Abstract
This article is characterized by approaching one of the problems encountered in port systems known as the berth allocation problem. As the operational activities require a high degree of time to be carried out and, in most cases they are done manually, it becomes necessary to use an optimization tool. To obtain a good solution with a small amount of computational effort, a heuristic model, based on concepts of genetic algorithms (GA), is proposed, allowing this concept to be learnt. Prepared generically, with some minor adjustments in the data, the method can be applied to solve the problem in any port, as the ports use a similar management system. Finally, a numerical experiment examines and evaluates the results, noting their effectiveness in the aid the improvement and refinement of the management system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Epsilon is the adopted parameter to evaluate if the deviation obtained between the fitness of the worst and the best chromosome is acceptable. The deviation is found by the following:
Deviation = (Worst cromo fitness − best cromo fitness)/best cromo fitness
In this manner, if Deviation > Epsilon, the algorithm continues.
References
Brown G G, Lawphongpanich S, Thurman K P (1994) Optimizing ship berthing. Naval Research Logistics 41:1–15.
Brown G G, Cormican K J, Lawphongpanich S, Widdis D B (1997) Optimizing submarine berthing with a persistence incentive. Naval Research Logistics 44.
Guan Y, Cheung R K (2004) The berth allocation problem: models and solutions methods. OR Spectrum 26:75–92.
Imai A, Nishimura E, Papadimitriou S (2001) The dynamic berth allocation problem for a container port. Transportation Research Part B 3:401–417.
Imai A, Nishimura E, Papadimitriou S (2003) Berth allocation with service priority. Transportation Research Part B 37:437–457.
Kim K H, Moon K C (2003) Berth scheduling by simulated annealing. Transportation Research Part B 37:541–560.
Lim A (1998) The berth planning problem. Operations Research Letters 22:105–110.
Moon K C (2000) A mathematical model and a heuristic algorithm for berth planning. Brain Korea 21 Logistics Team, July.
Mulato F M, Oliveira M M B de (2006) O impacto de um sistema de agendamento antecipado de docas para carga e descarga na gestão da cadeia de suprimentos. Revista Produção Online 6:3–96, set./dez.
Nishimura E, Imai A, Papadimitriou S (2001) Berth allocation planning in the public berth system by genetic algorithms. European Journal of Operational Research 131:282–292.
Silva, V M D, Coelho, A S (2007) Uma visão sobre o problema de alocação de berços. Revista Produção Online 7:2:85–98.
Silva, V M D, Coelho, A S (2008) Modelo heurístico para a resolução de um problema operacional portuário utilizando algoritmos genéticos. Actas del XV Congreso Panamericano de Inginieria Transito y Transporte (PANAM). Cartagena das Indias.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer -Verlag Berlin Heidelberg
About this paper
Cite this paper
Silva, V.M.D., Novaes, A.G., Coelho, A.S. (2011). Resolution of the Berth Allocation Problem through a Heuristic Model Based on Genetic Algorithms. In: Kreowski, HJ., Scholz-Reiter, B., Thoben, KD. (eds) Dynamics in Logistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11996-5_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-11996-5_42
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11995-8
Online ISBN: 978-3-642-11996-5
eBook Packages: EngineeringEngineering (R0)