4.7 Article

A simulation optimization method for internal trucks sharing assignment among multiple container terminals

Journal

ADVANCED ENGINEERING INFORMATICS
Volume 27, Issue 4, Pages 598-614

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.aei.2013.08.001

Keywords

Multiple container terminals; Internal trucks scheduling; Sharing strategy; Integer programming; Simulation optimization

Funding

  1. National Natural Science Foundation [71101090]
  2. Shanghai Top Academic Discipline Project management science engineering
  3. Shanghai Municipal Education Commission [12ZZ148, 13YZ080]
  4. Ministry of Transport Research Projects [2012-329-810-180]
  5. Shanghai Maritime University [20120102, 20120125]

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Owing that the internal trucks (ITs) are frequently used as transportation equipments between yards and quaysides, the transportation efficiency of ITs secures a crucial position in container terminal productivity. Hence, a container terminal cannot contain a big number of ITs. As such, it is an imperative to explore an appropriate IT assignment strategy. Specifically for those container terminals with adjacent locations, an approach to sharing internal trucks among multiple container terminals (SIMT) is investigated. In this study, a novel strategy to resolve the SIMT problem was proposed for a specific large port with multiple adjacent container terminals. Firstly, an illustration of the SIMT strategy was presented. Then, an integer programming model for this problem is developed, where the objective functions are subject to the minimization of the total overflowed workloads and total transferring costs in every time-period among these container terminals. In particular, the rolling-horizon approach is employed for considering the immediate scheduling. Furthermore, a simulation optimization method, which integrates the genetic algorithm (GA) searching and simulation, is proposed for the near optimal solutions. Finally, the computational experiments are used to verify the effectiveness of the proposed SIMT strategy and simulation optimization method. (C) 2013 Elsevier Ltd. All rights reserved.

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