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Development of Optimization Model to Reduce Unloading and Loading Time at Berth in Container Ports

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Iranian Journal of Science and Technology, Transactions of Civil Engineering Aims and scope Submit manuscript

Abstract

Today, the maritime transportation industry is significantly crucial in the countries' economic cycle, considering that about 90% of the freight transported is done through the sea. Meanwhile, container terminals are becoming increasingly important as a link between the sea and lands. Therefore, the quality of services offered at ports is crucial for accelerating the transportation process, responding promptly to its customer, and attracting new customers. The presented paper examines the optimum time to service ships at the container port berths, which is done by finding a logical-mathematical relation between contributing factors. The contributing factors including required service time, delay time, ship length, length of the berth, number of 20 or 40-inch containers discharged or loaded, number of equipment assigned to each berth, and berth water depth. Finally, the optimal service time is identified by determining the fitness function, initial population, and ultimately imposing constraints in developing the genetic algorithm (GA). Although the GA is used wildly due to powerful algorithms in optimization, in port optimization problem was not considered, which is the novelty of the paper. The optimization results show that 4323 containers were discharged, and 1020 containers were loaded for 5186 min using a single gantry crane. It means that an average of 0.97 min is needed for loading or unloading each container. Using two gantry cranes, this process can be done in 5100 min, with an average of 0.95 min for each container, while the needed time for three gantry cranes was 4908 min, with the average of 0.91 min for each container. Based on the paper results, two important keys to reducing waiting time with berth allocation are determining suitable access channel depths and increasing the number of berths which in this paper are studied and analyzed as practical solutions.

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Notes

  1. The available data were up to 2016, and it is one of our paper limitations.

References

  • Abaei MM, Arzaghi E, Abbassi R, Garaniya V, Javanmardi M, Chai S (2018) Dynamic reliability assessment of ship grounding using Bayesian inference. Ocean Eng 159:47–55

    Article  Google Scholar 

  • Alderton PM, Saieva G (2013) Port management and operations. Taylor & Francis, Abingdon

    Google Scholar 

  • Almawsheki ES, Shah MZ (2015) Technical efficiency analysis of container terminals in the middle eastern region. Asian J Shipp Logist 31(4):477–486

    Article  Google Scholar 

  • Bajpai P, Kumar M (2010) Genetic algorithm–an approach to solve global optimization problems. Indian J Comput Sci Eng 1(3):199–206

    Google Scholar 

  • Barros CP, Athanassiou M (2015) Efficiency in European seaports with DEA: evidence from Greece and Portugal. In: Port management. Palgrave Macmillan, London, pp 293–313

  • Branch A (2012) Elements of port operation and management. Springer Science & Business Media, Berlin

    Google Scholar 

  • Dadashi A, Dulebenets MA, Golias MM, Sheikholeslami A (2017) A novel continuous berth scheduling model at multiple marine container terminals with tidal considerations. Marit Bus Rev 2(2):142–157

    Article  Google Scholar 

  • De Oliveira GF, Cariou P (2015) The impact of competition on container port (in) efficiency. Transp Res Part A Policy Pract 78:124–133

    Article  Google Scholar 

  • Ding ZY, Jo GS, Wang Y, Yeo GT (2015) The relative efficiency of container terminals in small and medium-sized ports in China. Asian J Ship Logist 31(2):231–251

    Article  Google Scholar 

  • Golias M, Boile M, Theofanis S (2007) The berth allocation problem: a formulation reflecting time window service deadlines, (No. 1428-2016-118593)

  • Gotoh J-Y, Kim MJ, Lim AEB (2018) Robust empirical optimization is almost the same as mean-variance optimization. Oper Res Lett 46(4):448–452

    Article  MathSciNet  Google Scholar 

  • Guan Y, Xia WQ, Chenung RK, Li CL (2002) A multiprocessor task scheduling model for berth allocation: heuristic and worst-case analysis. Oper Res Lett 30:343–350

    Article  MathSciNet  Google Scholar 

  • Hanne T, Dornberger R (2017) Computational intelligence. In: Computational intelligence in logistics and supply chain management. Springer, Cham, pp 13–41

