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A computing strategy called Double Track-Most Significant Operation First (DT-MSOF) is proposed. The goal of this strategy is to reduce computation time by reducing the number of operations that need to be executed, while maintaining a correct final result. Executions are conducted on a sequence of computing operations that have previously been sorted based on significance. Computation will only run until the result meets the needs of the user. In this study, the DT-MSOF strategy was used to modify the Analytic Hierarchy Process (AHP) algorithm into MD-AHP in order to reduce the number of operations that need to be done. The conventional AHP uses a run-to-completion approach, in which decisions can only be obtained after all of the operations have been completed. On the other hand, the calculations in MD-AHP are carried out iteratively only until the conditions are reached where a decision can be made. The simulation results show that MD-AHP can reduce the number of operations that need to be done to obtain the same results (decisions) as obtained by conventional AHP. It was also found that the more uneven the distribution of priority values, the more the number of operations could be reduced.

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