Optimization solutions for self-propelled modular transporter (SPMT) load-outs based on ballast simulation

H Zhu, Z Du, D Xu, Y Tang - Ocean Engineering, 2020 - Elsevier
H Zhu, Z Du, D Xu, Y Tang
Ocean Engineering, 2020Elsevier
This paper focuses on the optimization of self-propelled modular transporter (SPMT) load-
outs. In common practice, a series of independent sub-systems are usually established
based on the idea of multi-tasking. However, the conventional strategy features weak
capability of finding globally optimal ballast solution thus has inefficient performance in
actual applications. In this study, we developed a new ballast strategy with a unified ballast
system. The strategy dynamically allocates ballast resources and compensates cargo loads …
Abstract
This paper focuses on the optimization of self-propelled modular transporter (SPMT) load-outs. In common practice, a series of independent sub-systems are usually established based on the idea of multi-tasking. However, the conventional strategy features weak capability of finding globally optimal ballast solution thus has inefficient performance in actual applications. In this study, we developed a new ballast strategy with a unified ballast system. The strategy dynamically allocates ballast resources and compensates cargo loads as well as tide variation together by taking globally optimized ballast actions. On the basis of the proposed strategy, we established the approaches of ballast trials simulation, operation duration prediction and feasibility assessment for load-out plans. Then a simulation-based optimization method was developed to lower the time cost and prevent loading failures. In demonstration cases, the proposed ballast strategy was tested and proved to be able to reduce the time consumption by up to three quarters. The optimization method was applied in finding appropriate start time, barge and pumping capacity for particular projects. The above-mentioned optimization solutions can enhance the existing analysis workflow and help engineers make informed decisions.
Elsevier
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