At present, from the situation and trend that the three major domestic oil giants have successively gone abroad to participate in project construction in overseas countries, the importance of the centralized control(CC) system of offshore engineering(OE) is increasingly prominent. It has become an inevitable trend for the CC and integration of offshore oil engineering to enter informatization and standardization. At the same time, it is also one of the important topics facing the offshore oil engineering industry. This paper designs and analyzes the centralized management and control system of OE based on dynamic programming algorithm(DPA). By analyzing the problems existing in the management of OE, this paper discusses the framework principles and processes of the development of the centralized management and control system of OE, analyzes the application of DPA in the centralized management and control of OE, and then designs the centralized management and control system, which greatly improves the development efficiency and security of OE.

Vempaty Velmurugan. Centralized Management and Control System of Offshore Engineering based on Dynamic Programming Algorithm. Frontiers in Ocean Engineering (2021), Vol. 2, Issue 3: 46-54. https://doi.org/10.38007/FOE.2021.020306.

[1] Sazzed S , Scheible P , He J , et al. Tracing Filaments in Simulated and Experimental 3D Cryo-Electron Tomography Maps Using a Fast Dynamic Programming Algorithm. Microscopy and Microanalysis, 2021, 27(S1):3236-3237. https://doi.org/10.1017/S1431927621011156

[2] Beuchat P N , Warrington J , Lygeros J . Accelerated Point-wise Maximum Approach to Approximate Dynamic Programming. IEEE Transactions on Automatic Control, 2021, PP(99):1-1. https://doi.org/10.1109/TAC.2021.3050440

[3] Karpov D A , Struchenkov V I . Dynamic programming in applied tasks which are allowing to reduce the options selection. Russian Technological Journal, 2020, 8(4):96-111. https://doi.org/10.32362/2500-316X-2020-8-4-96-111

[4] Mh A , Jw B , Jl B . A moment and sum-of-squares extension of dual dynamic programming with application to nonlinear energy storage problems. European Journal of Operational Research, 2020, 283(1):16-32. https://doi.org/10.1016/j.ejor.2019.10.041

[5] Hajiamini S , Shirazi B , Crandall A , et al. A Dynamic Programming Framework for DVFS-Based Energy-Efficiency in Multicore Systems. IEEE Transactions on Sustainable Computing, 2020, 5(1):1-12. https://doi.org/10.1109/TSUSC.2019.2911471

[6] Ribault A , Vercraene S , Henry S , et al. Economically Optimal Control of a Cold Room Using an Artificial Neural Network and Dynamic Programming. IFAC-PapersOnLine, 2019, 52(13):2002-2007. https://doi.org/10.1016/j.ifacol.2019.11.497

[7] Houari B , Mohammed L , Hamid B , et al. Solution of Economic Load Dispatch Problems Using Novel Improved Harmony Search Algorithm. International Journal on Electrical Engineering and Informatics, 2021, 13(1):218-241. https://doi.org/10.15676/ijeei.2021.13.1.13

[8] Cote P , Arsenault R . Efficient Implementation of Sampling Stochastic Dynamic Programming Algorithm for Multireservoir Management in the Hydropower Sector. Journal of Water Resources Planning and Management, 2019, 145(4):05019005.1-05019005.8. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001050

[9] Wei Q , Wu Y . Dynamic Programming Algorithms for Two- Machine Hybrid Flow-Shop Scheduling with a Given Job Sequence and Deadline. IEEE Access, 2020, PP(99):1-1. https://doi.org/10.1109/ACCESS.2020.2993857

[10] Banias O . Test case selection-prioritization approach based on memoization dynamic programming algorithm. Information and software technology, 2019, 115(Nov.):119-130. https://doi.org/10.1016/j.infsof.2019.06.001

[11] Al-Dabooni S , Wunsch D C . An Improved N-Step Value Gradient Learning Adaptive Dynamic Programming Algorithm for Online Learning. IEEE Transactions on Neural Networks and Learning Systems, 2019, PP(99):1-15.

[12] Kira A , Inakawa K , Fujita T . A Dynamic Programming Algorithm For Optimizing Baseball Strategies. Journal of the Operations Research Society of Japan, 2019, 62(2):64-82. https://doi.org/10.15807/jorsj.62.64

[13] Salazar A , Berzoy A , Song W , et al. Energy Management of Islanded Nanogrids Through Nonlinear Optimization Using Stochastic Dynamic Programming. IEEE Transactions on Industry Applications, 2020, 56(3):2129-2137. https://doi.org/10.1109/TIA.2020.2980731

[14] Beppu H , Maruta I , Fujimoto K . Approximate Dynamic Programming with Gaussian Processes for Optimal Control of Continuous-Time Nonlinear Systems. IFAC-PapersOnLine, 2020, 53(2):6715-6722. https://doi.org/10.1016/j.ifacol.2020.12.098

[15] Fairbank M , Prokhorov D , Alonso E . Optimal Torque Control of Permanent Magnet Synchronous Motors Using Adaptive Dynamic Programming. Neural Networks and Learning Systems, IEEE Transactions on, 2019, 25(10):1909-1920. https://doi.org/10.1109/TNNLS.2014.2297991

[16] Tamaki H . Positive-instance driven dynamic programming for treewidth. Journal of combinatorial optimization, 2019, 37(4):1283–1311. https://doi.org/10.1007/s10878-018-0353-z

[17] Falsafain H , Tamannaei M . A Novel Dynamic Programming Approach to the Train Marshalling Problem. IEEE Transactions on Intelligent Transportation Systems, 2019, PP(99):1-10.

[18] Hsu H P , Wang C N . A Hybrid Approach Combining Improved Shuffled Frog-leaping Algorithm with Dynamic Programming for Disassembly Process Planning. IEEE Access, 2021, PP(99):1-1. https://doi.org/10.1109/ACCESS.2021.3072831