Determining the optimal method to upgrade shipyard infrastructures for overhaul of offshore drilling rigs using genetic algorithm: A case study for ISOICO
Restricted accessResearch articleFirst published online August, 2022
Determining the optimal method to upgrade shipyard infrastructures for overhaul of offshore drilling rigs using genetic algorithm: A case study for ISOICO
The purpose of this research is to select the optimal combination of infrastructure and equipment to upgrade a shipbuilding yard to repair offshore drilling rigs considering installation time and cost of the equipment needed and the overhaul cost and time of a rig. These four components constitute a four-objective problem. To solve this problem, a list of main equipment with various capacities and installation costs was developed. Then the cost and time of overhaul of drilling rigs, as well as the installation time of equipment were extracted using the Delphi method. Finally, the SPEA2 algorithm was employed to analyze the data. An optimal Pareto combination of equipment was obtained at a cost of $242.6 million, which can be provided in a maximum period of 200 months. The findings suggest the optimum way to equip shipyards for the overhaul of drilling rigs based on time and cost optimization for both rig and yard owners.
BerumentMHCeylanNBDoganN.The impact of oil price shocks on the economic growth of selected MENA1 countries. Energy J2010; 31(1): 149–176.
2.
SadeghiK.An overview of design, analysis, construction and installation of offshore petroleum platforms suitable for Cyprus oil/gas fields. GAU Journal of Soc. & Applied Sciences2007; 2(4): 1–16.
3.
GhodratiAGRokneddinKKarimpourFM, et al. Seismic hazard assessment of southern Iran dry docks. In Proceeding of the First European conference on earthquake engineering and seismology, Geneva, Switzerland, 2006.
4.
KaiserMJSnyderB.The five offshore drilling rig markets. Mar Pol2013; 39: 201–214.
5.
MotamedPMajrouhiJ.A framework to select the best strategy for Iran’s entry into the repair and maintenance market of oil drilling rigs. Civ Eng J2018; 4(11): 2702–2718.
6.
TaghizadeKHeidariANoorzaiE.Environmental impact profiles for glazing systems: strategies for early design process. J Archit Eng2019; 25(2): 04019005.
7.
Gharouni JafariKNoorzaiEMakkiabadiSR, et al. Provide a model to select proper delivery system for railway projects In Iran. ISAHP 2014 Conference 2014, pp.1–14. DOI: 10.13033/isahp.y2014.140
8.
NoorzaiE.Public–Private partnership risks in conflict zones and solutions: case study for Afghanistan. J Infrastruct Syst2021; 27(1): 05021001.
9.
StanićVFafandjelNMatuljaT.A methodology for improving productivity of the existing shipbuilding process using modern production concepts and the AHP method. Brodogradnja: Teorija i praksa brodogradnje i pomorske tehnike2017; 68(3): 37–56.
10.
TalbiE-G.Metaheuristics: from design to implementation. Hoboken, NJ: John Wiley & Sons, 2009.
11.
ZvaigzneABondarenkoOBoikoA.Decision support system on the base of genetic algorithm for optimal design of a specialized maritime platform. Comput. Model. New Technol2017; 21(2): 11–18.
12.
TempleDColletteM.Understanding lifecycle cost trade-offs for naval vessels: minimising production, maintenance, and resistance. Ships Offshore Struct2017; 12(6): 756–766.
13.
DadkaniPNoorzaiEGhanbariA, et al. Risk analysis of Gas leakage in Gas pressure reduction station and its consequences: a case study for Zahedan. Heliyon2021; 7(5): e06911.
14.
KaiserMJSnyderBF.Newbuild and replacement cost functions. In: The offshore drilling industry and rig construction in the Gulf of Mexico. London: Springer, 2013, pp.209–222.
15.
MorandiACSmithIAVirkGS.Reliability of jack-ups under extreme storm conditions. Mar Struct2001; 14(4–5): 523–536.
16.
LiuXZhengJFuJ, et al. Optimal inspection planning of corroded pipelines using BN and GA. J Pet Sci Eng2018; 163: 546–555.
17.
PisanòFSchipperRSchreppersG-J.Input of fully 3D FE soil-structure modelling to the operational analysis of jack-up structures. Mar Struct2019; 63: 269–288.
18.
Mortezaei FarizhendyMNoorzaiEGolabchiM. Implementing the NSGA-II genetic algorithm to select the optimal repair and maintenance method of jack-up drilling rigs in Iranian shipyards. Journal of Ocean Engineering2020; 211: 107548.
19.
Mortezaei FarizhendyMGolabchiMNoorzaiE. Identifying the required infrastructure to improve the jack-up maintenance process based on effective criteria in the Persian Gulf. J Facil Manag2021; 19: 263–283.
