Finance:Berth allocation problem

The berth allocation problem (also known as the berth scheduling problem) is a NP-complete problem in operations research, regarding the allocation of berth space for vessels in container terminals. Vessels arrive over time and the terminal operator needs to assign them to berths in order to be served (loading and unloading containers) as soon as possible. Different factors affect the berth and time assignment of each vessel. Among models found in the literature, there are four most frequently observed cases:

1. discrete vs. continuous berthing space,
2. static vs. dynamic vessel arrivals,
3. static vs. dynamic vessel handling times, and
4. variable vessel arrivals.

In the discrete problem, the quay is viewed as a finite set of berths. In the continuous problem, vessels can berth anywhere along the quay and the majority of research deals with the former case. In the static arrival problem all vessels are already at the port whereas in the dynamic only a portion of the vessels to be scheduled are present. The majority of the published research in berth scheduling considers the latter case. In the static handling time problem, vessel handling times are considered as input, whereas in the dynamic they are decision variables. Finally, in the last case, the vessel arrival times are considered as variables and are optimized.

Technical restrictions such as berthing draft and inter-vessel and end-berth clearance distance are further assumptions that have been adopted in some of the studies dealing with the berth allocation problem, bringing the problem formulation closer to real world conditions. Introducing technical restrictions to existing berth allocation models is rather straightforward and it may increase the complexity of the problem but simplify the use of metaheuristics (decrease in the feasible space).

Some of the most notable objectives addressed in the literature are:

1. Minimization of vessel total service times (waiting and handling times),
2. Minimization of early and delayed departures,
3. Optimization of vessel arrival times,
4. Optimization of emissions and fuel consumption.

Problems have been formulated as single and multi-objective as well as single and bi-level.

