Minimizing the total energy consumption of yard crane under the peak demand constraint

CUI Weiwei, ZHEN Lu

Systems Engineering - Theory & Practice ›› 2021, Vol. 41 ›› Issue (2) : 358-369.

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Systems Engineering - Theory & Practice ›› 2021, Vol. 41 ›› Issue (2) : 358-369. DOI: 10.12011/SETP2020-1522

Minimizing the total energy consumption of yard crane under the peak demand constraint

  • CUI Weiwei, ZHEN Lu
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Abstract

With the increasing concern of environment pollution caused by the massive usage of fossil fuels, the energy-efficient operation of green container terminals is becoming one of the hottest topics in recent years. This paper investigates the yard cranes with the pre-marshalling tasks, in which the power demand of crane is different when the crane runs in different mode. Considering the peak power constraint, a mixed integer programming model is established to minimize the total energy consumption of all cranes with non-crossing and safety clearance requirements in the storage yard. Two kinds of coding methods are designed based on the idea of discrete bay and continuous bay, then the chromosome is decoded using heuristic rules. And, the crossover and mutation methods are designed for the genetic algorithm (GA) correspondingly. Numerical results show that the method of continuous bay is better than the discrete bay. Then, the effectiveness of designed GA is validated by the results obtained by the comparison between GA and Cplex, particle swarm optimization, artificial bee colony algorithm. In addition, compared with the traditional policy, our model performs well in both of total energy consumption and peak demand. The research findings can guide the port managers to reduce the energy cost effectively without interfering the processing of daily tasks.

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green port / yard crane scheduling / peak power / energy consumption

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CUI Weiwei , ZHEN Lu. Minimizing the total energy consumption of yard crane under the peak demand constraint. Systems Engineering - Theory & Practice, 2021, 41(2): 358-369 https://doi.org/10.12011/SETP2020-1522

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Funding

National Natural Science Foundation of China (71831008, 71801147)
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