Effective EV Population Seeding Technique For Vehicle Routing Problem With Time Windows Using Genetic Algorithm
The Vehicle Routing Problem with Time Windows (VRPTW) consist of a fleet of vehicles and a set of
customer/cities in which all the vehicles will start and end in the depot. The objective is to minimize the vehicle fleet and the
sum of travel time. Genetic Algorithm (GA) is a heuristic method that uses genetics as a model for solving complex problems.
It’s a search technique to find approximate solutions into optimization. In GA, random population initialization technique is
efficient but is takes more computational time for convergence in most of the instances. This gives a motivation to propose a
novel Ordered Distance Vector (ODV) based Equi-begin with Variable-diversity (EV) technique to solve the problem. The
convergence solution generated by EV technique is compared with Random seeding technique and we found that the
performance of EV shows better than the Random. The computational results a r e carried out using the VRPTW
benchmark instances obtained from the VRPLIB were experimented using MATLAB shows that the proposed EV technique
outperforms for the most of the instances compared with the existing population initialization method in terms of
Keywords—Population Seeding Technique, Vehicle Routing Problem with Time Windows, Combinatorial Problem,
Ordered Distance Vector, EV Technique, Performance Analysis.