PDF
Abstract
The need to transport goods across countries and islands has resulted in a high demand for commercial vessels. Owing to such trends, shipyards must efficiently produce ships to reduce production costs. Layout and material flow are among the crucial aspects determining the efficiency of the production at a shipyard. This paper presents the initial design optimization of a shipyard layout using Nondominated Sorting Algorithm-II (NSGA-II) to find the optimal configuration of workstations in a shipyard layout. The proposed method focuses on simultaneously minimizing two material handling costs, namely work-based material handling and duration-based material handling. NSGA-II determines the order of workstations in the shipyard layout. The semiflexible bay structure is then used in the workstation placement process from the sequence formed in NSGA-II into a complete design. Considering that this study is a case of multiobjective optimization, the performance for both objectives at each iteration is presented in a 3D graph. Results indicate that after 500 iterations, the optimal configuration yields a work-based MHC of 163 670.0 WBM-units and a duration-based MHC of 34 750 DBM-units. Starting from a random solution, the efficiency of NSGA-II demonstrates significant improvements, achieving a 50.19% reduction in work-based MHC and a 48.58% reduction in duration-based MHC.
Keywords
Shipyard
/
Multiobjective optimization
/
Material handling
/
Nondominated sorting algorithm-II
Cite this article
Download citation ▾
Gunawan, Ghulam Tulus Pambudi, Allesandro Setyo Anggito Utomo.
Multisystem of Material Handling for Shipyard Facility Layout Optimization Using NSGA-II.
Journal of Marine Science and Application, 2025, 24(4): 855-863 DOI:10.1007/s11804-025-00643-2
| [1] |
AllahyariMZ, AzabA. Mathematical modeling and multi-start search simulated annealing for unequal-area facility layout problem. Expert Syst. Appl., 2018, 91: 46-62
|
| [2] |
BesbesM, ZolghadriM, AffonsoRC. A method to solve 2D Facility Layout Problem with equipment inputs/outputs constraints using meta-heuristics algorithms. Procedia CIRP, 2021, 104: 1698-1703
|
| [3] |
BesbesM, ZolghadriM, Costa AffonsoR, MasmoudiF, HaddarM. A methodology for solving facility layout problem considering barriers: genetic algorithm coupled with A* search. J. Intell. Manuf., 2020, 31: 615-640
|
| [4] |
BragliaM, ZanoniS, ZavanellaL. Layout design in dynamic environments: Strategies and quantitative indices. Int. J. Prod. Res., 2003, 41: 995-1016
|
| [5] |
ChoiM, KimSH, ChungH. Optimal shipyard facility layout planning based on a genetic algorithm and stochastic growth algorithm. Ships Offshore Struct., 2017, 12: 486-494
|
| [6] |
DebK, PratapA, AgarwalS, MeyarivanT. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput., 2002, 6: 182-197
|
| [7] |
Deep K (2020) Facility layout design using genetic algorithm approach. IUP J. Oper. Manag
|
| [8] |
Gonçalves J, Resende M (2015) A biased random-key genetic algorithm for the unequal area facility layout problem. Eur. J. Oper. Res. 246. https://doi.org/10.1016/j.ejor.2015.04.029
|
| [9] |
GuanC, ZhangZ, LiuS, GongJ. Multi-objective particle swarm optimization for multi-workshop facility layout problem. J. Manuf. Syst., 2019, 53: 32-48
|
| [10] |
Gunawan, Utomo ASA, Benediktus HSV (2021) Optimization of shipyard layout with material handling cost as the main parameter using genetic algorithm. AIP Conf. Proc. 2376. https://doi.org/10.1063/5.0063890
|
| [11] |
HaniY, AmodeoL, YalaouiF, ChenH. Ant colony optimization for solving an industrial layout problem. Eur. J. Oper. Res., 2007, 183: 633-642
|
| [12] |
