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Problem instance summary ns: AircraftGroupGreedy. Experimental results All experiments are executed on an Intel Core i7- [22], crew pairing is often part of proposed processor 2.

Reported problem sizes e. The algorithm is given as a number of flights are generally rather low with less single-threaded Java implementation. Used memory than flights and less than 10 aircraft types. The was below 1GB in all cases. Our experiments include large Throughout all instances, we observe a quick instances that are solved in a reasonable time 73, increase for the solution quality which implies that flights, 10 aircraft types.

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The suitability of the the algorithm finds the good solutions at an early algorithm for such large instances results from using stage. Results in Table 3 show that the robustness is meta-heuristics. Other authors employing heuristics high with an average of 0. AustralianTwo with a value of 0.

This can result from an inappropriate set of available aircraft types. The One major contribution is the flight plan number of deadhead flights is rather low ranging generator and visualization suite ARPViS.

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Availability of such a expenses and maximum revenues employing the library is of major interest in order to compare most efficient aircraft types demonstates that positive algorithms and define requirements for problem revenues are out of reach. Results show for this test instances. We aim to fill this gap by creating a test instance a remarkably high percentage of deadhead instance generator that considers several parameters, flights The reason for this behavior might be the density, maximum number of aircraft slots per low flight density of the test instance.

An increase in airport, aircraft type count, number of maintenance used aircraft would raise parking expenses to a stations per aircraft, fleet size, and maximum greater extend than diminishing deadhead flight. We employ real-world input, if available, from different sources, e. Nevertheless, the validity of the Over the last years, several authors proposed new results depends on the parameter settings and requires algorithms to solve the combined fleet assignment individual verification.

Currently, we do not consider and ARP. We used the with maintenance constraints that might not be an generator to create eight instances regarding different issue anymore. Our proposed algorithm solves test specifications as an initial set for a library; see Figure instances with more than 73, flights, more than 10 12 for a visualization of the flight plan Europe with aircraft types, and deadhead flights to improve profits 30, flights and 20 airports.

The instances are and to consider all levels of maintenance further visualized using ARPViS [4], a tool that allows for regarding robustness of solutions. Robustness is of depicting special parameters of the flight plan, e. The connections, etc. The runtime for very large instances is still below an hour understanding of the flight plan is enhanced by and, therefore, applicable during the planning stage in underlying maps as well as interactive elements to order to evaluate various flight plans.

A further project results of individual flight legs. The planer is substantial contribution is the presented test instance further supported, e. Our intention of the generator and the eight instances used in this paper is the creation of a test instance library to allow for effective comparison of algorithms in the future.

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Currently, such a library does not exist. Finally, our visualization tool supports handling flight plans and interpretation of solutions. Website, December Afsar, M. Espinouse, and B. Buidling flight planning for an airline company under maintenance constraints. Journal of Quality in Maintenance Engineering, 15 4 , Figure Flight plan and result presentation [3] S. Ahmad-Beygi, A.

Computational Intelligence in Integrated Airline Scheduling

Cohn, and M. Decreasing 7.

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IIE Transactions, 42 7 , Airline planners have to solve complex tasks. We discuss the current state-of-the-art for the extended [4] T. Reiners, J. Pahl, M. Maroszek, C. Technical Report. We concentrate on the development of heuristics, thus leaving [5] C. Barnhart, N. Boland, L. Clarke, E. Johnson, mathematical problem formulation to a later stage.

Nemhauser, and R. Flight string models The major drawback of published algorithms is for aircraft fleeting and routing.

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Transportation Science, the limited size of considered test instances and the 32 3 : , With instances containing less than 3, flights, only small airlines [6] J. OR-Library: Distributing test problems by electronic mail. Journal of Operational Research might use related algorithms and software. Society, , Department of [19] S. Lan, J. Clarke, and C.

Planning for Transportation. Transportation Science, 40 1 , Department of Transportation. Average delay for the Newark International [20] M. Lohatepanont and C. Airline Schedule Airport and Continental Airlines between Website generated , March b. Department of [21] L. Marla and C. Robust optimization: Transportation. Website generated , March c. Johnson, G.

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Nemhauser, and Z. The [22] J. Rosenberger, E. Johnson, and G. Annals of Operations Research, Nemhauser. Isolation and Short Cycles. Transportation Science, , Cook and G.

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Rushmeier and S. Transportation marginal delay costs. Monograph, Transport Studies Science, 31 2 , Group, University of Westminster, London, Sandhu and D. Integrated Airline Fleeting [12] G. Desaulniers, J.