scholarly journals Vehicle-Routing Optimization for Municipal Solid Waste Collection Using Genetic Algorithm: The Case of Southern Nablus City

2017 ◽  
Vol 26 (3) ◽  
pp. 43-57 ◽  
Author(s):  
Ramiz Assaf ◽  
Yahya Saleh
Keyword(s):  
Genetic Algorithm ◽  
Solid Waste ◽  
Vehicle Routing ◽  
Service Level ◽  
Current Situation ◽  
Waste Collection ◽  
Solid Waste Collection ◽  
Routing Problem ◽  
Routing Optimization ◽  
Vehicle Routing Optimization

Abstract Municipalities are responsible for solid waste collectiont for environmental, social and economic purposes. Practices of municipalities should be effective and efficient, with the objectives of reducing the total incurred costs in the solid waste collection network concurrently achieving the highest service level. This study aims at finding the best routes of solid waste collection network in Nablus city-Palestine. More specifically, the study seeks the optimal route that minimizes the total travelled distance by the trucks and hence the resulted costs. The current situation is evaluated and the problem is modelled as a Vehicle Routing Problem (VRP). The VRP is then optimized via a genetic algorithm. Specifically, compared to the current situation, the trucks total travelled distance was reduced by 66%, whereas the collection time was reduced from 7 hours per truck-trip to 2.3 hours. The findings of this study is useful for all municipality policy makers that are responsible for solid waste collection.

scholarly journals Vehicle Routing Optimization System with Smart Geopositioning Updates

2021 ◽  
Vol 11 (22) ◽  
pp. 10933
Author(s):  
Radosław Belka ◽  
Mateusz Godlewski
Keyword(s):  
Vehicle Routing ◽  
Optimization Algorithms ◽  
Real Life ◽  
Distance Matrix ◽  
Gps Tracking ◽  
Routing Problem ◽  
Optimization System ◽  
Routing Optimization ◽  
Vehicle Routing Optimization ◽  
The Impact

Solving the vehicle routing problem (VRP) is one of the best-known optimization issues in the TLS (transport, logistic, spedition) branch market. Various variants of the VRP problem have been presented and discussed in the literature for many years. In most cases, batch versions of the problem are considered, wherein the complete data, including customers’ geographical distribution, is well known. In real-life situations, the data change dynamically, which influences the decisions made by optimization systems. The article focuses on the aspect of geopositioning updates and their impact on the effectiveness of optimization algorithms. Such updates affect the distance matrix, one of the critical datasets used to optimize the VRP problem. A demonstration version of the optimization system was developed, wherein updates are carried out in integration with both open source routing machine and GPS tracking services. In the case of a dynamically changing list of destinations, continuous and effective updates are required. Firstly, temporary values of the distance matrix based on the correction of the quasi-Euclidean distance were generated. Next, the impact of update progress on the proposed optimization algorithms was investigated. The simulation results were compared with the results obtained “manually” by experienced planners. It was found that the upload level of the distance matrix influences the optimization effectiveness in a non-deterministic way. It was concluded that updating data should start from the smallest values in the distance matrix.

A Modified Kruskal's Algorithm to Improve Genetic Search for Open Vehicle Routing Problem

2021 ◽  
pp. 403-425
Author(s):  
Joydeep Dutta ◽  
Partha Sarathi Barma ◽  
Samarjit Kar ◽  
Tanmay De
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Central Point ◽  
Routing Problem ◽  
Genetic Search ◽  
Routing Optimization ◽  
Open Vehicle Routing Problem ◽  
Open Vehicle Routing

This article has proposed a modified Kruskal's method to increase the efficiency of a genetic algorithm to determine the path of least distance starting from a central point to solve the open vehicle routing problem. In a vehicle routing problem, vehicles start from a central point and several customers placed in different locations to serve their demands and return to the central point. In the case of the open vehicle routing problem, the vehicles do not go back to the central point after serving the customers. The challenge is to reduce the number of vehicles used and the distance travelled simultaneously. The proposed method applies genetic algorithms to find the set of customers those are covered by a particular vehicle and the authors have applied the proposed modified Kruskal's method for local routing optimization. The results of the new method are analyzed in comparison with some of the evolutionary methods.

Sign in / Sign up

Export Citation Format

Share Document