The Research On Vehicle Scheduling Problem Based On Intelligent Optimization Algorithm And Statistical Partition

Vehicle scheduling problem has been a hot issue and it is a typical NP hardproblem. With a variety of intelligent optimization algorithms applied, such assimulated annealing algorithm, ant colony algorithm, genetic algorithms andcontinuous improvements for intelligent optimization algorithms, all of those providethe solution to the vehicle scheduling problem a wider means. In any case, thetraditional vehicle scheduling problem has been studied in the pursuit of a moreaccurate optimal solution, and the speed and efficiency of the Vehicle schedulingproblem has been continuously improved. In recent years, the research on vehiclescheduling problem has been closer to the reality and the actual direction, rather thanjust confined to the vehicle scheduling theory. For sake of this, the researchers haveadded restrictions to the vehicle scheduling problem.These constraints include thetime window, unit cost distance function, load rate of vehicles, even a reasonableworking hours of drivers to be considered. Such research does provide more specificsupport for the theory to solve practical problems, and this also makes a geometricincrease in the scale of the problem. Combined with the size of e-commerce, the rapidrise of mobile Internet, the logistics industry is also a geometric increase. These havegreatly expanded the breadth and depth of the vehicle scheduling problem. In order toreduce the size of the vehicle scheduling problem, methods about statistical partitionare involved in few articles. And studies have showed that vehicle schedulingproblems between different regions have weak correlation in fact. This indicates thatthe vehicle scheduling partition is feasible. This paper attempts to partition therunning node of vehicle scheduling from the view of the statistical partition to reducethe scale of the problem, then use the intelligent algorithm to solve sub-region vehiclescheduling problem.