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A decision support system for a capacity management problem at a container terminal

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  • Mar-Ortiz, Julio
  • Castillo-García, Norberto
  • Gracia, María D.

Abstract

This paper aims to design an optimization-based decision support system (DSS) for a capacity management problem in container terminals. The capacity management problem at a container terminal seeks to balance supply (equipment assignment) and demand (arrivals of trucks) while improving customer service level and productivity indicators. Specifically, this paper addresses the problem of determining the appointment quota for each time slot on a one-day planning horizon, within a container terminal working on a truck appointment system (TAS) environment. The DSS is developed in cooperation with an industrial partner to help them with their operational decision making when determining the appointment quotas, considering yard resource availability. The underlying approach makes use of a tabu search metaheuristic algorithm to solve a related optimization problem that balance the supply and demand of moves in the yard. The DSS is flexible, allowing several parameters to be configured and read from external files. Experimental examples using real-world data were conducted to evaluate the effectiveness of the system, and the suitability of the DSS.

Suggested Citation

  • Mar-Ortiz, Julio & Castillo-García, Norberto & Gracia, María D., 2020. "A decision support system for a capacity management problem at a container terminal," International Journal of Production Economics, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:proeco:v:222:y:2020:i:c:s0925527319303226
    DOI: 10.1016/j.ijpe.2019.09.023
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    4. Mohammad Torkjazi & Nathan Huynh & Ali Asadabadi, 2022. "Modeling the Truck Appointment System as a Multi-Player Game," Logistics, MDPI, vol. 6(3), pages 1-25, July.
    5. Roy, Debjit & van Ommeren, Jan-Kees & de Koster, René & Gharehgozli, Amir, 2022. "Modeling landside container terminal queues: Exact analysis and approximations," Transportation Research Part B: Methodological, Elsevier, vol. 162(C), pages 73-102.
    6. Lange, Ann-Kathrin & Kreuz, Felix & Langkau, Sven & Jahn, Carlos & Clausen, Uwe, 2020. "Defining the quota of truck appointment systems," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conferen, volume 30, pages 211-246, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    7. Lange, Ann-Kathrin & Nellen, Nicole & Jahn, Carlos, 2022. "Truck appointment systems: How can they be improved and what are their limits?," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Jahn, Carlos & Blecker, Thorsten & Ringle, Christian M. (ed.), Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New , volume 33, pages 615-655, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    8. Bajomo, Mary & Ogbeyemi, Akinola & Zhang, Wenjun, 2022. "A systems dynamics approach to the management of material procurement for Engineering, Procurement and Construction industry," International Journal of Production Economics, Elsevier, vol. 244(C).

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