Optimization of container stowage using simulated annealing and genetic algorithms

M.A. Yurtseven; E. Boulougouris; O. Turan; N. Papadopoulos

Optimization of container stowage using simulated annealing and genetic algorithms

M.A. Yurtseven, E. Boulougouris, O. Turan, N. Papadopoulos

Naval Architecture, Ocean And Marine Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

1 Citation (Scopus)

Abstract

The purpose of this paper is to investigate the optimization of container stowage plan problem for a container vessel with multiple ports of call. Generally, container vessels visit many different ports on their voyage. Due to the loading and offloading at each port, finding the stowage planning for container vessel is getting more difficult for each subsequent port and also the complexity in stowage planning increases. For that reason, container stowage problem is called NP-hard problem. Genetic Algorithm and Simulated Annealing Algorithm are implemented herein to obtain the optimum solution. After finding the optimum solution from these two algorithms, the results are compared to evaluate their computational cost and efficiency.

Original language	English
Title of host publication	Maritime Transportation and Harvesting of Sea Resources
Subtitle of host publication	Proceedings of the 17th International Congress of the International Maritime Association of the Mediterranean
Editors	Carlos Guedes Soares, Ângelo P. Teixeira
Place of Publication	[S.I.]
Pages	881-886
Number of pages	6
Publication status	Published - 20 Nov 2017
Event	17th International Congress of the International Maritime Association of the Mediterranean - Lisbon, Portugal Duration: 9 Oct 2017 → 11 Oct 2017 Conference number: 17 http://www.imamhomepage.org/imam2017/index.aspx

Conference

Conference	17th International Congress of the International Maritime Association of the Mediterranean
Abbreviated title	IMAM 2017
Country/Territory	Portugal
City	Lisbon
Period	9/10/17 → 11/10/17
Internet address	http://www.imamhomepage.org/imam2017/index.aspx

Keywords

container stowage
container vessels

Cite this

Yurtseven, M. A., Boulougouris, E., Turan, O., & Papadopoulos, N. (2017). Optimization of container stowage using simulated annealing and genetic algorithms. In C. G. Soares, & Â. P. Teixeira (Eds.), Maritime Transportation and Harvesting of Sea Resources: Proceedings of the 17th International Congress of the International Maritime Association of the Mediterranean (pp. 881-886). [107].

Yurtseven, M.A. ; Boulougouris, E. ; Turan, O. ; Papadopoulos, N. / Optimization of container stowage using simulated annealing and genetic algorithms. Maritime Transportation and Harvesting of Sea Resources: Proceedings of the 17th International Congress of the International Maritime Association of the Mediterranean. editor / Carlos Guedes Soares ; Ângelo P. Teixeira. [S.I.], 2017. pp. 881-886

@inproceedings{8d2402dcc5bc46568b8cf6d41aa4578c,

title = "Optimization of container stowage using simulated annealing and genetic algorithms",

abstract = "The purpose of this paper is to investigate the optimization of container stowage plan problem for a container vessel with multiple ports of call. Generally, container vessels visit many different ports on their voyage. Due to the loading and offloading at each port, finding the stowage planning for container vessel is getting more difficult for each subsequent port and also the complexity in stowage planning increases. For that reason, container stowage problem is called NP-hard problem. Genetic Algorithm and Simulated Annealing Algorithm are implemented herein to obtain the optimum solution. After finding the optimum solution from these two algorithms, the results are compared to evaluate their computational cost and efficiency.",

keywords = "container stowage, container vessels",

author = "M.A. Yurtseven and E. Boulougouris and O. Turan and N. Papadopoulos",

year = "2017",

month = nov,

day = "20",

language = "English",

isbn = "9780815379935",

pages = "881--886",

editor = "Soares, {Carlos Guedes} and Teixeira, {{\^A}ngelo P.}",

booktitle = "Maritime Transportation and Harvesting of Sea Resources",

note = "17th International Congress of the International Maritime Association of the Mediterranean, IMAM 2017 ; Conference date: 09-10-2017 Through 11-10-2017",

url = "http://www.imamhomepage.org/imam2017/index.aspx",

}

Yurtseven, MA , Boulougouris, E , Turan, O & Papadopoulos, N 2017, Optimization of container stowage using simulated annealing and genetic algorithms. in CG Soares & ÂP Teixeira (eds), Maritime Transportation and Harvesting of Sea Resources: Proceedings of the 17th International Congress of the International Maritime Association of the Mediterranean., 107, [S.I.], pp. 881-886, 17th International Congress of the International Maritime Association of the Mediterranean, Lisbon, Portugal, 9/10/17.

Optimization of container stowage using simulated annealing and genetic algorithms. / Yurtseven, M.A.; Boulougouris, E.; Turan, O.; Papadopoulos, N.

Maritime Transportation and Harvesting of Sea Resources: Proceedings of the 17th International Congress of the International Maritime Association of the Mediterranean. ed. / Carlos Guedes Soares; Ângelo P. Teixeira. [S.I.], 2017. p. 881-886 107.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Optimization of container stowage using simulated annealing and genetic algorithms

AU - Yurtseven, M.A.

AU - Boulougouris, E.

AU - Turan, O.

AU - Papadopoulos, N.

N1 - Conference code: 17

PY - 2017/11/20

Y1 - 2017/11/20

N2 - The purpose of this paper is to investigate the optimization of container stowage plan problem for a container vessel with multiple ports of call. Generally, container vessels visit many different ports on their voyage. Due to the loading and offloading at each port, finding the stowage planning for container vessel is getting more difficult for each subsequent port and also the complexity in stowage planning increases. For that reason, container stowage problem is called NP-hard problem. Genetic Algorithm and Simulated Annealing Algorithm are implemented herein to obtain the optimum solution. After finding the optimum solution from these two algorithms, the results are compared to evaluate their computational cost and efficiency.

AB - The purpose of this paper is to investigate the optimization of container stowage plan problem for a container vessel with multiple ports of call. Generally, container vessels visit many different ports on their voyage. Due to the loading and offloading at each port, finding the stowage planning for container vessel is getting more difficult for each subsequent port and also the complexity in stowage planning increases. For that reason, container stowage problem is called NP-hard problem. Genetic Algorithm and Simulated Annealing Algorithm are implemented herein to obtain the optimum solution. After finding the optimum solution from these two algorithms, the results are compared to evaluate their computational cost and efficiency.

KW - container stowage

KW - container vessels

UR - http://www.imamhomepage.org/imam2017/index.aspx

UR - https://www.crcpress.com/Developments-in-Maritime-Transportation-and-Harvesting-of-Sea-Resources/Soares-Teixeira/p/book/9780815379935

M3 - Conference contribution book

SN - 9780815379935

SP - 881

EP - 886

BT - Maritime Transportation and Harvesting of Sea Resources

A2 - Soares, Carlos Guedes

A2 - Teixeira, Ângelo P.

CY - [S.I.]

T2 - 17th International Congress of the International Maritime Association of the Mediterranean

Y2 - 9 October 2017 through 11 October 2017

ER -

Yurtseven MA , Boulougouris E , Turan O, Papadopoulos N. Optimization of container stowage using simulated annealing and genetic algorithms. In Soares CG, Teixeira ÂP, editors, Maritime Transportation and Harvesting of Sea Resources: Proceedings of the 17th International Congress of the International Maritime Association of the Mediterranean. [S.I.]. 2017. p. 881-886. 107

Optimization of container stowage using simulated annealing and genetic algorithms

Abstract

Conference

Keywords

Other files and links

Fingerprint

Cite this