Research on Multi-Distribution Center Vehicle Routing Optimization Method Based on Improved Multi-Layer Coding Genetic Algorithm

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.

Download Full-text

Emergency logistics vehicle routing optimization based on insufficient supply

E3S Web of Conferences ◽

10.1051/e3sconf/201913604068 ◽

2019 ◽

Vol 136 ◽

pp. 04068

Author(s):

Yina Yuan ◽

Xiaoguang Zhou ◽

Mengke Yang

Keyword(s):

Vehicle Routing ◽

Optimization Method ◽

Decision Makers ◽

Emergency Vehicle ◽

Routing Problem ◽

Routing Optimization ◽

Theoretical Support ◽

Emergency Logistics ◽

The Face ◽

Vehicle Routing Optimization

In the face of various emergencies, emergency logistics vehicles are required to meet the needs of the affected areas in a short enough time. However, due to the suddenness of the incident and the shortage of relief supplies, it is necessary to further consider how to optimize the route of emergency vehicles in case of insufficient supply. In this paper, when the supply point is insufficient, the emergency vehicle routing can be optimized in the shortest possible time and at the same time to meet the requirements of the disaster site. By establishing the corresponding mathematical model and using the genetic algorithm to solve the relevant examples, the new solution is provided for the emergency logistics vehicle routing problem when the relief materials are insufficient. According to the analysis results of the example, the effectiveness of the optimization method is further demonstrated, and theoretical support is provided for relevant decision makers.

Download Full-text

The optimization of vehicle routing of communal waste in an urban environment using a nearest neighbirs' algorithm and genetic algorithm: Communal waste vehicle routing optimization in urban areas

2017 Ninth International Conference on Advanced Computational Intelligence (ICACI) ◽

10.1109/icaci.2017.7974519 ◽

2017 ◽

Author(s):

Milan Misic ◽

Aleksandar Dordevic ◽

Aleksandra Kokic Arsic

Keyword(s):

Genetic Algorithm ◽

Vehicle Routing ◽

Urban Environment ◽

Urban Areas ◽

Routing Optimization ◽

Vehicle Routing Optimization

Download Full-text

Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm

Mathematical Problems in Engineering ◽

10.1155/2018/8961505 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 6

Author(s):

Xiao-Hong Liu ◽

Mi-Yuan Shan ◽

Ren-Long Zhang ◽

Li-Hong Zhang

Keyword(s):

Vehicle Routing ◽

Carbon Emission ◽

Optimal Allocation ◽

Cloud Model ◽

Research Problem ◽

Optimization Method ◽

Immune Algorithm ◽

Practical Significance ◽

Routing Optimization ◽

Vehicle Routing Optimization

Green Vehicle Routing Optimization Problem (GVROP) is currently a scientific research problem that takes into account the environmental impact and resource efficiency. Therefore, the optimal allocation of resources and the carbon emission in GVROP are becoming more and more important. In order to improve the delivery efficiency and reduce the cost of distribution requirements through intelligent optimization method, a novel multiobjective hybrid quantum immune algorithm based on cloud model (C-HQIA) is put forward. Simultaneously, the computational results have proved that the C-HQIA is an efficient algorithm for the GVROP. We also found that the parameter optimization of the C-HQIA is related to the types of artificial intelligence algorithms. Consequently, the GVROP and the C-HQIA have important theoretical and practical significance.

Download Full-text