20.
SohrabiHNoorzaiE.Risk assessment in Iranian oil and Gas construction industry: a process approach. Int J Qual Reliab Manag. Epub ahead of print 2021; DOI: 10.1108/IJQRM-03-2021-0069
21.
PerronsRKRichardsMG.Applying maintenance strategies from the space and satellite sector to the upstream oil and gas industry: a research agenda. Energy Policy2013; 61: 60–64.
22.
KaiserMJ.The role of factor and activity-based models in offshore operating cost estimation. J Pet Sci Eng2019; 174: 1062–1092.
23.
NamSShenHRyuC, et al. SCP-Matrix based shipyard APS design: application to long-term production plan. Int J Nav Archit Ocean Eng2018; 10(6): 741–761.
24.
LiJSunMHanD, et al. A governance platform for multi-project management in shipyards. Comput Ind Eng2018; 120: 179–191.
25.
CaoYGZhangSH.Failure analysis of a pinion of the jacking system of a jack-up platform. Eng Fail Anal2013; 33: 212–221.
26.
FriezePAMorandiACBirkinshawM, et al. Fixed and jack-up platforms: basis for reliability assessment. Mar Struct1997; 10(2-4): 263–284.
27.
ZhaoJDuanMZhangA, et al. Bearing capacity of square spudcan of jack-up rig based on a three-dimensional failure mechanism. Ships Offshore Struct2014; 9(2): 149–160.
28.
RagniRWangDMašínD, et al. Numerical modelling of the effects of consolidation on jack-up spudcan penetration. Comput Geotech2016; 78: 25–37.
29.
HuntRJMarshPD.Opportunities to improve the operational and technical management of jack-up deployments. Mar Struct2004; 17(3–4): 261–273.
30.
ZhangJKohCGTrinhTN, et al. Identification of jack-up spudcan fixity by an output-only substructural strategy. Mar Struct2012; 29(1): 71–88.
31.
KuNHaS.Dynamic response analysis of heavy load lifting operation in shipyard using multi-cranes. Ocean Eng2014; 83: 63–75.
32.
HaSKuN-KRohM-I, et al. Multibody system dynamics simulator for process simulation of ships and offshore plants in shipyards. Adv Eng Softw2015; 85: 12–25.
33.
BarlasBIzciFB.Individual and workplace factors related to fatal occupational accidents among shipyard workers in Turkey. Saf Sci2018; 101: 173–179.
34.
ShabakhtyNBoonstraHVan GelderP.System reliability of jack-up structures based on fatigue degradation. In: BedfordTvan GelderP (eds) Safety and reliability. Lisse: Swets & Zeitlinger, 2003, pp.1437–1780.
35.
TianMParkHKooH, et al. Impact of work boots and load carriage on the gait of oil rig workers. Int J Occup Saf Ergon2017; 23(1): 118–126.
36.
MattoranoDAMerínarT.Respiratory protection on offshore drilling rigs. Appl Occup Environ Hyg1999; 14(3): 141–148.
37.
CelebiUBVardarN.Investigation of VOC emissions from indoor and outdoor painting processes in shipyards. Atmos Environ2008; 42(22): 5685–5695.
XiaBChanAPC. Measuring complexity for building projects: a Delphi study. Eng Construct Architect Manag2012; 19(1): 7–24.
40.
Gharouni JafariKNoorzaiE. Selecting the most appropriate project manager to improve the performance of the occupational groups in road construction projects in Warm regions. J Constr Eng Manag2021; 147(10): 04021131.
41.
Gharouni JafariKNoorzaiEHosseiniMR. Assessing the capabilities of computing features in addressing the most common issues in the AEC industry. Constr Innov2021; 21(4): 875–898.
42.
ManoliadisOTsolasINakouA.Sustainable construction and drivers of change in Greece: a Delphi study. Constr Manag Econ2006; 24(2): 113–120.
43.
NoorzaiEGharouni JafariKMoslemi NaeniL.Lessons learned on selecting the best mass housing method based on performance evaluation criteria in Iran. Int J Constr Educ Res. Epub ahead of print 2020.
44.
EdmundsH.The Focus Group research handbook. Chicago, IL: NTC Business Books, 1999.
ZitzlerELaumannsMBleulerS.A tutorial on evolutionary multi-objective optimization. In: GandibleuxXSevauxMSörensenK (eds) Metaheuristics for multi-objective optimization. Berlin, Heidelberg: Springer, 2004, pp.3–37.
59.
MouraMDCLinsIDDroguettEL, et al. A multi-objective genetic algorithm for determining efficient risk-based inspection programs. Reliab Eng Syst Saf2015; 133: 253–265.