  • Imai A, Nishimura E, Papadimitriou S (2005) Berth allocation in a container port using a continuous location space approach. Transp Res Part B 39:199–221

    Article  Google Scholar 

  • Jeevan J, Ghaderi H, Bandara YM, Saharuddin AH, Othman MR (2015) The implications of the growth of port throughput on the port capacity: the case of Malaysian major container seaports. Int J e-Navigation Marit Econ 3:84–98

    Google Scholar 

  • Kim KH, Moon KC (2003) Berth scheduling by simulated annealing. Transp Res Part B Methodol 37(6):541–560

    Article  Google Scholar 

  • Le Carrer N, Ferson S, Green PL (2020) Optimizing cargo loading and ship scheduling in tidal areas. Eur J Oper Res 280(3):1082–1094

    Article  Google Scholar 

  • Levinson M (2016) The box: how the shipping container made the world smaller and the world economy bigger-with a new chapter by the author. Princeton University Press, Princeton

    Book  Google Scholar 

  • Li H, Shu D, Zhang Y, Grace YY (2018) Simultaneous variable selection and estimation for multivariate multilevel longitudinal data with both continuous and binary responses. Comput Stat Data Anal 118:126–137

    Article  MathSciNet  Google Scholar 

  • Mallidis I, Iakovou E, Dekker R, Vlachos D (2018) The impact of slow steaming on the carriers’ and shippers’ costs: the case of a global logistics network. Transp Res Part E Logist Transp Rev 111:18

    Article  Google Scholar 

  • Mili K, Sadraoui T (2015) Optimizing the operational process at container terminal. Int J 3(2):91–98

    Google Scholar 

  • Mokhtar K, Shah MZ (2006) A regression model for vessel turnaround time, Tokyo Academic, Industry & Cultural Integration Tour 2006, 10–19 December, Shibaura Institute of Technology, Japan

  • Notteboom TE (2002) Consolidation and contestability in the European container handling industry. Marit Policy Manag 29(3):257–269

    Article  Google Scholar 

  • Notteboom TE (2006) The time factor in liner shipping services. Marit Econ Logist 8(1):19–39

    Article  Google Scholar 

  • Panahi R, Ghasemi Koohi Kheili A, Golpira A (2017) Future of container shipping in Iranian ports: traffic and connectivity index forecast. J Adv Transp

  • Said GAENA, El-Horbaty ESM (2015) An optimization methodology for container handling using genetic algorithm. Proc Comput Sci 65:662–671

    Article  Google Scholar 

  • Shahpanah A, Poursafary S, Shariatmadari S, Gholamkhasi A, Zahraee SM (2014) Optimization waiting time at berthing area of a port container terminal with hybrid Genetic Algorithm (GA) and Artificial Neural Network (ANN). In: Advanced materials research, vol 902, pp 431–436. Trans Tech Publications

  • Wong A (2008) Optimization of container process at multimodal container terminals, BSc, CertEd, University of Hong Kong

  • Wong A, Kozan E (2010) Optimization of container process at seaport terminals. J Oper Res Soc 61(4):658–665. https://doi.org/10.1057/jors.2009.18

    Article  Google Scholar 

  • Zeng Q, Yang Z (2009) Integrating simulation and optimization to schedule loading operations in container terminals. Comput Oper Res 36(6):1935–1944

    Article  Google Scholar 

  • Zheng XB, Park NK (2016) A study on the efficiency of container terminals in Korea and China. Asian J Ship Logist 32(4):213–220

    Article  Google Scholar 

Download references

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Correspondence to Alireza Mahpour.

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Mahpour, A., Nazifi, A. & Mohammadian Amiri, A. Development of Optimization Model to Reduce Unloading and Loading Time at Berth in Container Ports. Iran J Sci Technol Trans Civ Eng 45, 2831–2840 (2021). https://doi.org/10.1007/s40996-021-00590-2

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  • DOI: https://doi.org/10.1007/s40996-021-00590-2

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