1,692 research outputs found

    Optimization of Vehicle Routing Problem with Tight Time Windows, Short travel time and Re-used Vehicles (VRPTSR) for Aircraft Refueling in Airport Using Ant Colony Optimization Algorithm

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    Scheduling in aircraft refueling has an important role in aviation. Scheduling of aircraft refueling is called Airport Ground Service Scheduling (AGSS) that can be formulated as Vehicle Routing Problem with Tight time windows, Short travel time and Re-used Vehicles (VRPTSR) This research is focusing in scheduling design for aircraft refueling with refueller truck in Juanda Airport, Surabaya, so minimum amount of truck will be used using Ant Colony optimization. The result shows that Ant Colony optimization could do scheduling in refueling well so minimum amount of truck will be used

    Optimal scheduling for refueling multiple autonomous aerial vehicles

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    The scheduling, for autonomous refueling, of multiple unmanned aerial vehicles (UAVs) is posed as a combinatorial optimization problem. An efficient dynamic programming (DP) algorithm is introduced for finding the optimal initial refueling sequence. The optimal sequence needs to be recalculated when conditions change, such as when UAVs join or leave the queue unexpectedly. We develop a systematic shuffle scheme to reconfigure the UAV sequence using the least amount of shuffle steps. A similarity metric over UAV sequences is introduced to quantify the reconfiguration effort which is treated as an additional cost and is integrated into the DP algorithm. Feasibility and limitations of this novel approach are also discussed

    A Multiple Ant Colony Metaheuristic for the Air Refueling Tanker Assignment Problem

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    The performance of the Nuclear Facility (NFAC) incident module in modeling a nuclear reactor accident is evaluated. Fallout predictions are compared with air concentration measurements of I-131 in Europe over a five-day period. Two categories of source term specifications are used: NFAC-generated source terms based on plant conditions and accident severity, and user-defined source terms based on specifying the release of I-131. The Atmospheric Transport Model Evaluation Study report source term provided the needed detailed release information. The Air Force Combat Climatology Center provided weather data covering Europe during the release\u27s 11-day duration. For the NFAC-generated source terms as few as 20% and as many as 52% of the values are within the intended accuracy, depending on which source term specification was selected. For the user-defined source terms, values ranged 35% to 56% being within the intended accuracy, again depending on which source term specification was used. Performance improved in all cases for monitoring sites closest to Chernobyl, with up to 87% of the values falling within the intended accuracy. This indicates there may be a limit for selecting the spatial domain, making HPAC more useful as a tool for smaller spatial domains, rather than on a continental scale

    Improving the Air Mobility Command\u27s Air Refueler Route Building Capabilities

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    We consider the problem of routing an aircraft (receiver) from a starting location to a target and back to an ending location while maintaining a fuel level above a predetermined level during all stages of the route and avoiding threat and no-fly zones. The receiver is routed to air refueling locations to refuel as required. The development of the network requires the processing of threat and no-fly zones to create the set of nodes that includes the bases (starting and end locations), the targets, and air refueling locations in addition to the restricted zone nodes. We develop a greedy heuristic that builds the route using arc paths and the on board fuel level to determine the termination of each sequential arc path. Post processing of the routes reduces the fuel remaining on board by shifting the time at target or reversing the route. The results from the greedy heuristic are compared to the results from the current methodology and show that the heuristic requires less time to produce routes that require less fuel

    A Two-Stage Approach for Routing Multiple Unmanned Aerial Vehicles with Stochastic Fuel Consumption

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    The past decade has seen a substantial increase in the use of small unmanned aerial vehicles (UAVs) in both civil and military applications. This article addresses an important aspect of refueling in the context of routing multiple small UAVs to complete a surveillance or data collection mission. Specifically, this article formulates a multiple-UAV routing problem with the refueling constraint of minimizing the overall fuel consumption for all of the vehicles as a two-stage stochastic optimization problem with uncertainty associated with the fuel consumption of each vehicle. The two-stage model allows for the application of sample average approximation (SAA). Although the SAA solution asymptotically converges to the optimal solution for the two-stage model, the SAA run time can be prohibitive for medium- and large-scale test instances. Hence, we develop a tabu-search-based heuristic that exploits the model structure while considering the uncertainty in fuel consumption. Extensive computational experiments corroborate the benefits of the two-stage model compared to a deterministic model and the effectiveness of the heuristic for obtaining high-quality solutions.Comment: 18 page

    Solution Repair/Recovery in Uncertain Optimization Environment

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    Operation management problems (such as Production Planning and Scheduling) are represented and formulated as optimization models. The resolution of such optimization models leads to solutions which have to be operated in an organization. However, the conditions under which the optimal solution is obtained rarely correspond exactly to the conditions under which the solution will be operated in the organization.Therefore, in most practical contexts, the computed optimal solution is not anymore optimal under the conditions in which it is operated. Indeed, it can be "far from optimal" or even not feasible. For different reasons, we hadn't the possibility to completely re-optimize the existing solution or plan. As a consequence, it is necessary to look for "repair solutions", i.e., solutions that have a good behavior with respect to possible scenarios, or with respect to uncertainty of the parameters of the model. To tackle the problem, the computed solution should be such that it is possible to "repair" it through a local re-optimization guided by the user or through a limited change aiming at minimizing the impact of taking into consideration the scenarios
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