Developing a dynamic rolling-horizon decision strategy for yard crane scheduling

  • Authors:

  • Daofang Chang;Zuhua Jiang;Wei Yan;Junliang He

  • Affiliations:

  • School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China and Engineering Research Center of Container Supply Chain Technology, Ministry of Education, Shanghai Mar ...;School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China;Engineering Research Center of Container Supply Chain Technology, Ministry of Education, Shanghai Maritime University, Shanghai 201306, PR China;Engineering Research Center of Container Supply Chain Technology, Ministry of Education, Shanghai Maritime University, Shanghai 201306, PR China

  • Venue:
  • Advanced Engineering Informatics
  • Year:
  • 2011

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Abstract

As a hub for land and marine transportation, container terminals play an important role in global trade. In today's competitive environment, container terminals should improve their service quality, i.e., effective space resource handling and equipment resource scheduling, for their prosperity or even survival. Although intensive researches were attempted on yard crane scheduling, the solutions from these approaches likely reached a local optimum, and thereafter a rational strategy towards global optimum was still lacking. Accordingly, it became an imperative to explore a rational strategy for this purpose. To resolve this problem, a novel dynamic rolling-horizon decision strategy was proposed for yard crane scheduling in this study. Initially, an integer programming model was established to minimize the total task delaying at blocks. Due to the computational scale with regard to the yard crane scheduling problem, a heuristic algorithm, along with a simulation model, was then applied. In this fashion, the simulation model was next investigated to alternate the periods and evaluate the task delaying. Subsequently, a genetic algorithm was employed to optimize the initial solutions generated. Consequently, computational experiments were used to illustrate the proposed strategy for yard crane scheduling and verify the effectiveness and efficiency of the proposed approach.