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Previous researchers have developed various DFP methods for different problem settings.
Two case studies for different problem settings are used for demonstrating the functionalities of the method and the results that are obtained from it.
Nevertheless, MSA already offers a powerful approach for different problem settings (e.g., the vehicle routing problem with stochastic customers).
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The results obtained by the application of different problem settings are compared and discussed.
Then, we report the experimental results on the efficiency tests using different problem settings on different datasets.
It requires different solutions for different problems".
Different data structures are suited for different problems.
It does not have different divisions for different problems.
Then, numerical experiments under different problem parameters' settings are used to evaluate the performance of the proposed algorithm.
A number of idealizations of the process model are investigated and shown to be suitable for appropriate problem settings.
The proposed method is successfully experienced and validated on test instances reproducing different network topologies and problem settings.
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