A typical coast guard has complex responsibilities, subject to its multiple missions in maritime jurisdiction regions. These maritime jurisdiction regions can be partitioned into responsibility areas, and each type of coast guard vessel has a set of attributes for each responsibility area. The distribution of coast guard assets deployed in their respective areas of responsibility should be linked to the types of events occurring in these areas. The objective of this study is to determine the appropriate distribution of coast guard assets in various maritime zones; we do this by evaluating models that assign coast guard surface vessel types to missions in these maritime zones. We use the analytic network process model to compare various vessel types and determine the task-type weights for each vessel type. Weighted goal programming allows us to combine data, including the weights we derive from the use of other decision-aid tools. Our objective is to determine the best compromise solution for distributing coast guard assets into four main responsibility zones. The Turkish Coast Guard implemented the model in 2014, and decided to use it once a year prior to executing the main process in which it allocates coast guard assets.
ANP model to review the overall performance of the various alternatives and the interactions ANP with BOCR model to handle the complexities of real world problem Determining the priorities of the coast guard surface vessel types Search and rescue (SAR) at sea is one of the most common and extraordinarily important missions for the coast guard. Figure A. The flow diagram for the solution process based on ANP with BOCR model. Purpose: The purpose o this study is to propose a model based on Analytic Network Process (ANP) and Benefits, Opportunities, Costs and Risks (BOCR) to determine the SAR status and priorities of coast guard surface vessels in Turkish Coast Guard command. Theory and Methods: We use ANP with BOCR model in order to compare various boat types, and to determine the task type weights for each boat type for SAR missions. Results: This integrated approach allows us to find "dimensionless measurements" called as weights or priorities of the surface vessel types according to SAR task. This study is the first application in the literature that provides basic input using the ANP and BOCR approach for decision making problems for SAR missions. The results of the ANP with BOCR model can easily be adapted and used as input for other related decisionmaking problems, such as force structure, acquisition and (or) disposal of vessel types, reallocation of existing vessels, and dynamic deployment. The proposed models could also be used in the assignment process in other application areas in which multiple resources (e.g., coast guard vessels) have multiple roles. Conclusion: The more suitable boat types have been found for SAR missions by using the proposed ANP and BOCR approach. The proposed model allows decision makers to review the overall performance of the various boat types and the interactions. This model can also be adapted to handle the complexities of other real world problems.
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