Abstract:
In this thesis, Evolutionary Multi-objective Optimization Algorithm for
solving Fuzzy Vehicle Routing Problem (FVRP) is described. FVRP is an extension
of VRP with Time Windows, which is one of the variants of VRP. In addition to
FVRP, Multiple Depot VRP (MDVRP) is used in solving the problem. So, the
proposed work and the solution approach is a Fuzzy Multiple Depot VRP
(FMDVRP). The objectives that are to be optimized in this solution approach are the
minimization of: total travelled distance by vehicles, waiting time of vehicles and
customers, and maximization of: load capacity of vehicles and service satisfaction of
customers.
NSGA-II is a multi-objective optimization algorithm that is used for
problems with several objectives to be optimized. In NSGA-II, there is population,
which is initialized randomly, and then through several generations a new population
is generated from the previous one, and the best of these populations are chosen. The
typical genetic operators are applied for generating new population. In addition,
NSGA-II uses a new parameter called crowding distance, which is used for better
divergence.
In experimental results, benchmark problem instances classified by
geographical distribution of customers are used in order to compare the results
obtained with others. From the results, it is observed that the proposed solution
minimizes the waiting time of vehicles by 30% more than the proposed solutions of
other researchers.
Description:
Master of Science in Computer Engineering. Thesis (M.S.)--Eastern Mediterranean University, Faculty of Engineering, Dept. of Computer Engineering, 2011. Supervisor: Assist. Prof. Dr. Ahmet Ünveren.
