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

• Published in: PloS one Vol. 13; no. 6; p. e0198931
• Main Authors: Ma, Changxi, Hao, Wei, Pan, Fuquan, Xiang, Wang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 21.06.2018; Public Library of Science (PLoS)
• Subjects: Algorithms; Automobile safety; Biology and Life Sciences; Chemical transportation; Computer and Information Sciences; Construction materials; Data processing; Decision making; Design; Dynamic programming; Engineering; Engineering and Technology; Fatalities; Federal regulation; Genetic algorithms; Hazardous materials; Hazardous Substances; Heuristic; Humans; Laboratories; Materials handling; Mathematical models; Medical screening; Medicine and Health Sciences; Multiple objective analysis; Mutation; Network management systems; Neural networks; Neural Networks (Computer); Operations research; Optimization; Optimization theory; Physical Sciences; Research and Analysis Methods; Roads; Roads & highways; Robustness; Route choice; Route optimization; Security; Transportation; Transportation planning; Transportation problem; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0198931

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.