See also

• Container port design process
• List of NP-complete problems

Further reading

• Golias, Mihalis M. (2009). "The berth allocation problem: Optimizing vessel arrival time". Maritime Economics & Logistics 11 (4): 358–377. doi:10.1057/mel.2009.12. 
• Guan, Yongpei; Cheung, Raymond K. (2004). "The berth allocation problem: models and solution methods". OR Spectrum 26 (1): 75–92. doi:10.1007/s00291-003-0140-8. 
• Pinedo, Michael L. (2008). Scheduling: Theory, Algorithms, and Systems. New York: Springer. ISBN 978-0-387-78934-7. 
• Briano C, Briano E., Bruzzone A. G., Revetria R. (2005) Models for Support Maritime Logistics: A Case Study for Improving Terminal Planning . 19th European Conference on Modeling and Simulation. June 1–4, 2005 Riga, Latvia
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• Canonaco, P., Legato, P., Mazza, R., Musmanno, R. A queuing network model for the management of berth crane operations. Computers and Operations Research, Vol. 35(8), 2008, pp. 2432–2446.
• Cordeau, J.-F., Laporte, G., Legato, P., Moccia, L. Models and tabu search heuristics for the berth-allocation problem. Transportation Science. Vol. 39, 2005, pp. 526–538.
• Dai, J., Liu, W., Moorthy, R. and Teo, C.-P. Berth Allocation Planning Optimization in Container Terminals. http://www.bschool.nus.edu.sg/staff/bizteocp/berthplanningjuly2004.pdf%5B%5D
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• Golias M.M. (2011) A bi-objective berth allocation formulation to account for vessel handling time uncertainty. Journal of Maritime Economics and Logistics. 13:419-441
• Golias M.M., Haralambides H.E. Berth scheduling with variable cost functions. (2011) Journal of Maritime Economics and Logistics. 13:174-189
• Golias M.M., Boilé M., Theofanis S., Efstathiou C. (2010) The berth scheduling problem: Maximizing berth productivity and minimizing fuel consumption and emissions production. Transportation Research Record: Journal of the Transportation Research Board, Marine Transportation and Port Operations, 2166, 20-27.
• Golias M.M., Boilé M., Theofanis S. (2010) The discrete berth scheduling problem: Towards a unified mathematical formulation. Transportation Research Record: Journal of the Transportation Research Board, Freight Transportation Modeling, Planning, and Logistics, 2168, 1-8.
• Golias M.M., Boilé M., Theofanis S., Taboada A.H. (2010) A multi-objective decision and analysis approach for the berth scheduling problem. International Journal of Information Technology Project Management, 1(1), 54-73.
• Saharidis G.K.D., Golias M.M., Boilé M., Theofanis S., Ierapetritou M. (2009) The berth scheduling problem with customer differentiation: A new methodological approach based on hierarchical optimization. International Journal of Advanced Manufacturing Technology, 46(1-4), 377-393.
• Golias M.M., Boilé M., Theofanis S. (2009) Service time based customer differentiation berth scheduling. Transportation Research Part E: Logistics and Transportation Review, 45(6), 878-892.
• Golias M.M., Boilé M., Theofanis S. (2009) A lambda-optimization based heuristic for the discrete berth scheduling problem. Transportation Research Pt. C, 18(5), 794-806.
• Golias M.M., Boilé M., Theofanis S. (2009) An adaptive time window partitioning based algorithm for the discrete and dynamic berth scheduling problem. Transportation Research Record: Journal of the Transportation Research Board, Network Modeling, 2091, 21-30.
• Boilé M., Golias M.M., Theofanis S. (2009) Scheduling of berthing resources at a marine container terminal via the use of Genetic Algorithms: Current and Future Research. In: Pinheiro dos Santos, Wellington et al. (Eds.), Evolutionary Computation. Vukovar: In-Teh. ISBN:978-953-307-008-7, pp. 61–76.
• Guan Y, Xiao W-Q, Cheung R K, and Li C-L. A multiprocessor task scheduling model for berth allocation: heuristic and worst case analysis. Operations Research Letters, Vol. 30, 2002, pp. 343–350.
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• Hansen, P., Oguz, C. and Mladenovic, N. Variable neighborhood search for minimum cost berth allocation. European Journal of Operational Research, Vol. 131(3), 2008, pp. 636–649.
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• Imai A., Nagaiwa K., Tat C-W. Efficient planning of berth allocation for container terminals in Asia. Journal of Advanced Transportation, Vol. 31, 1997, pp. 75–94.
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• Imai A., Sun X., Nishimura E., and Papadimitriou S. Berth Allocation in a Container Port: Using Continuous Location Space Approach. Transportation Research Part B, Vol. 39, 2005, pp. 199–221.
• Imai, A., Nishimura, E. and Papadimitriou, S. Berthing ships at a multi-user container terminal with a limited quay capacity. Transportation Research Part E, Vol. 44(1), 2007, pp. 136–151.
• Imai, A., Nishimura, E. and Papadimitriou, S. Corrigendum to “The dynamic berth allocation problem for a container port”. Transportation Research Part B, Vol. 39(3), 2005a, p. 197.
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• Iris, C., Pacino, D., Ropke, S., Larsen, A., Integrated Berth Allocation and Quay Crane Assignment Problem: Set partitioning models and computational results. Transportation Research Part E, Vol. 81, 2015, pp. 75–97.
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• Meisel, F. (2009). Seaside operations planning in container terminals. Physica-Verlag Berlin Heidelberg.
• Meisel, F., and Bierwirth, C. (2009) Heuristics for the integration of crane productivity in the berth allocation problem. Transportation Research Part E 45(1): 196-209.
• Monaco, M.F. and Samara, M. The Berth Allocation Problem: a Strong Formulation Solved by a Lagrangean Approach”, Transportation Science, Vol. 41, No.2, 2007, pp. 265–280.
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• Nishimura E., Imai A., Papadimitriou S. Berth allocation planning in the public berth system by genetic algorithms. European Journal of Operational Research, Vol. 131, 2001, pp. 282–292.
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• Wang, C., Miao, L., Zhang, C., Wu, T., & Liang, Z. (2023). Robust optimization for the integrated berth allocation and quay crane assignment problem. Naval Research Logistics (NRL), 1–25. https://doi.org/10.1002/nav.22159