Advances in Production Engineering & Management
Volume 13 | Number 4 | December 2018 | pp 479–491
https://doi.org/10.14743/apem2018.4.305
Multi-objective transport network design with a reversible simulated annealing algorithm
Feng, X.; Ruan, Z.; Zhu, X.; Zhang, L.
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A B S T R A C T
In order to rationally coordinate inconsistent objectives in transport network design, this research newly develops a multi-objective network layout optimisation model solved by an improved Simulated Annealing Algorithm (SAA). Two temperature control variables and one cost difference control variable are defined in the proposed SAA. They work in cooperation to restart the optimum search from the latest temporary optimal solution if the search is made excessively in any searching direction as well as expand the searching area for the globally optimal network layout with the minimum operation cost. The genetic algorithm is embedded into the reversible SAA to iteratively provide a network configuration with the minimum total time expense of all the transports for the minimisation of the network operation cost. It is confirmed that the new optimisation model solved by the reversible SAA integrating the genetic algorithm is able to effectively minimise both the total transport time expense and the network operation cost with searching for the best fits between these two basically inconsistent objectives from different perspectives. The proposed approach can be utilised to optimise configurations of not only urban transit lines for passenger mobility organisation but also logistics transportation routes for manufacturing production management.
A R T I C L E I N F O
Keywords • Transport network design; Multi-objective optimisation modelling; Reversible simulated annealing algorithm; Genetic algorithm; Double temperatures; Network operation cost difference
Corresponding author • Feng, X.
Article history • Received 13 October 2018, Revised 28 November 2018, Accepted 7 December 2018
Published on-line • 21 December 2018
E X P O R T C I T A T I O N
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