| People | Locations | Statistics |
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| Mouftah, Hussein T. |
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| Dugay, Fabrice |
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| Rettenmeier, Max |
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| Tomasch, Ernst | Graz |
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| Cornaggia, Greta |
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| Palacios-Navarro, Guillermo |
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| Uspenskyi, Borys V. |
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| Khan, Baseem |
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| Fediai, Natalia |
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| Derakhshan, Shadi |
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| Somers, Bart | Eindhoven |
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| Anvari, B. |
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| Kraushaar, Sabine | Vienna |
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| Kehlbacher, Ariane |
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| Das, Raj |
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| Werbińska-Wojciechowska, Sylwia |
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| Brillinger, Markus |
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| Eskandari, Aref |
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| Gulliver, J. |
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| Loft, Shayne |
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| Kud, Bartosz |
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| Matijošius, Jonas | Vilnius |
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| Piontek, Dennis |
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| Kene, Raymond O. |
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| Barbosa, Juliana |
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Bocewicz, Grzegorz
in Cooperation with on an Cooperation-Score of 37%
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Publications (28/28 displayed)
- 2024Preventive planning of Product-as-a-Service offers to maintain the availability of required service levelcitations
- 2023Proactive Mission Planning of Unmanned Aerial Vehicle Fleets Used in Offshore Wind Farm Maintenancecitations
- 2023IDENTIFYING THE POTENTIAL OF UNMANNED AERIAL VEHICLE ROUTING FOR EMERGENCY BLOOD DISTRIBUTION
- 2023IDENTIFYING THE POTENTIAL OF UNMANNED AERIAL VEHICLE ROUTING FOR BLOOD DISTRIBUTION IN EMERGENCY REQUESTS
- 2023Location Suitability for the Implementation of Unmanned Aerial Vehicles in the Vaccine Supply Chain of Sri Lankacitations
- 2022UAVs' Dynamic Routing, Subject to Time Windows Variation
- 2021Reference model of milk-run traffic systems prototypingcitations
- 2021Ordered–Fuzzy-Numbers-Driven Approach to Out-Plant Milk-Run Dynamic Routing and Schedulingcitations
- 2020Unmanned aerial vehicle routing problems:A literature reviewcitations
- 2020A Proactive Approach to Resistant UAV Mission Planningcitations
- 2020Unmanned aerial vehicle routing problemscitations
- 2020Declarative UAVs Fleet Mission Planningcitations
- 2020Declarative UAVs Fleet Mission Planning:A Dynamic VRP Approachcitations
- 2020UAV mission planning resistant to weather uncertaintycitations
- 2019Energy consumption in unmanned aerial vehicles:A review of energy consumption models and their relation to the UAV routingcitations
- 2019Factors affecting energy consumption of unmanned aerial vehicles:An analysis of how energy consumption changes in relation to UAV routingcitations
- 2019Milk-run routing and scheduling subject to different pick-up/delivery profiles and congestion-avoidance constraintscitations
- 2019A decision support model for prototyping in-plant milk-run traffic systemscitations
- 2019Factors affecting energy consumption of unmanned aerial vehiclescitations
- 2019A solution approach for UAV fleet mission planning in changing weather conditionscitations
- 2019Energy consumption in unmanned aerial vehiclescitations
- 2019Planning deliveries with UAV routing under weather forecast and energy consumption constraintscitations
- 2019Reference model of a milk-run delivery problemcitations
- 2019Multimodal processes prototyping subject to grid-like network and fuzzy operation time constraintscitations
- 2017Reduction of congestion in transport networks with a fractal structurecitations
- 2016Multimodal processes optimization subject to fuzzy operation time constraintscitations
- 2016Planning of vessel speed and fuel bunkering over a route with speed limitscitations
- 2016Multimodal processes optimization subject to fuzzy operation time constraints:declarative modeling approachcitations
Places of action
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article
A solution approach for UAV fleet mission planning in changing weather conditions
Abstract
With a rising demand for utilizing unmanned aerial vehicles (UAVs) to deliver materials in outdoor environments, particular attention must be given to all the different aspects influencing the deployment of UAVs for such purposes. These aspects include the characteristics of the UAV fleet (e.g., size of fleet, UAV specifications and capabilities), the energy consumption (highly affected by weather conditions and payload) and the characteristics of the network and customer locations. All these aspects must be taken into account when aiming to achieve deliveries to customers in a safe and timely manner. However, at present, there is a lack of decision support tools and methods for mission planners that consider all these influencing aspects together. To bridge this gap, this paper presents a decomposed solution approach, which provides decision support for UAVs’ fleet mission planning. The proposed approach assists flight mission planners in aerospace companies to select and evaluate different mission scenarios, for which flight-mission plans are obtained for a given fleet of UAVs, while guaranteeing delivery according to customer requirements in a given time horizon. Mission plans are analyzed from multiple perspectives including different weather conditions (wind speed and direction), payload capacities of UAVs, energy capacities of UAVs, fleet sizes, the number of customers visited by a UAV on a mission and delivery performance. The proposed decision support-driven declarative model supports the selection of the UAV mission planning scenarios subject to variations on all these configurations of the UAV system and variations in the weather conditions. The computer simulation based experimental results, provides evidence of the applicability and relevance of the proposed method. This ultimately contributes as a prototype of a decision support system of UAVs fleet-mission planning, able to determine whether is it possible to find a flight-mission plan for a given fleet of UAVs guaranteeing customer satisfaction ...
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