Download Computational intelligence in integrated airline scheduling by Tobias Grosche PDF

By Tobias Grosche

An airline agenda represents the valuable making plans component of every one airline. more often than not, the target of airline agenda optimization is to discover the airline time table that maximizes working revenue. This making plans job isn't just an important but in addition the main advanced activity an airline is faced with. before, this job is played by means of dividing the final making plans challenge into smaller and no more advanced subproblems which are solved individually in a chain. even if, this process is barely of youngster strength to house interdependencies among the subproblems, leading to much less ecocnomic schedules than these being attainable with an strategy fixing the airline time table optimization challenge in a single step. during this paintings, making plans techniques for built-in airline scheduling are offered. One technique follows the normal sequential strategy: present types from literature for person subproblems are carried out and superior in an total iterative regimen permitting to build airline schedules from scratch. the opposite making plans appraoch represents a really simultaneous airline scheduling: utilizing metaheuristics, airline schedules are processed and optimized immediately with no separation into assorted optimization steps for its subproblems.

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Furthermore, requiring that A should also be self-dual leads to the following alternative characterization of invariant aggregation functions. Corollary 3. (10) An n-ary aggregation function A is invariant if and only if it is invariant under all involutive negators. Various characterizations of N -invariant aggregation functions, with N some involutive negator, have been proposed in (12). All of them invoke a [0, 1]2 → [0, 1] function C that allows to characterize the class of N -invariant aggregation functions in the following sense: A [0, 1]n → [0, 1] function A is an N -invariant aggregation function if and only if there exists an aggregation function B such that A(x1 , .

The former are essentially recursive implementations of off-line algorithms while in the latter case the off-line algorithms can be employed directly, with minor modifications. The approach followed here is the latter, the use of the off-line training method described in section 3, to a sliding window of data. Fig. 12. Evolution of the error. Fixed model. The following modifications were introduced to the off-line algorithm: • • • the data used for training, in instant k, is stored in a sliding window which is also updated in each instant, using a FIFO policy – the current pair input-target data replaces the oldest pair stored in the window; the initial values of the network parameters, for instant k, are the final values of the optimization conducted in instant k-1.

The second bisector. t. −id. t. a given monotone [0, 1] → [0, 1] bijection Φ. For instance, suppose that Φ contains part of a circle with center (x0 , y0 ) belonging to F . There does not exist a unique straight line perpendicular to Φ that contains (x0 , y0 ). To overcome this problem we have introduced in (11) the Φ-inverse of a set F : F Φ := {(x, y) ∈ [0, 1]2 | (Φ−1 (y), Φ(x)) ∈ F }. This definition has the following geometrical interpretation. Through every point (x, y) ∈ F we draw a line parallel to the X-axis and a line parallel to the Y-axis.

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