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Citation:
 Lijun Liu,Pu Cao,Yajing zhou,et al.Integrated Optimization Scheduling Model for Ship Outfitting Production with Endogenous Uncertainties[J].Journal of Marine Science and Application,2025,(1):194-209.[doi:10.1007/s11804-024-00454-x]
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Integrated Optimization Scheduling Model for Ship Outfitting Production with Endogenous Uncertainties

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Title:
Integrated Optimization Scheduling Model for Ship Outfitting Production with Endogenous Uncertainties
Author(s):
Lijun Liu1 Pu Cao1 Yajing zhou2 Zhixin Long1 Zuhua Jiang3
Affilations:
Author(s):
Lijun Liu1 Pu Cao1 Yajing zhou2 Zhixin Long1 Zuhua Jiang3
1. College of Mechanical & Electrical Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China;
2. School of management, Hefei University of Technology, Hefei 230009, China;
3. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Keywords:
Ship outfittingProduction schedulingPurchase planningEndogenous uncertaintyMultistage stochastic programming
分类号:
-
DOI:
10.1007/s11804-024-00454-x
Abstract:
Ship outfitting is a key process in shipbuilding. Efficient and high-quality ship outfitting is a top priority for modern shipyards. These activities are conducted at different stations of shipyards. The outfitting plan is one of the crucial issues in shipbuilding. In this paper, production scheduling and material ordering with endogenous uncertainty of the outfitting process are investigated. The uncertain factors in outfitting equipment production are usually decision-related, which leads to difficulties in addressing uncertainties in the outfitting production workshops before production is conducted according to plan. This uncertainty is regarded as endogenous uncertainty and can be treated as non-anticipativity constraints in the model. To address this problem, a stochastic two-stage programming model with endogenous uncertainty is established to optimize the outfitting job scheduling and raw material ordering process. A practical case of the shipyard of China Merchants Heavy Industry Co., Ltd. is used to evaluate the performance of the proposed method. Satisfactory results are achieved at the lowest expected total cost as the complete kit rate of outfitting equipment is improved and emergency replenishment is reduced.

References:

Akatsu S, Masuda A, Shida T, Tsuda K (2020) A study of quality indicator model of large-scale open source software projects for adoption decision-making. Procedia Computer Science 176: 3665-3672. DOI:https://doi.org/10.1016/j.procs.2020.09.020
Almatroushi H, Hariga M, As’Ad R, Al-Bar AR (2020) The multi resource leveling and materials procurement problem: an integrated approach. Engineering Construction & Architectural Management 27(9): 2135-2161. DOI:https://doi.org/10.1108/ECAM-10-2019-0563
Apap RM, Grossmann IE (2017) Models and computational strategies for multistage stochastic programming under endogenous and exogenous uncertainties. Computers and Chemical Engineering 103: 233-274. DOI:https://doi.org/10.1016/j.compchemeng.2016.11.011
Aquilano NJ, Smith DE (1980) A formal set of algorithms for project scheduling with critical path scheduling/material requirements planning. Journal of Operations Management 1(2): 57-67. DOI: 10.1016/0272-6963(80)90013-3
Asadujjaman M, Rahman HF, Chakrabortty RK, Ryan MJ (2021) Resource constrained project scheduling and material ordering problem with discounted cash flows. Computers and Industrial Engineering 158: 107427. DOI: 10.1016/j.cie.2021.107427
Bai S, Zhang Y, Li L, Shan N, Chen X (2021) Effective link prediction in multiplex networks: A TOPSIS method. Expert Systems with Applications 177: 114973. DOI:https://doi.org/10.1016/j.eswa.2021.114973
Bhuiyan TH, Medal HR, Harun S (2020) A stochastic programming model with endogenous and exogenous uncertainty for reliable network design under random disruption. European Journal of OperationalResearch 285(2): 670-694. DOI: 10.1016/j.ejor.2020.02.016
Bhuiyan TH, Moseley MC, Medal HR, Rashidi E, Grala RK (2019) A stochastic programming model with endogenous uncertainty for incentivizing fuel reduction treatment under uncertain landowner behavior. European Journal of Operational Research 277(2): 699-718. DOI:https://doi.org/10.1016/j.ejor.2019.03.003
Bruni ME, Beraldi P, Conforti D (2015) A stochastic programming approach for operating theatre scheduling under uncertainty. IMA Journal of Management Mathematics 26(1): 99-119. DOI:https://doi.org/10.1093/imaman/dpt027
Dodin B, Elimam AJIT (2001) Integrated project scheduling and material planning with variable activity duration and rewards 33(11): 1005-1018. DOI:https://doi.org/10.1023/A:1010994519405
Dong F, Deglise-Hawkinson J, Oyen MPV, Singer DJ (2013) Analytical approach to a two-stage queuing network for the planning of outfitting processes in shipbuilding. Journal of Ship Production and Design 29(3): 136-141. DOI:https://doi.org/10.5957/JSPD.29.3.120062
Dong F, Deglise-Hawkinson JR, Oyen MPV, Singer DJ (2016) Dynamic control of a closed two-stage queueing network for outfitting process in shipbuilding. Computers & Operations Research 72: 1-11. DOI:https://doi.org/10.1016/j.cor.2015.05.002
Ghadimi P, Toosi FG, Heavey C (2018) A multi-agent systems approach for sustainable supplier selection and order allocation in a partnership supply chain. European Journal of Operational Research, 269(1): 386-301. DOI:https://doi.org/10.1016/j.ejor.2017.07.014
Goel V, Grossmann IE (2004) A stochastic programming approach to planning of offshore gas field developments under uncertainty in reserves. Computers & Chemical Engineering 28(8): 1409-1429. DOI:https://doi.org/10.1016/j.compchemeng.2003.10.005
Günter H, Snoo CD, Shepherd C, Moscoso P, Riedel J (2011) Collaborative planning in supply chains: The importance of creating high quality relationships. Springer Berlin Heidelberg. DOI:https://doi.org/10.1007/978-3-642-13382-4_5
Higle JL, Sen S (1991) Stochastic decomposition: An algorithm for two-stage linear programs with recourse. Mathematics of Operations Research 16(3): 650-669. DOI:https://doi.org/10.1287/moor.16.3.650
Hooshmand F, Mirhassani SA, Akhvein A (2018) Adapting GA to solve a novel model for operating room scheduling problem with endogenous uncertainty. Operations Research for Health Care 19: 26-43. DOI:https://doi.org/10.1016/j.orhc.2018.02.002
Huo L, Wang JY (2022) Research on solving postdisaster material distribution and scheduling with improved NSGA-II algorithm. Computational Intelligence and Neuroscience 2022: 2529805. DOI:https://doi.org/10.1155/2022/2529805
Jia HZ, Fuh JYH, Nee AYC, Zhang YF (2007) Integration of genetic algorithm and Gantt chart for job shop scheduling in distributed manufacturing systems. Computers & Industrial Engineering 53(2): 313-320. DOI:https://doi.org/10.1016/j.cie.2007.06.024
Karanassos HA (2016) Ship’s Outfit: Group 4. Commercial Ship Surveying. DOI: 10.1016/B978-0-08-100303-9.00009-2
Keller B, Bayraksan G (2009) Scheduling jobs sharing multiple resources under uncertainty: A stochastic programming approach. Lie Transactions 42(1): 16-30. DOI: 10.1080/07408170902942683
Lee JH, Kim SH, Lee K (2012) Integration of evolutional BOMs for design of ship outfitting equipment. Computer-Aided Design 2012(3): 44. DOI:https://doi.org/10.1016/j.cad.2011.07.009
Leo E, Engell S (2022) Condition-based maintenance optimization via stochastic programming with endogenous uncertainty. Computers and Chemical Engineering 156: 107550. DOI:https://doi.org/10.1016/j.compchemeng.2021.107550
Li C, Grossmann IE (2021) A review of stochastic programming methods for optimization of process systems under uncertainty. Frontiers in Chemical Engineering 2: 622241. DOI: 10.3389/fceng.2020.622241
