Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm

Route optimization of hazardous materials transportation is one of the basic steps in ensuring the safety of hazardous materials transportation. The optimization scheme may be a security risk if road screening is not completed before the distribution route is optimized. For road screening issues of hazardous materials transportation, a road screening algorithm of hazardous materials transportation is built based on genetic algorithm and Levenberg-Marquardt neural network (GA-LM-NN) by analyzing 15 attributes data of each road network section. A multi-objective robust optimization model with adjustable robustness is constructed for the hazardous materials transportation problem of single distribution center to minimize transportation risk and time. A multi-objective genetic algorithm is designed to solve the problem according to the characteristics of the model. The algorithm uses an improved strategy to complete the selection operation, applies partial matching cross shift and single ortho swap methods to complete the crossover and mutation operation, and employs an exclusive method to construct Pareto optimal solutions. Studies show that the sets of hazardous materials transportation road can be found quickly through the proposed road screening algorithm based on GA-LM-NN, whereas the distribution route Pareto solutions with different levels of robustness can be found rapidly through the proposed multi-objective robust optimization model and algorithm.

Medienart:

E-Artikel

Erscheinungsjahr:

2018

Erschienen:

2018

Enthalten in:

Zur Gesamtaufnahme - volume:13

Enthalten in:

PloS one - 13(2018), 6 vom: 22., Seite e0198931

Sprache:

Englisch

Beteiligte Personen:

Ma, Changxi [VerfasserIn]
Hao, Wei [VerfasserIn]
Pan, Fuquan [VerfasserIn]
Xiang, Wang [VerfasserIn]

Links:

Volltext

Themen:

Hazardous Substances
Journal Article

Anmerkungen:

Date Completed 31.12.2018

Date Revised 10.12.2019

published: Electronic-eCollection

Citation Status MEDLINE

doi:

10.1371/journal.pone.0198931

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM285717278