Improved Ant Colony Algorithm for Vehicle Scheduling Problem in Airport Ground Service Support

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.

Improved algorithms to plan missions for agile earth observation satellites

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.

Engine universal characteristic modeling based on improved ant colony optimization

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.

Winner determination problem with loss-averse buyers in reverse auctions

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.