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Data envelopment analysis of AGV fleet sizing at a port container terminal

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  • Danijela Pjevcevic
  • Milos Nikolic
  • Natasa Vidic
  • Katarina Vukadinovic

Abstract

A decision-making approach based on Data Envelopment Analysis (DEA) for determining the efficient container handling processes (considering the number of employed Automated Guided Vehicles (AGVs)) at a port container terminal (PCT) is presented in this paper. Containers are unloaded from the ship by quay cranes and transported to the storage area by AGVs. We defined performance measures of proposed container handling processes and analysed the effects when changing the number of AGVs. The values of performance measures were collected and/or calculated from simulation. Container handling process, with a fixed number of quay cranes, when a different number of AGVs is used to transport containers from berth to assigned locations within storage area, represents a decision-making unit (DMU). We applied the basic CCR (Charnes, Cooper and Rhodes) DEA model with two inputs: average ship operating delay costs and average operating costs of employed equipment at a PCT, and two outputs: average number of handled import containers per ship and weighted average utilisation rate of equipment at a PCT. DEA method proved to be useful when testing different DMUs and when determining efficient DMUs for planning purposes. This study shows that efficiency evaluation of AGV fleet sizing and operations is useful for planning purposes at PCTs.

Suggested Citation

  • Danijela Pjevcevic & Milos Nikolic & Natasa Vidic & Katarina Vukadinovic, 2017. "Data envelopment analysis of AGV fleet sizing at a port container terminal," International Journal of Production Research, Taylor & Francis Journals, vol. 55(14), pages 4021-4034, July.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:14:p:4021-4034
    DOI: 10.1080/00207543.2016.1241445
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    Cited by:

    1. Fragapane, Giuseppe & de Koster, René & Sgarbossa, Fabio & Strandhagen, Jan Ola, 2021. "Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda," European Journal of Operational Research, Elsevier, vol. 294(2), pages 405-426.
    2. Wenxiang Xu & Shunsheng Guo, 2019. "A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode," Sustainability, MDPI, vol. 11(5), pages 1-24, March.

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