Li XT, Dai YX, Ge YX, Liu J, Shan Y, Duan LY (2022) Uncertainty modeling for out-of-distribution generalization. Computer Science arXiv:2202.03958. https://doi.org/10.48550/arXiv.2202.03958
Lucht T, Mutze A, Kmpfer T, Nyhuis P (2021) Model-based approach for assessing planning quality in production logistics. IEEE Access 2021(9): 1-13. DOI:https://doi.org/10.1109/ACCESS.2021.3104717
Menon KG, Fukasawa R, Ricardez-Sandoval LA (2021) A novel stochastic programming approach for scheduling of batch processes with decision dependent time of uncertainty realization. Annals of Operations Research 305(1): 163-190. DOI: 10.1007/s10479-021-04141-w
Mohammed A, Harris I, Govindan K (2019) A hybrid MCDM-FMOO approach for sustainable supplier selection and order allocation. International Journal of Production Economics 217: 171-184. DOI:https://doi.org/10.1016/j.ijpe.2019.02.003
Najafi AA, Zoraghi N, Azimi F (2011) Scheduling a project to minimize costs of material requirements. International Journal of Industrial and Manufacturing Engineering 5(6): 968-971. DOI: 10.13140/2.1.2098.3681
Pereira MVF, Pinto LMVG (1991) Multi-stage stochastic optimization applied to energy planning. Mathematical Programming 52: 359-375. DOI: 10.1007/BF01582895
Rahman HF, Nielsen I (2019) Scheduling automated transport vehicles for material distribution systems. Applied Soft Computing 82: 105552. DOI: 10.1016/j.asoc.2019.105552
Rose CD, Coenen JMG (2015) Comparing four metaheuristics for solving a constraint satisfaction problem for ship outfitting scheduling. International Journal of Production Research 53(19-20): 5782-5796. DOI:https://doi.org/10.1080/00207543.2014.998786
Sha Y, Zhang J, Cao H (2021) Multistage stochastic programming approach for joint optimization of job scheduling and material ordering under endogenous uncertainties. European Journal of Operational Research 290(3): 886-900. DOI: 10.1016/j.ejor.2020.08.0570
Sajadieh MS, Shadrokh S, Hassanzadeh F (2009) Concurrent project scheduling and material planning: A genetic algorithm approach. Scientia Iranica 16(2): 91-99. DOI:https://doi.org/10.1016/j.ssci.2008.10.016
Smith-Daniels DE, Smith-Daniels VL (1987) Optimal project scheduling with materials ordering. IIE Transactions 19(2): 122-129. DOI:https://doi.org/10.1080/07408178708975378
Smith-Daniels DE, Aquilano NJ (1984) Constrained resource project scheduling subject to material constraints. Journal of Operations Management 4(4): 369-387. DOI: 10.1016/0272-6963(84)90022-6
Tabrizi BH, Ghaderi SF (2016) A robust bi-objective model for concurrent planning of project scheduling and material procurement. Computers & Industrial Engineering 98: 11-29. DOI:https://doi.org/10.1016/j.cie.2016.05.017
Tabrizi BHJ (2018) Integrated planning of project scheduling and material procurement considering the environmental impacts. Computers & Industrial Engineering 120: 103-115. DOI: 1016/j.cie.2018.04.031
Tang L, Li F, Chen ZL (2019) Integrated scheduling of production and two-stage delivery of make-to-order products: Offline and online algorithms. Informs Journal on Computing 31(3): 493-514. DOI:https://doi.org/10.1287/ijoc.2018.0842
Wang G, Hu X, Li X, Zhang Y, Feng S, Yang A (2020) Multiobjective decisions for provider selection and order allocation considering the position of the CODP in a logistics service supply chain. Computers & Industrial Engineering 140: 106216.1-106216.15. DOI:https://doi.org/10.1016/j.cie.2019.106216.
Wang J, Zhu M, Fan X, Yin X, Zhou Z (2020) Multi-channel augmented reality interactive framework design for ship outfitting guidance. IFAC-Papers On Line 53(5): 189-196. DOI: 10.1016/j.ifacol.2021.04.098
Wassick JM, Agarwa A, Akiya N, Ferrio J, Bury S, You F (2012) Addressing the operational challenges in the development, manufacture, and supply of advanced materials and performance products. Computers & Chemical Engineering 47: 157-169. DOI: 10.1016/j.compchemeng.2012.06.041
Yazdaninejad M, Amjady N, Hatziargyriou ND (2021) Nested Bilevel Optimization for DERA Operation Strategy: A Stochastic Multiobjective IGDT Model With Hybrid Endogenous/Exogenous Scenarios. IEEE Systems Journal 15(4): 5495-5506. DOI:https://doi.org/10.1109/JSYST.2021.3085987
Zeng C, Tang J, Fan Z (2019) Auction-based approach for a flexible job-shop scheduling problem with multiple process plans. Engineering Optimization 51(11): 1902-1919. DOI: 10.1080/0305215X.2018.1561884
Zhang Y, Cui N (2021) Project scheduling and material ordering problem with storage space constraints. Automation in Construction 129(5): 103796. DOI: 10.1016/j.autcon.2021.103796
Zhou BH, Shen CY (2018) Multi-objective optimization of material delivery for mixed model assembly lines with energy consideration. Journal of Cleaner Production 192: 293-305. DOI: 10.1016/j.jclepro.2018.04.251

Memo

Memo:
Received date:2022-9-12;Accepted date:2024-1-17。
Foundation item:This work was supported in part by the High-tech ship scientific research project of the Ministry of Industry and Information Technology of the People’s Republic of China, and the National Nature Science Foundation of China (Grant No. 71671113), and the Science and Technology Department of Shaanxi Province (No. 2020GY- 219), the Ministry of Education Collaborative Project of Production, Learning and Research (No. 201901024016).
Corresponding author:Lijun Liu,E-mail:liulijun@sust.edu.cn
Last Update: 2025-02-26