An Ontology for Modeling Vehicle Routing Problems

2021 ◽  
Author(s):  
Syrine Belguith ◽  
Soulef Khalfallh ◽  
Ouajdi Korbaa
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Combinatorial Problem ◽  
Decision Makers ◽  
Vehicle Classification ◽  
Vehicle Routing Problems ◽  
Routing Problem ◽  
Feasible Solutions ◽  
Industrial Context

Vehicle routing problem (VRP) is a hard combinatorial problem. In practice, specificities of concepts (vehicles and networks transportation) related to VRP must be explicitly considered in modeling to obtain accurate cost and feasible solutions. Each of these concepts is represented in literature for a specific purpose. In this study, we present an ontology for modeling VRP as a unified representation based on road and vehicle classification. The approach proposed aims at providing decision-makers in transport companies a consistent understanding of the field based on ontology. Moreover, it aims at generating parameters of classification of VRPs, and at facilitating later on solving these problems, in the academic or industrial context.

scholarly journals Waste Collection Vehicle Routing Problem: Literature Review

2015 ◽  
Vol 27 (4) ◽  
pp. 345-358 ◽  
Author(s):  
Hui Han ◽  
Eva Ponce Cueto
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Research Literature ◽  
Arc Routing ◽  
Waste Collection ◽  
Logistics System ◽  
Routing Problems ◽  
Routing Problem ◽  
Node Routing

Waste generation is an issue which has caused wide public concern in modern societies, not only for the quantitative rise of the amount of waste generated, but also for the increasing complexity of some products and components. Waste collection is a highly relevant activity in the reverse logistics system and how to collect waste in an efficient way is an area that needs to be improved. This paper analyzes the major contribution about Waste Collection Vehicle Routing Problem (WCVRP) in literature. Based on a classification of waste collection (residential, commercial and industrial), firstly the key findings for these three types of waste collection are presented. Therefore, according to the model (Node Routing Problems and Arc Routing problems) used to represent WCVRP, different methods and techniques are analyzed in this paper to solve WCVRP. This paper attempts to serve as a roadmap of research literature produced in the field of WCVRP.

scholarly journals A New Plant Intelligent Behaviour Optimisation Algorithm for Solving Vehicle Routing Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Godfrey Chagwiza
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Future Research ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Routing Problem ◽  
Optimisation Algorithm ◽  
Input Parameters ◽  
Problem Instances

A new plant intelligent behaviour optimisation algorithm is developed. The algorithm is motivated by intelligent behaviour of plants and is implemented to solve benchmark vehicle routing problems of all sizes, and results were compared to those in literature. The results show that the new algorithm outperforms most of algorithms it was compared to for very large and large vehicle routing problem instances. This is attributed to the ability of the plant to use previously stored memory to respond to new problems. Future research may focus on improving input parameters so as to achieve better results.

scholarly journals ROUTING OF VEHICLES USING THE METAHEURISTIC ALGORITHM

2020 ◽  
Vol 72 (4) ◽  
pp. 225-230
Author(s):  
D. Nurserik ◽  
◽  
F.R. Gusmanova ◽  
G.А. Abdulkarimova ◽  
K.S. Dalbekova ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Input Data ◽  
Optimal Solution ◽  
Combinatorial Problem ◽  
Routing Problem ◽  
Metaheuristic Methods ◽  
Short Period ◽  
Np Complete

The main goal of the proposed research is to solve the problem of vehicle routing using genetic algorithms. Vehicle Routing Problem (VRP) is an NP-complete complex combinatorial problem. With a large amount of input data in a VRP problem, it is very expensive to find the most optimal solution. Genetic algorithms offer the most optimal solution in a short period of time. This article discusses genetic algorithms based on the mechanism of evolution for finding the optimal route by metaheuristic methods. The aim of the work is to minimize the time needed to find the most acceptable optimal solution to the problem, as well as to develop metaheuristic methods.

An Improved Genetic Algorithm for Solving Multi Depot Vehicle Routing Problems

2021 ◽  
pp. 375-402
Author(s):  
Varimna Singh ◽  
L. Ganapathy ◽  
Ashok K. Pundir
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Carrying Capacity ◽  
Vehicle Routing Problem ◽  
Np Hard ◽  
Vehicle Routing Problems ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Hybrid Clustering ◽  
The Cost

The classical Vehicle Routing Problem (VRP) tries to minimise the cost of dispatching goods from depots to customers using vehicles with limited carrying capacity. As a generalisation of the TSP, the problem is known to be NP-hard and several authors have proposed heuristics and meta-heuristics for obtaining good solutions. The authors present genetic algorithm-based approaches for solving the problem and compare the results with available results from other papers, in particular, the hybrid clustering based genetic algorithm. The authors find that the proposed methods give encouraging results on all these instances. The approach can be extended to solve multi depot VRPs with heterogeneous fleet of vehicles.

