A Centroid-based Heuristic Algorithm for the Capacitated Vehicle Routing Problem

The vehicle routing problem (VRP) is famous as a nondeterministic polynomial-time hard problem. This study proposes a centroid-based heuristic algorithm to solve the capacitated VRP in polynomial time. The proposed algorithm consists of three phases: cluster construction, cluster adjustment, and route establishment. At the cluster construction phase, the farthest node (customer) among un-clustered nodes is selected as a seed to form a cluster. The notion of the geometrical centre of a cluster is introduced in this study to be utilized at the cluster construction and the cluster adjustment phases. The proposed algorithm has a polynomial time complexity of O(n2.2). Experimental results on Augerat benchmark dataset show that the proposed 3-phase approach can result in smaller distances than the Sweep algorithm in more cases

Efficient heuristic algorithms for location of charging stations in electric vehicle routing problems

Indexación: Scopus.This work has been partially supported by CONICYT FONDECYT by grant 11150370, FONDEF IT17M10012 and the “Grupo de Logística y Transporte” at the Universidad del Bío-Bío.. This support is gratefully acknowledged.Eco-responsible transportation contributes at making a difference for companies devoted to product delivery operations. Two specific problems related to operations are the location of charging stations and the routing of electric vehicles. The first one involves locating new facilities on potential sites to minimise an objective function related to fixed and operational opening costs. The other one, electric vehicle routing problem, involves the consolidation of an electric-type fleet in order to meet a particular demand and some guidelines to optimise costs. It is determined by the distance travelled, considering the limited autonomy of the fleet, and can be restored by recharging its battery. The literature provides several solutions for locating and routing problems and contemplates restrictions that are closer to reality. However, there is an evident lack of techniques that addresses both issues simultaneously. The present article offers four solution strategies for the location of charging stations and a heuristic solution for fleet routing. The best results were obtained by applying the location strategy at the site of the client (relaxation of the VRP) to address the routing problem, but it must be considered that there are no displacements towards the recharges. Of all the other three proposals, K-means showed the best performance when locating the charging stations at the centroid of the cluster. © 2012-2018. National Institute for R and D in Informatics.https://sic.ici.ro/wp-content/uploads/2018/03/Art.-8-Issue-1-2018-SIC.pd

Petal-shaped clustering for the capacitated vehicle routing problem

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, in partial fulfillment of the requirements for the degree of Master of Science in Engineering. Johannesburg, February 2018In this research report, k-medoid (petal-shaped) clustering is modelled and evaluated for the Capacitated Vehicle Routing Problem (CVRP). To determine routes, an existing metaheuristic, termed the Ruin and Recreate method, is applied to each generated cluster. Results are benchmarked to that of a well-known clustering method, k-means clustering. The performance of the methods is measured in terms of travel cost and distance travelled, which are well-known metrics for Vehicle Routing Problems (VRPs). The results show that k-medoid outperforms the benchmark method for most instances of the test datasets, although the CVRP without any predefined clusters still provide solutions that are closer to optimal. Clustering remains a reliable distribution management tool and reduces processing requirements of large scale CVRPs.MT 201

A robust solving strategy for the vehicle routing problem with multiple depots and multiple objectives

This document presents the development of a robust solving strategy for the Vehicle Routing Problem with Multiple Depots and Multiple Objectives (MO-MDVRP). The problem tackeled in this work is the problem to minimize the total cost and the load imbalance in vehicle routing plan for distribution of goods. This thesis presents a MILP mathematical model and a solution strategy based on a Hybrid Multi- Objective Scatter Search Algorithm. Several experiments using simulated instances were run proving that the proposed method is quite robust, this is shown in execution times (less than 4 minutes for an instance with 8 depots and 300 customers); also, the proposed method showed good results compared to the results found with the MILP model for small instances (up to 20 clients and 2 depots).MaestríaMagister en Ingeniería Industria

A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers

We propose a ridesharing strategy with integrated transit in which a private on-demand mobility service operator may drop off a passenger directly door-to-door, commit to dropping them at a transit station or picking up from a transit station, or to both pickup and drop off at two different stations with different vehicles. We study the effectiveness of online solution algorithms for this proposed strategy. Queueing-theoretic vehicle dispatch and idle vehicle relocation algorithms are customized for the problem. Several experiments are conducted first with a synthetic instance to design and test the effectiveness of this integrated solution method, the influence of different model parameters, and measure the benefit of such cooperation. Results suggest that rideshare vehicle travel time can drop by 40-60% consistently while passenger journey times can be reduced by 50-60% when demand is high. A case study of Long Island commuters to New York City (NYC) suggests having the proposed operating strategy can substantially cut user journey times and operating costs by up to 54% and 60% each for a range of 10-30 taxis initiated per zone. This result shows that there are settings where such service is highly warranted

A new method for generating initial solutions of capacitated vehicle routing problems

In vehicle routing problems, the initial solutions of the routes are important for improving the quality and solution time of the algorithm. For a better route construction algorithm, the obtained initial solutions must be basic, fast, and flexible with reasonable accuracy. In this study, initial solutions improvement for CVRP is introduced based on a method that is introduced in the literature. Using a different formula for addressing the gravitational forces, a new method is introduced and compared with the previous physics inspired algorithm. By using the initial solutions of the proposed method and using them as RTR and SA initial routes, it is seen that better results are obtained when compared with various algorithms from the literature. Also, in order to fairly compare the algorithms executed on different machines, a new comparison scale for the solution quality of vehicle routing problems is proposed that depends on the solution time and the deviation from the best known solution. The obtained initial solutions are then input to Record-to-Record and Simulated Annealing algorithms to obtain final solutions. Various test instances and CVRP solutions from the literature are used for comparison. The comparisons with the proposed method have shown promising results