New optimisation model and fuzzy adaptive weighted genetic algorithm for hazardous material transportation

Authors:
Changxi Ma;Yinzhen Li;Ruichun He;Gang Duan;Li Sun;Bo Qi
Affiliations:
School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, China;School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, China;School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, China;School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, China;School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, China;School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, China
Venue:
International Journal of Computing Science and Mathematics
Year:
2012

Citing 8
Cited 9

A Maritime Global Route Planning Model for Hazardous Materials Transportation

Transportation Science
Designing a Road Network for Hazardous Materials Transportation

Transportation Science
Solving the hazmat transport network design problem

Computers and Operations Research
Unbalanced supply chain design using the analytic network process and a hybrid heuristic-based algorithm with balance modulating mechanism

International Journal of Bio-Inspired Computation
Simulated annealing-based particle swarm optimisation with adaptive jump strategy for modelling of dynamic cerebral pressure autoregulation mechanism

International Journal of Bio-Inspired Computation
Optimal coverage configuration with social emotional optimisation algorithm in wireless sensor networks

International Journal of Wireless and Mobile Computing
Research on location problem of emergency service facilities based on genetic-simulated annealing algorithm

International Journal of Wireless and Mobile Computing
An Adaptive Tradeoff Model for Constrained Evolutionary Optimization

IEEE Transactions on Evolutionary Computation

Study on multi-objective travelling salesman problem for hazardous materials transportation based on improved genetic algorithm

International Journal of Computing Science and Mathematics
Modelling and solving for ready-mixed concrete scheduling problems with time dependence

International Journal of Computing Science and Mathematics
Route optimisation models and algorithms for hazardous materials transportation under different environments

International Journal of Bio-Inspired Computation
Study on the hazardous materials' vehicle scheduling route based on uncertain operator

International Journal of Wireless and Mobile Computing
Parallel ant colony optimisation algorithm for continuous domains on graphics processing unit

International Journal of Computing Science and Mathematics
Study on urban three-lane mixed traffic flow with buses based on the Nagel-Schreckenberg model

International Journal of Wireless and Mobile Computing
Cultural quantum-inspired shuffled frog leaping algorithm for direction finding of non-circular signals

International Journal of Computing Science and Mathematics
Quantum particle swarm optimisation based on chaotic mutation for automatic parameters determination of pulse coupled neural network

International Journal of Computing Science and Mathematics
Research on flatness errors evaluation based on artificial fish swarm algorithm and Powell method

International Journal of Computing Science and Mathematics

Quantified Score

Hi-index	0.00

Visualization

Abstract

On the basis of analysing the unusual objectives of hazardous material transportation HMT, a new multi-objective optimisation model for hazardous material transportation MOM-HMT is established, which takes into account transportation risk, operation time, the number of sensitive population, risks fairness and multi-batch transportation simultaneously. Then a fuzzy adaptive weighted genetic algorithm FAWGA is set up to solve the MOM-HMT by designing priority-based encoding method, partial matching crossover, fuzzy logic control and adaptive weighted assignment mechanism. Finally, the model and algorithm are applied to a real case. The study results show the new model is feasible and the improved genetic algorithm is more effective than the standard genetic algorithm and the improved ant colony algorithm.