Javanshir, H., Seyedalizadeh Ganji, S. (2010). Yard crane scheduling in port container terminals using genetic algorithm. Journal of Industrial Engineering, International, 6(11), 39-50.
H Javanshir; S.R Seyedalizadeh Ganji. "Yard crane scheduling in port container terminals using genetic algorithm". Journal of Industrial Engineering, International, 6, 11, 2010, 39-50.
Javanshir, H., Seyedalizadeh Ganji, S. (2010). 'Yard crane scheduling in port container terminals using genetic algorithm', Journal of Industrial Engineering, International, 6(11), pp. 39-50.
Javanshir, H., Seyedalizadeh Ganji, S. Yard crane scheduling in port container terminals using genetic algorithm. Journal of Industrial Engineering, International, 2010; 6(11): 39-50.
Yard crane scheduling in port container terminals using genetic algorithm
1Assistant Professor, Dep. of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
2M. Sc., Dep. of Transportation Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
Abstract
Yard crane is an important resource in container terminals. Efficient utilization of the yard crane significantly improves the productivity and the profitability of the container terminal. This paper presents a mixed integer programming model for the yard crane scheduling problem with non- interference constraint that is NPHARD in nature. In other words, one of the most important constraints in this model which we can mention to yard crane non- interference constraint is that they usually move on the same rails in the yard block. Optimization methods, like branch and bound algorithm, has no sufficient efficiency to solve this model and become perfectly useless when the problem size increases. In this situation, using an advanced search method like genetic algorithm (GA) may be suitable. In this paper, a GA is proposed to obtain near optimal solutions. The GA is run by MATLAB 7.0 and the researchers used LINGO software which benefits from the Branch and Bound algorithm for comparing outputs of GA and the exact solution. We should consider the abilities of the LINGO software which is not capable of solving the problems larger than 5 slots to 3 yard cranes. The computational results show that the proposed GA is effective and efficient in solving the considered yard crane scheduling problem.
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