Adaptive Genetic Artificial Bee Colony Algorithm And Its Application On The Path Optimization Of The Container Trucks In Terminals

The rapid development of global economy and trade brings a sharp growth of the logistics volume.According to statistics,more than 90%goods of international trade are transported through the port,and most of which are transported by container transportation.In order to improve their economic benefit,container terminal must make full use of various resources and equipments.Among those equipments,container truck undertakes tasks of horizontal transportation in most container terminal.How to organize the truck is one of the core techno--logy which affects the efficiency of the whole terminal.The route optimization of the truck which is an important part of truck organization is a typical complicated combinatorial optimization problem.The optimizing on the problem could effectively improve the efficiency of container truck,and reduce the cost of container truck operation,that leads to the improvement on economic benefit of the terminal.Aiming at the problem of container truck route optimization,we analyzed two kinds of container truck operation mode,then set up a container truck operation model based on cost that faced the working face.This problem was often optimized using mathematical programming which has defects like local convergence and poor robustness.Here,we adopt a novel Artificial Bee Colony(ABC)to solve the problem,looking forward that we can have a better result.Artificial Bee Colony is an algorithm whose structure is simple and easy to implement,but as a group of swarm intelligent algorithm,it still has shortcomings such as easily premature and slowly convergence speed.In the meantime,ABC was mostly used in continuous problem in the past research and applications.In order to use ABC on solving the problem of container truck optimization above which belongs to discrete problem,some improvements are used on ABC in this paper,then an algorithm named AGA-ABC is proposed.Its main idea is to introduce the operator of crossover and mutation of GA into ABC to modify ABC,makes the ABC could solve discrete problem better.Then,the adaptive factor is introduced which makes the algorithm could effectively avoid premature in early days and speed up convergence to the global optimal point,thus improving the overall performance of the algorithm.To verify the performance of the proposed algorithm,using the algorithm to solving differ-rent sizes of classic TSP problems,the results verified the feasibility and superiority.Further,the proposed algorithm is used on the model of container truck route optimization which has been set,simulated test respectively in two working conditions,one is two ships which one is import ship and one is export ship arrive the port,the other is one ship that do both import and export arrives the port.Results shows that the proposed algorithm can effectively optimize the container truck route problem when satisfying various of constraints,and satisfactory engineering optimization results of proposed problem could be got.Work shows that the proposed algorithm is effective to the container truck route optimization problem.The superior optimization result could be got and used as a reference to the actual container truck operation organization.The research work in this paper has certain application value and theoretical significance.