International Journal of Advanced Multidisciplinary Research and Studies
Volume 4, Issue 6, 2024
An Integrated Planning Model for Coordinating Subsea Surveillance, Inspection, and Workover Operations Using Vessel Scheduling Constraints
Author(s): Malvern Iheanyichukwu Odum, Iduate Digitemie Jason, Dazok Donald Jambol
DOI: https://doi.org/10.62225/2583049X.2024.4.6.4616
Abstract:
Efficient coordination of subsea surveillance, inspection, and workover operations is critical to maintaining offshore asset integrity and optimizing operational costs. This paper presents an integrated planning model that addresses the complexities of subsea task scheduling while incorporating vessel availability and logistical constraints. The model consolidates task-specific requirements, vessel transit times, maintenance cycles, and inter-task dependencies into a unified framework that optimizes resource utilization and minimizes operational delays. Through the application of advanced scheduling logic and task clustering, the model improves vessel utilization by reducing idle time and aligning tasks based on geographic proximity and urgency. The framework also balances operational efficiency with planning flexibility, allowing adaptive responses to dynamic offshore conditions such as weather disruptions and vessel maintenance. Validation against industry benchmarks demonstrates the model’s effectiveness in enhancing subsea operation coordination, reducing scheduling conflicts, and supporting informed decision-making. Future extensions include integration with autonomous underwater platforms, real-time re-planning capabilities, and regional asset coordination to enhance offshore logistics and operational resilience further. This integrated approach represents a significant advancement in offshore planning systems, offering a practical tool to optimize complex subsea workflows within stringent vessel scheduling constraints.
Keywords: Subsea Operations, Vessel Scheduling, Integrated Planning Model, Offshore Logistics, Task Coordination, Resource Optimization
Pages: 2570-2578
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