3 research outputs found
A Granular Tabu Search Algorithm for a Real Case Study of a Vehicle Routing Problem with a Heterogeneous Fleet and Time Windows
Purpose: We consider a real case study of a vehicle routing problem with a heterogeneous fleet
and time windows (HFVRPTW) for a franchise company bottling Coca-Cola products in
Colombia. This study aims to determine the routes to be performed to fulfill the demand of the
customers by using a heterogeneous fleet and considering soft time windows. The objective is to
minimize the distance traveled by the performed routes.
Design/methodology/approach: We propose a two-phase heuristic algorithm. In the
proposed approach, after an initial phase (first phase), a granular tabu search is applied during the
improvement phase (second phase). Two additional procedures are considered to help that the
algorithm could escape from local optimum, given that during a given number of iterations there
has been no improvement.
Findings: Computational experiments on real instances show that the proposed algorithm is able
to obtain high-quality solutions within a short computing time compared to the results found by
the software that the company currently uses to plan the daily routes.
Originality/value: We propose a novel metaheuristic algorithm for solving a real routing
problem by considering heterogeneous fleet and time windows. The efficiency of the proposed approach has been tested on real instances, and the computational experiments shown its
applicability and performance for solving NP-Hard Problems related with routing problems with
similar characteristics. The proposed algorithm was able to improve some of the current solutions
applied by the company by reducing the route length and the number of vehicles.Peer Reviewe
Solving Area Coverage Problem with UAVs: A Vehicle Routing with Time Windows Variation
In real life, providing security for a set of large areas by covering the
area with Unmanned Aerial Vehicles (UAVs) is a difficult problem that consist
of multiple objectives. These difficulties are even greater if the area
coverage must continue throughout a specific time window. We address this by
considering a Vehicle Routing Problem with Time Windows (VRPTW) variation in
which capacity of agents is one and each customer (target area) must be
supplied with more than one vehicles simultaneously without violating time
windows. In this problem, our aim is to find a way to cover all areas with the
necessary number of UAVs during the time windows, minimize the total distance
traveled, and provide a fast solution by satisfying the additional constraint
that each agent has limited fuel. We present a novel algorithm that relies on
clustering the target areas according to their time windows, and then
incrementally generating transportation problems with each cluster and the
ready UAVs. Then we solve transportation problems with the simplex algorithm to
generate the solution. The performance of the proposed algorithm and other
implemented algorithms to compare the solution quality is evaluated on example
scenarios with practical problem sizes