scholarly journals Selected Genetic Algorithms for Vehicle Routing Problem Solving

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3147
Author(s):  
Joanna Ochelska-Mierzejewska ◽  
Aneta Poniszewska-Marańda ◽  
Witold Marańda
Keyword(s):  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Real Life ◽  
Economic Benefits ◽  
Added Value ◽  
Genetic Operators ◽  
Routing Problem ◽  
Starting Point

The traveling salesman problem (TSP) consists of finding the shortest way between cities, which passes through all cities and returns to the starting point, given the distance between cities. The Vehicle Routing Problem (VRP) is the issue of defining the assumptions and limitations in mapping routes for vehicles performing certain operational activities. It is a major problem in logistics transportation. In specific areas of business, where transportation can be perceived as added value to the product, it is estimated that its optimization can lower costs up to 25% in total. The economic benefits for more open markets are a key point for VRP. This paper discusses the metaheuristics usage for solving the vehicle routing problem with special attention toward Genetic Algorithms (GAs). Metaheuristic algorithms are selected to solve the vehicle routing problem, where GA is implemented as our primary metaheuristic algorithm. GA belongs to the evolutionary algorithm (EA) family, which works on a “survival of the fittest” mechanism. This paper presents the idea of implementing different genetic operators, modified for usage with the VRP, and performs experiments to determine the best combination of genetic operators for solving the VRP and to find optimal solutions for large-scale real-life examples of the VRP.

APPLICATION OF GENETIC ALGORITHMS TO A LARGE-SCALE MULTIPLE-CONSTRAINT VEHICLE ROUTING PROBLEM

2003 ◽  
Vol 03 (01) ◽  
pp. 1-21 ◽  
Author(s):  
GEORGE MOURKOUSIS ◽  
MATHEW PROTONOTARIOS ◽  
THEODORA VARVARIGOU
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
High Performance ◽  
Large Scale ◽  
Hybrid Genetic Algorithm ◽  
Real Life ◽  
Search Space ◽  
Genetic Operators ◽  
Actual Problem ◽  
Routing Problem

This paper presents a study on the application of a hybrid genetic algorithm (HGA) to an extended instance of the Vehicle Routing Problem. The actual problem is a complex real-life vehicle routing problem regarding the distribution of products to customers. A non homogenous fleet of vehicles with limited capacity and allowed travel time is available to satisfy the stochastic demand of a set of different types of customers with earliest and latest time for servicing. The objective is to minimize distribution costs respecting the imposed constraints (vehicle capacity, customer time windows, driver working hours and so on). The approach for solving the problem was based on a "cluster and route" HGA. Several genetic operators, selection and replacement methods were tested until the HGA became efficient for optimization of a multi-extrema search space system (multi-modal optimization). Finally, High Performance Computing (HPC) has been applied in order to provide near-optimal solutions in a sensible amount of time.

scholarly journals Optimization of Vehicle Routing Problem with Time Window (VRPTW) for Food Product Distribution Using Genetics Algorithm

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Rayandra Yala Pratama ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithms ◽  
Population Size ◽  
Vehicle Routing ◽  
Mutation Rate ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Food Distribution ◽  
Genetic Operators ◽  
Routing Problem ◽  
Crossover And Mutation

Food distribution process is very important task because the product can expire during distribution and the further the distance the greater the cost. Determining the route will be more difficult if all customers have their own time to be visited. This problem is known as the Vehicle Routing Problem with Time Windows (VRPTW). VRPTW problems can be solved using genetic algorithms because genetic algorithms generate multiple solutions at once. Genetic algorithms generate chromosomes from serial numbers that represent the customer number to visit. These chromosomes are used in the calculation process together with other genetic operators such as population size, number of generations, crossover and mutation rate. The results show that the best population size is 300, 3,000 generations, the combination of crossover and mutation rate is 0.4:0.6 and the best selection method is elitist selection. Using a data test, the best parameters give a good solution that minimize the distribution route.

Large-Scale Vehicle Routing Problem with Massive Precedence and Cluster Constraints

2022 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Simin Huang ◽  
Yongzhong Wu ◽  
Yu Chen
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Routing Problem

scholarly journals POPMUSIC for a real-world large-scale vehicle routing problem with time windows

2009 ◽  
Vol 60 (7) ◽  
pp. 934-943 ◽  
Author(s):  
A Ostertag ◽  
K F Doerner ◽  
R F Hartl ◽  
E D Taillard ◽  
P Waelti
Keyword(s):  
Vehicle Routing ◽  
Real World ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Time Windows ◽  
Routing Problem

A Hybrid Metaheuristic for a Real Life Vehicle Routing Problem

2007 ◽  
pp. 247-254 ◽  
Author(s):  
Panagiotis P. Repoussis ◽  
Christos D. Tarantilis ◽  
George Ioannou
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Real Life ◽  
Routing Problem ◽  
Hybrid Metaheuristic

scholarly journals Multidepot Heterogeneous Vehicle Routing Problem for a Variety of Hazardous Materials with Risk Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Bochen Wang ◽  
Qiyuan Qian ◽  
Zheyi Tan ◽  
Peng Zhang ◽  
Aizhi Wu ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Programming Model ◽  
Hazardous Materials ◽  
Mixed Integer ◽  
Small Scale ◽  
Routing Problem ◽  
Heterogeneous Vehicle Routing Problem

This study investigates a multidepot heterogeneous vehicle routing problem for a variety of hazardous materials with risk analysis, which is a practical problem in the actual industrial field. The objective of the problem is to design a series of routes that minimize the total cost composed of transportation cost, risk cost, and overtime work cost. Comprehensive consideration of factors such as transportation costs, multiple depots, heterogeneous vehicles, risks, and multiple accident scenarios is involved in our study. The problem is defined as a mixed integer programming model. A bidirectional tuning heuristic algorithm and particle swarm optimization algorithm are developed to solve the problem of different scales of instances. Computational results are competitive such that our algorithm can obtain effective results in small-scale instances and show great efficiency in large-scale instances with 70 customers, 30 vehicles, and 3 types of hazardous materials.

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