2012CB315802National Key Technology Research and Development Program of China under Grant No.2012BAH94F02+5 种基金The 863 Program under Grant No.2013AA102301NNSF of China under Grant No.61132001, 61170273Program for New Century Excel-lent Talents in University under Grant No. NCET-11-0592Project of New Generation Broad band Wireless Network under Grant No.2014ZX03006003The Technology Development and Experiment of Innovative Network Architecture(CNGI-12-03-007)The Open Fund Project of CAAC InformationTechnology Research Base(CAACITRB-201201)
文摘Batteries transfer management is one important aspect in electric vehicle(EV)network's intelligent operation management system.Batteries transfer is a special and much more complex VRP(Vehicle Routing Problem) which takes the multiple constraints such as dynamic multi-depots,time windows,simultaneous pickups and deliveries,distance minimization,etc.into account.We call it VRPEVB(VRP with EV Batteries).This paper,based on the intelligent management model of EV's battery power,puts forward a battery transfer algorithm for the EV network which considers the traffic congestion that changes dynamically and uses improved Ant Colony Optimization.By setting a reasonable tabv range,special update rules of the pheromone and path list memory functions,the algorithm can have a better convergence,and its feasibility is proved by the experiment in an EV's demonstration operation system.
文摘In order to expand the natural energy and the energy conservation, ＂the smart PV （photovoltaic power generation） ＆ EV （electric vehicle） system＂ has been proposed and the effect has been clarified. In the smart PV ＆ EV system, it is important that electric vehicles become popular. Therefore, the AI-EV （air-conditioner integrated electric vehicle） has been proposed. In this paper, the AI-EV is designed based on the required car air-conditioner capacity. And, the value of AI-EV is compared with a gasoline vehicle, HV （hybrid vehicle） and EV using the mathematical simulation model As a result, it is clarified that the minimum displacement of the small-engine is 120 cc for AI-EV. In the smart PV ＆ EV system, AI-EV can reduce CO_2 emissions by 20% almost the same as EV. Additionally, AI-EV is able to gain the cruising range more than twice as long as EV.
基金Supported by National Information Industry Department (01XK310020)Shanghai Natural Science Foundation (No. 01ZF14004)
文摘The technology of production planning and scheduling is one of the critical technologies that decide whether the automated manufacturing systems can get the expected economy. Job shop scheduling belongs to the special class of NP-hard problems. Most of the algorithms used to optimize this class of problems have an exponential time; that is, the computation time increases exponentially with problem size. In scheduling study, makespan is often considered as the main objective. In this paper, makespan, the due date request of the key jobs, the availability of the key machine, the average wait-time of the jobs, and the similarities between the jobs and so on are taken into account based on the application of mechanical engineering. The job shop scheduling problem with multi-objectives is analyzed and studied by using genetic algorithms based on the mechanics of genetics and natural selection. In this research, the tactics of the coding and decoding and the design of the genetic operators, along with the description of the mathematic model of the multi-objective functions, are presented. Finally an illu-strative example is given to testify the validity of this algorithm.
