* Corresponding Author
Hide
Show all
Agent-based Truck Appointment System for Containers Pick-up Time Negotiation
https://doi.org/10.22146/ijccs.51274 Fakhri Ihsan Ramadhan(1*), Meditya Wasesa(2)
(1) Institut Teknologi Bandung
(2) Institut Teknologi Bandung
(*) Corresponding Author
Abstract
Congestion in the seaports area is a common issue in many parts of the world. Fluctuating truck arrival has been identified as one of the significant determinants of congestion. In response, a truck appointment system (TAS) is introduced to manage truck arrival, particularly at peak times. In the existing TAS mechanism, the scheduling decision is centralized and disregards the concerns of trucking companies. Moreover, TAS may complicate the business operation of trucking companies that already have a constrained truck schedule. This study proposes a decentralized negotiation mechanism in TAS that allows trucking companies to adjust arrival times by utilizing the waiting time estimation provided by the terminal operator. We develop an agent-based model of a TAS in the container terminal pick-up procedure. The simulation results indicate that compared to the existing TAS mechanism, the negotiation TAS mechanism generates a shorter average truck turnaround time regardless of truck arrival rates. In terms of average net time cost, the negotiation TAS mechanism provides better value under high truck arrival rate conditions. The incentive for trucking companies to participate in the negotiations is even higher at peak times.
Keywords
Full Text:
PDFReferences
[1] Q. Zeng, Y. Feng, and Z. Yang, “Integrated optimization of pickup sequence and container rehandling based on partial truck arrival information,” Comput. Ind. Eng., vol. 127, no. November 2016, pp. 366–382, 2019.
[2] A. Azab, A. Karam, and A. Eltawil, Impact of collaborative external truck scheduling on yard efficiency in container terminals, vol. 884. Springer International Publishing, 2018.
[3] UNCTAD, Review of Maritime Transport 2019, no. October. 2019.
[4] C. Guan and R. Liu, “Container terminal gate appointment system optimization,” Marit. Econ. Logist., vol. 11, no. 4, pp. 378–398, 2009.
[5] R. Stahlbock and S. Voß, “Operations research at container terminals : A literature update,” OR Spectr., no. April, 2008.
[6] O. Sharif, N. Huynh, and J. M. Vidal, “Application of El Farol model for managing marine terminal gate congestion,” Res. Transp. Econ., vol. 32, no. 1, pp. 81–89, 2011.
[7] G. Chen, K. Govindan, and Z. Yang, “Managing truck arrivals with time windows to alleviate gate congestion at container terminals,” Int. J. Prod. Econ., vol. 141, no. 1, pp. 179–188, 2013.
[8] G. Giuliano and T. O’Brien, “Reducing port-related truck emissions: The terminal gate appointment system at the Ports of Los Angeles and Long Beach,” Transp. Res. Part D Transp. Environ., vol. 12, no. 7, pp. 460–473, 2007.
[9] P. Morais and E. Lord, “Terminal Appointment System Study,” 2006.
[10] N. Huynh, D. Smith, and F. Harder, “Truck Appointment Systems: Where We Are and Where to Go from Here,” Transp. Res. Rec. J. Transp. Res. Board, vol. 2548, no. 1, pp. 1–9, 2016.
[11] B. Dragović, E. Tzannatos, and N. K. Park, “Simulation modelling in ports and container terminals: literature overview and analysis by research field, application area and tool,” Flex. Serv. Manuf. J., vol. 29, no. 1, pp. 4–34, 2017.
[12] W. Zhao and A. V. Goodchild, “Using the truck appointment system to improve yard efficiency in container terminals,” Maritime Economics and Logistics, vol. 15, no. 1. pp. 101–119, 2013.
[13] S. Shiri and N. Huynh, “Optimization of drayage operations with time-window constraints,” Int. J. Prod. Econ., vol. 176, no. March 2016, pp. 7–20, 2016.
[14] N. A. D. Do, I. E. Nielsen, G. Chen, and P. Nielsen, “A simulation-based genetic algorithm approach for reducing emissions from import container pick-up operation at container terminal,” Ann. Oper. Res., vol. 242, no. 2, pp. 285–301, 2016.
[15] N. Huynh, “Reducing Truck Turn Times at Marine Terminals with Appointment Scheduling,” Transp. Res. Rec. J. Transp. Res. Board, vol. 2100, no. 1, pp. 47–57, 2009.
[16] N. Huynh and C. M. Walton, “Robust Scheduling of Truck Arrivals at Marine Container Terminals,” J. Transp. Eng., vol. 134, no. 8, pp. 347–353, 2008.
[17] M. H. Phan and K. H. Kim, “Negotiating truck arrival times among trucking companies and a container terminal,” Transp. Res. Part E Logist. Transp. Rev., vol. 75, pp. 132–144, 2015.
[18] P. Davidsson, L. Henesey, L. Ramstedt, J. Törnquist, and F. Wernstedt, “An analysis of agent-based approaches to transport logistics,” Transp. Res. Part C Emerg. Technol., vol. 13, no. 4, pp. 255–271, 2005.
[19] N. Huynh and J. M. Vidal, “A novel methodology for modelling yard cranes at seaport terminals to support planning and real-time decision making,” Int. J. Six Sigma Compet. Advant., vol. 7, no. 1, p. 62, 2012.
[20] F. Rolansa and A. S.N., “Sistem Simulasi Evakuasi Kebakaran Berbasis Multi Agen,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 9, no. 1, p. 101, 2015.
[21] N. Li, G. Chen, K. Govindan, and Z. Jin, “Disruption management for truck appointment system at a container terminal: A green initiative,” Transp. Res. Part D Transp. Environ., vol. 61, pp. 261–273, 2018.
[22] M. Wasesa, Agent-based Inter-organizational Systems in Advanced Logistics Operations. No. EPS-2017-424-LIS, 2017.
DOI: https://doi.org/10.22146/ijccs.51274
Article Metrics
Abstract views : 3582
| 
views : 3616
Refbacks
- There are currently no refbacks.
Copyright (c) 2020 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1