Multi-year planning for optimal navigation channel dredging and dredged material management. (March 2022) - Bian, Zheyong; Bai, Yun; Douglas, W. Scott; Maher, Ali; Liu, Xiang
Highlights: This paper studies a problem of multi-year planning for optimal navigation channel dredging and dredged material management. A mixed integer programming model is developed to optimize the channel dredging and dredged material disposal activities. A heuristic algorithm, called the dynamic planning prioritization (DPP) algorithm, is proposed to solve large-scale problems due to the computational complexity. A real-world case study is proposed to illustrate the model results and demonstrate the effectiveness of the developed model and algorithm. Abstract: Navigation channels are critical maritime infrastructure that supports economic and recreational activities and intermodal freight supply chains, impacting broad areas of the hinterland. Maintenance of the maritime infrastructure is vital to the operations of the transportation system. This paper studies a strategic-level maritime infrastructure asset management problem: multi-year planning of navigation channel maintenance dredging and dredged material management. A mathematical model called dredging planning optimization model (DPOM) is developed to optimize channel dredging planning and dredged material management in confined disposal facilities (CDFs), accounting for practical constraints and considerations, including channel linkage and dependency, channel bundling, CDF accessibility and capacity, shoaling and navigability deterioration over time, reimbursable costs, channel economic values, etc. The problem is formulated as a mixed integer nonlinear programming (MINLP) model, with the objective of maximizing the total economic-value-weighted average navigability under fixed budget. The model can be reformulated as an equivalent mixed integer programming (MIP) model, which can be solved by the CPLEX solver, using the branch and bound algorithm. A heuristic algorithm called the dynamic planning prioritization (DPP) algorithm, is proposed in order to solve large-scale problems due to the computational complexity. DPP incorporates a dynamic ranking criterion to overcome the challenge of simultaneously handling all of the practical constraints and considerations, as well as the impact of channel prioritization on future year decisions. Finally, a real-world case study is proposed to illustrate the model results and demonstrate the effectiveness of the MIP model and the DPP algorithm.