The vehicle routing problem in the dairy sector: a case study

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Marta Rinaldi ◽  
Eleonora Bottani ◽  
Federico Solari ◽  
Roberto Montanari
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Nearest Neighbor ◽  
Current System ◽  
Real Life ◽  
Combinatorial Problem ◽  
Food Sector ◽  
Routing Problem ◽  
Practical Tool

Abstract The vehicle routing problem is one of the most studied NP-hard combinatorial problem. In the food sector, the complexity of the issue grows because of the presence of strict constraints. Taking into account the variability and the restrictions typical of the dairy sector, the aim of this paper is to provide a practical tool for solving the milk collection problem in real scenarios. A heuristic approach has been proposed to determine a feasible solution for a real-life problem, including capacity and time constraints. Two different applications of the Nearest Neighbor algorithm have been modelled and compared with the current system. Different tests have been implemented for evaluating the suitability of the outcomes. Results show that the greedy approach allows for involving less vehicles and reducing the travel time. Moreover, the tool has been proved to be flexible, able to solve routing problems with stochastic times and high supply variability.

An Improved Genetic Algorithm for Solving Multi Depot Vehicle Routing Problems

2019 ◽  
Vol 12 (4) ◽  
pp. 1-26 ◽  
Author(s):  
Varimna Singh ◽  
L. Ganapathy ◽  
Ashok K. Pundir
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Carrying Capacity ◽  
Vehicle Routing Problem ◽  
Np Hard ◽  
Vehicle Routing Problems ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Hybrid Clustering ◽  
The Cost

The classical Vehicle Routing Problem (VRP) tries to minimise the cost of dispatching goods from depots to customers using vehicles with limited carrying capacity. As a generalisation of the TSP, the problem is known to be NP-hard and several authors have proposed heuristics and meta-heuristics for obtaining good solutions. The authors present genetic algorithm-based approaches for solving the problem and compare the results with available results from other papers, in particular, the hybrid clustering based genetic algorithm. The authors find that the proposed methods give encouraging results on all these instances. The approach can be extended to solve multi depot VRPs with heterogeneous fleet of vehicles.

The Research of Vehicle Routing Problem With Time Window for Changsha Yunda Express in Kaifu District

2013 ◽  
Vol 336-338 ◽  
pp. 2525-2528
Author(s):  
Zhong Liu
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Distribution Center ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Routing Problem ◽  
Service Levels ◽  
Shortest Distance

For express companies' distribution center, optimizing the vehicle routing can improve service levels and reduce logistics costs. This paper combines the present vehicle routing situation of Chang Sha Yunda Express in KaiFu area with the specific circumstances to analyze. A model of the vehicle routing problem with time window for the shortest distance was built and then use genetic algorithm to solve the problem. Its application showed that the method can effectively solve the current vehicle routing problems.

scholarly journals Heuristics for Routing Heterogeneous Unmanned Vehicles with Fuel Constraints

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
David Levy ◽  
Kaarthik Sundar ◽  
Sivakumar Rathinam
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Unmanned Vehicles ◽  
Neighborhood Search ◽  
Fast Variable ◽  
Routing Problem ◽  
Multiple Depot ◽  
Feasible Solutions ◽  
Better Than ◽  
Standard Library

This paper addresses a multiple depot, multiple unmanned vehicle routing problem with fuel constraints. The objective of the problem is to find a tour for each vehicle such that all the specified targets are visited at least once by some vehicle, the tours satisfy the fuel constraints, and the total travel cost of the vehicles is a minimum. We consider a scenario where the vehicles are allowed to refuel by visiting any of the depots or fuel stations. This is a difficult optimization problem that involves partitioning the targets among the vehicles and finding a feasible tour for each vehicle. The focus of this paper is on developing fast variable neighborhood descent (VND) and variable neighborhood search (VNS) heuristics for finding good feasible solutions for large instances of the vehicle routing problem. Simulation results are presented to corroborate the performance of the proposed heuristics on a set of 23 large instances obtained from a standard library. These results show that the proposed VND heuristic, on an average, performed better than the proposed VNS heuristic for the tested instances.

scholarly journals Multiple Ant Colony Optimization for Single Depot Multiple Trip Vehicle Routing Problems

10.29007/8tjs ◽  
2018 ◽  
Author(s):  
Zhengmao Ye ◽  
Habib Mohamadian
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Urban Areas ◽  
Ant Colony ◽  
Ant Colony System ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Routing Problem ◽  
Single Vehicle

The multiple trip vehicle routing problem involves several sequences of routes. Working shift of single vehicle can be scheduled in multiple trips. It is suitable for urban areas where the vehicle has very limited size and capacity over short travel distances. The size and capacity limit also requires the vehicle should be vacated frequently. As a result, the vehicle could be used in different trips as long as the total time or distance is not exceeded. Various approaches are developed to solve the vehicle routing problem (VRP). Except for the simplest cases, VRP is always a computationally complex issue in order to optimize the objective function in terms or both time and expense. Ant colony optimization (ACO) has been introduced to solve the vehicle routing problem. The multiple ant colony system is proposed to search for alternative trails between the source and destination so as to minimize (fuel consumption, distance, time) among numerous geographically scattered routes. The objective is to design adaptive routing so as to balance loads among congesting city networks and to be adaptable to connection failures. As the route number increases, each route becomes less densely packed. It can be viewed as the vehicle scheduling problem with capacity constraints. The proposed scheme is applied to typical cases of vehicle routing problems with a single depot and flexible trip numbers. Results show feasibility and effectiveness of the approach.

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