Computational intelligence in integrated airline scheduling by Tobias Grosche

By Tobias Grosche

An airline agenda represents the crucial making plans portion of each one airline. usually, the target of airline agenda optimization is to discover the airline agenda that maximizes working revenue. This making plans activity is not just an important but in addition the main complicated job an airline is faced with. beforehand, this activity is played through dividing the final making plans challenge into smaller and not more advanced subproblems which are solved individually in a series. besides the fact that, this technique is simply of teen potential to house interdependencies among the subproblems, leading to much less ecocnomic schedules than these being attainable with an process fixing the airline agenda optimization challenge in a single step. during this paintings, making plans methods for built-in airline scheduling are awarded. One technique follows the normal sequential procedure: current versions from literature for person subproblems are carried out and more advantageous in an total iterative regimen permitting to build airline schedules from scratch. the opposite making plans appraoch represents a very simultaneous airline scheduling: utilizing metaheuristics, airline schedules are processed and optimized right away with out a separation into varied optimization steps for its subproblems.

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For example, an automorphism φ fulfilling φ(a) = a, for some a ∈ ]0, 1[, φ(x) < x, whenever x < a, and x < φ(x), whenever a < x, cannot have an alternating behavior. The following theorem describes such automorphisms in terms of involutive negators. Theorem 16. (4; 10; 16; 22) 1. For every strict negator N there exist three involutive negators N1 , N2 and N3 such that N = N1 ◦ N2 ◦ N3 . 2. For every automorphism φ there exist four involutive negators N1 , N2 , N3 and N4 such that φ = N1 ◦ N2 ◦ N3 ◦ N4 .

The correlation tests for validation (Table 5) did not achieve the proposed goals due to the great non-linearity of the problem. The NN selected from the preferable set was N1, as it was the one who achieved the minimum value in 5 out of the 8 tests. E. M. M. Frazão Table 4. 23 144 17 8 135 14 9 128 15 8 171 18 9 126 20 6 Table 5. 120 Ree Re2e2 Fig. 11 shows the outputs of the N1 model in the generalization set. The same data was used to perform a comparison with a physical model, based on energy and mass balance integral-differential equations.

13) For a continuous binary aggregation function A the following assertions are equivalent: 1. A satisfies Eq. (5); 2. CaN ∈ Q(Da , M ), for every a ∈ [0, 1], and A(0, 1) = A(1, 0) = α; ˜ a , M −1 ), for every a ∈ [0, 1], and A(0, 1) = A(1, 0) = α. 3. C˜aN ∈ Q(D References 1. : On the structure of involutive, contracting and expanding negations. Fuzzy Sets and Systems 139, 661–672 (2003) 2. : Aggregation operators: properties, classes and construction methods. Studies in Fuzziness and Soft Computing, vol.

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