Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for...Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for current operational demands is proposed to study optimization algorithms for vehicle scheduling.The model is based on the constraint relationship of the initial operation time,time window,and gate position distribution,which gives an improvement to the ant colony algorithm(ACO).The impacts of the improved ACO as used for support vehicle optimization are compared and analyzed.The results show that the scheduling scheme of refueling trucks based on the improved ACO can reduce flight delays caused by refueling operations by 56.87%,indicating the improved ACO can improve support vehicle scheduling.Besides,the improved ACO can jump out of local optima,which can balance the working time of refueling trucks.This research optimizes the scheduling scheme of support vehicles under the existing conditions of airports,which has practical significance to fully utilize ground service resources,improve the efficiency of airport ground operations,and effectively reduce flight delays caused by ground service support.展开更多
This study concentrates of the new generation of the agile (AEOS). AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satell...This study concentrates of the new generation of the agile (AEOS). AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.展开更多
There have been some mathematics methods to model farm vehicle engine universal characteristic mapping(EUCM).Nevertheless,any of different mathematics methods used would possess its own strengths and weaknesses.As a r...There have been some mathematics methods to model farm vehicle engine universal characteristic mapping(EUCM).Nevertheless,any of different mathematics methods used would possess its own strengths and weaknesses.As a result,these modeling methods about EUCM are not the same among the most vehicle manufacturers.In order to obtain a better robustness EUCM,an improved ant colony optimization was introduced into a traditional cubic surface regression method for modeling EUCM.Based on this method,the test data were regressed into a three-dimensional cubic surface,after that it was cut by some equal specific fuel consumption(ESFC)planes,more than twenty two-dimensional ESFC equations were obtained.Furthermore,the engine speed in every ESFC equation was discretized to obtain a set of ESFC points,and this set of ESFC points was linked into a closed curve by a given sequence via the improved ant colony algorithm.In order to improve the modeling speed,dimensionality reduction and discretization methods were adopted.In addition,a corresponding simulation platform was also developed to obtain an optimal system configuration.There were 48000 simulation search tests carried out on the platform,and the major parameters of the algorithm were determined.In this way the EUCM was established successfully.In contrast with other methods,as a result of the application of the novel bionic intelligent algorithm,it has better robustness,less distortion and higher calculating speed,and it is available for both gasoline engines and diesel engines.展开更多
Reverse auctions have been widely adopted for purchasing goods and services. This paper considers a novel winner determination problem in a multiple-object reverse auction in which the buyer involves loss-averse behav...Reverse auctions have been widely adopted for purchasing goods and services. This paper considers a novel winner determination problem in a multiple-object reverse auction in which the buyer involves loss-averse behavior due to uncertain attributes. A corresponding winner determination model based on cumulative prospect theory is proposed. Due to the NP-hard characteristic, a loaded route strategy is proposed to ensure the feasibility of the model. Then, an improved ant colony algorithm that consists of a dynamic transition strategy and a Max-Min pheromone strategy is designed. Numerical experiments are conducted to illustrate the effectiveness of the proposed model and algorithm. We find that under the loaded route strategy, the improved ant colony algorithm performs better than the basic ant colony algorithm. In addition, the proposed model can effectively characterize the buyer's loss-averse behavior.展开更多
基金the Science and Technology Cooperation Research and Development Project of Sichuan Provincial Academy and University(Grant No.2019YFSY0024)the Key Research and Development Program in Sichuan Province of China(Grant No.2019YFG0050)the Natural Science Foundation of Guangxi Province of China(Grant No.AD19245021).
文摘Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for current operational demands is proposed to study optimization algorithms for vehicle scheduling.The model is based on the constraint relationship of the initial operation time,time window,and gate position distribution,which gives an improvement to the ant colony algorithm(ACO).The impacts of the improved ACO as used for support vehicle optimization are compared and analyzed.The results show that the scheduling scheme of refueling trucks based on the improved ACO can reduce flight delays caused by refueling operations by 56.87%,indicating the improved ACO can improve support vehicle scheduling.Besides,the improved ACO can jump out of local optima,which can balance the working time of refueling trucks.This research optimizes the scheduling scheme of support vehicles under the existing conditions of airports,which has practical significance to fully utilize ground service resources,improve the efficiency of airport ground operations,and effectively reduce flight delays caused by ground service support.
基金supported by the National Natural Science Foundation of China(7127106671171065+1 种基金71202168)the Natural Science Foundation of Heilongjiang Province(GC13D506)
文摘This study concentrates of the new generation of the agile (AEOS). AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.
基金The authors thank the financial and experimental support from FAW(First Auto Works),especially thank the contribution of Cheng Xuejun,Nie Xixian,Ye Wenhui and Chen Yuchun.
文摘There have been some mathematics methods to model farm vehicle engine universal characteristic mapping(EUCM).Nevertheless,any of different mathematics methods used would possess its own strengths and weaknesses.As a result,these modeling methods about EUCM are not the same among the most vehicle manufacturers.In order to obtain a better robustness EUCM,an improved ant colony optimization was introduced into a traditional cubic surface regression method for modeling EUCM.Based on this method,the test data were regressed into a three-dimensional cubic surface,after that it was cut by some equal specific fuel consumption(ESFC)planes,more than twenty two-dimensional ESFC equations were obtained.Furthermore,the engine speed in every ESFC equation was discretized to obtain a set of ESFC points,and this set of ESFC points was linked into a closed curve by a given sequence via the improved ant colony algorithm.In order to improve the modeling speed,dimensionality reduction and discretization methods were adopted.In addition,a corresponding simulation platform was also developed to obtain an optimal system configuration.There were 48000 simulation search tests carried out on the platform,and the major parameters of the algorithm were determined.In this way the EUCM was established successfully.In contrast with other methods,as a result of the application of the novel bionic intelligent algorithm,it has better robustness,less distortion and higher calculating speed,and it is available for both gasoline engines and diesel engines.
基金sponsored by the Distinguished Young Scholars Award of NSFC Grant #71325002the Major International Joint Research Project of NSFC Grant #71620107003+2 种基金the Foundation for Innovative Research Groups of NSFC Grant #61621004111 Project Grant #B16009the Fundamental Research Funds for State Key Laboratory of Synthetical Automation for Process Industries Grant #2013ZCX11
文摘Reverse auctions have been widely adopted for purchasing goods and services. This paper considers a novel winner determination problem in a multiple-object reverse auction in which the buyer involves loss-averse behavior due to uncertain attributes. A corresponding winner determination model based on cumulative prospect theory is proposed. Due to the NP-hard characteristic, a loaded route strategy is proposed to ensure the feasibility of the model. Then, an improved ant colony algorithm that consists of a dynamic transition strategy and a Max-Min pheromone strategy is designed. Numerical experiments are conducted to illustrate the effectiveness of the proposed model and algorithm. We find that under the loaded route strategy, the improved ant colony algorithm performs better than the basic ant colony algorithm. In addition, the proposed model can effectively characterize the buyer's loss-averse behavior.