JuniorWA, AzzoliniFGP. Evolutionary algorithm for optimization regarding the planning of topological facilities in layout of a shipyard. IEEE Lat. Am. Trans., 2019, 17: 1491-1500
|
| [13] |
JuniorWA, AzzoliniFGP, MundimLR, PortoAJV, AmaniHJS. Shipyard facility layout optimization through the implementation of a sequential structure of algorithms. Heliyon, 2023, 9e16714
|
| [14] |
KochharJS, FosterBT, HeraguSS. HOPE: A genetic algorithm for the unequal area facility layout problem. Comput. Oper. Res., 1998, 25: 583-594
|
| [15] |
Komarudin, WongKY. Applying ant system for solving unequal area facility layout problems. Eur. J. Oper. Res., 2010, 202: 730-746
|
| [16] |
Kulturel-KonakS, KonakA. Unequal area flexible bay facility layout using ant colony optimisation. Int. J. Prod. Res., 2011, 49: 1877-1902
|
| [17] |
LiuJ, ZhangH, HeK, JiangS. Multi-objective particle swarm optimization algorithm based on objective space division for the unequal-area facility layout problem. Expert Syst. Appl., 2018, 102: 179-192
|
| [18] |
LiuJ, LiuJ. Applying multi-objective ant colony optimization algorithm for solving the unequal area facility layout problems. Appl. Soft Comput. J., 2019, 74: 167-189
|
| [19] |
MakKL, WongYS, ChanFTS. A genetic algorithm for facility layout problems. Comput. Integr. Manuf. Syst., 1998, 11: 113-127
|
| [20] |
MatsonJO, MellichamphJM, SwaminathanSR. EXCITE: Expert consultant for in-plant transportation equipment. Int. J. Prod. Res., 1992, 30: 1969-1983
|
| [21] |
MuhayatH, UtamimaA. Solving unequal area facility layout problems with differential evolution and particle swarm optimization. Procedia Comput. Sci., 2024, 234: 302-309
|
| [22] |
Palomo-RomeroJM, Salas-MoreraL, García-HernándezL. An island model genetic algorithm for unequal area facility layout problems. Expert Syst. Appl., 2017, 68: 151-162
|
| [23] |
PalubeckisG. Single row facility layout using multi-start simulated annealing. Comput. Ind. Eng., 2017, 103: 1-16
|
| [24] |
Rosenblatt MJ (2013) Material handling BT-encyclopedia of operations research and management science, in: Gass, S. I., Fu, M. C. (Eds.). Springer US, Boston, MA, 945–948. https://doi.org/10.1007/978-1-4419-1153-7_590
|
| [25] |
SamarghandiH, TaabayanP, JahantighFF. A particle swarm optimization for the single row facility layout problem. Comput. Ind. Eng., 2010, 58: 529-534
|
| [26] |
SulaimanSS, Leela JancyP, MuthiahA, JanakiramanV, GnanarajSJP. An evolutionary optimal green layout design for a production facility by simulated annealing algorithm. Mater. Today Proc., 2021, 47: 4423-4430
|
| [27] |
TurgayS. Multi objective simulated annealing approach for facility layout design. Int. J. Math. Eng. Manag. Sci., 2018, 3: 365-380
|
| [28] |
TürkA, GürgenS, OzkokM, Altinİ. A comprehensive investigation into the performance of genetic algorithm for effective shipyard topological layout. Proc. Inst. Mech. Eng. Part M J. Eng. Marit. Environ., 2022, 236: 726-740
|
| [29] |
WangMJ, HuMH, KuMY. A solution to the unequal area facilities layout problem by genetic algorithm. Comput. Ind., 2005, 56: 207-220
|
| [30] |
WongKY, Komarudin. Solving facility layout problems using flexible bay structure representation and ant system algorithm. Expert Syst. Appl., 2010, 37: 5523-5527
|
| [31] |
Yu-Hsin ChenG. A new data structure of solution representation in hybrid ant colony optimization for large dynamic facility layout problems. Int. J. Prod. Econ., 2013, 142: 362-371
|
| [32] |
ZoueinPP, KattanS. An improved construction approach using ant colony optimization for solving the dynamic facility layout problem. J. Oper. Res. Soc., 2022, 73: 1517-1531
|
RIGHTS & PERMISSIONS
Harbin Engineering University and Springer-Verlag GmbH Germany, part of Springer Nature