Title :
An Improved Genetic Algorithm of Vehicle Scheduling Problems for Military Logistic Distribution
Author_Institution :
Automobile Manage. Inst., Bengbu, China
Abstract :
This paper is aimed to research into military vehicle scheduling problems by means of Genetic Algorithm. By converting the constrain conditions of delivery time windows and vehicle capacity constrains into penalty function of objective function, the paper built up a vehicle scheduling model based on minimum length of total transportation distance. It analyzed characteristics and application prospects of the model. It put forward a improved Genetic Algorithm program to solve the model. In the algorithm program it designed a chromosome coding to describe delivery routes, proposed a fitness function and constructed a reproduction operator, a crossover operator and a mutation operator to do optimization operation. Finally it provided an example to demonstrate feasibility of the algorithm. The study indicates that the improved Genetic Algorithm has higher algorithm efficiency and can effectively solve vehicle scheduling problems of military distribution centers.
Keywords :
genetic algorithms; logistics; military vehicles; scheduling; transportation; algorithm feasibility; chromosome coding; crossover operator; delivery time windows; fitness function; improved genetic algorithm program; military distribution centers; military logistic distribution; military vehicle scheduling problems; mutation operator; objective function penalty function; optimization operation; reproduction operator; transportation distance; vehicle capacity constrains; Algorithm design and analysis; Biological cells; Encoding; Genetic algorithms; Optimization; Scheduling; Vehicles; Genetic Algorithm; Military Logistics; Physical Distribution; Vehicle Scheduling Problem;
Conference_Titel :
Intelligent System Design and Engineering Applications (ISDEA), 2013 Third International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4673-4893-5
DOI :
10.1109/ISDEA.2012.71