Abstract:
Vehicle routing problem (VRP) is one of the important and widely researched topics in the
fields of operation research, distribution, transportation and logistics. Various variants of
VRP exist in literature, each formulated based on the nature of the problem and type of
constraints i.e., time, cost, capacity, distance under consideration. Capacitated Vehicle
Routing Problem (CVRP) is one such variant where the objective is to provide optimal
routes to a set of vehicles so that the combined cost or distance travelled by all vehicles is
as minimum as possible while serving all demand points under the constraints of vehicles’
capacity. This research aims to solve a variant CVRP for a dairy factory that has several
area offices. Each area office owns a fixed heterogeneous fleet and is responsible for
collecting raw milk from its respective Milk collection Centers (MCC). The objective of
this research is to minimize overall cost of milk collection by determining optimum routes
for area offices’ fleet considering their fuel economies and capacities. This research uses a
popular, two-phased approach i.e., clustering first and then solving TSP, for solving this
variant of CVRP. It uses a modified version of sweep algorithm in combination with nested
hybrid genetic for assigning suitable MCCs to vehicles and solving TSP for each vehicle.
The results show that the vehicles are utilized at their maximum capacities i.e., more than
90%, and improved Genetic Algorithm (GA) provides better and faster results than normal
GA. The GA on the top of sweep and improved GA provides optimum routes for vehicles
with significant drop in the overall cost.
