Therefore, this algorithm cannot achieve the optimal network configuration parameter under temporary traffic fluctuations.
Therefore, a general two-stage quantitative framework that enables decision makers to select the optimal network design scheme for CLNs under uncertainty is proposed in this paper.
For the Lilrb4 network the optimal network output is exactly recapitulated by the CAL network.
The optimal network was identified applying the CoD approach.
Methodologically, it also provides a more comprehensive method for inferring networks with predefined error rate and introduces the possibility of calculating the optimal networks under the MIT score adapted to handle continuous variables as well as discrete ones.
Under the condition of changing in demand, this may lead to large minimum cost deviation between the designed optimal network and real optimal network, respectively.
The first method, termed as SO-relaxation, exploits the property that an optimal network design solution under SO principle can be a good approximate solution under UE principle, and successively sorts the solutions in the order of increasing total travel time under SO principle.
Computational experiments considering different scenarios are conducted to obtain the new optimal networks under different cases.
In this paper we propose a model for the design of an optimal network integration of multisite refinery and petrochemical systems under uncertainty.
Third, for each scenario, we study throughput scaling behaviour of the secondary network under the optimal power allocation policy at the SBS.
