Synthetic Dataset Generation for Optimizing Multimodal Drone Delivery Systems
Street delivery faces significant challenges due to outdated road infrastructure, which was not designed to handle current vehicle volumes, leading to congestion and inefficiencies, especially in last-mile delivery. Integrating drones into the delivery system offers a promising solution by bypassing...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/12/724 |
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| _version_ | 1846105012902559744 |
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| author | Diyar Altinses David Orlando Salazar Torres Asrat Mekonnen Gobachew Stefan Lier Andreas Schwung |
| author_facet | Diyar Altinses David Orlando Salazar Torres Asrat Mekonnen Gobachew Stefan Lier Andreas Schwung |
| author_sort | Diyar Altinses |
| collection | DOAJ |
| description | Street delivery faces significant challenges due to outdated road infrastructure, which was not designed to handle current vehicle volumes, leading to congestion and inefficiencies, especially in last-mile delivery. Integrating drones into the delivery system offers a promising solution by bypassing congested roads, thereby enhancing delivery speed and reducing infrastructure strain. However, optimizing this multimodal delivery system is complex and data-driven, with real-world data often being costly and restricted. To address this, we propose a synthetic dataset generator that creates diverse and realistic delivery scenarios, incorporating environmental variables, customer profiles, and vehicle characteristics. The key contribution of our work is the development of a dynamic generator for multiple optimization problems with diverse complexities or even combinations of optimization problems. This generator allows for the incorporation of real-world factors such as traffic congestion and synthetically generated factors such as wind conditions and communication constraints, as well as others. The primary objective is to establish a foundation for creating benchmark scenarios that enable the comparison of existing and new approaches. We evaluate the generated dataset by applying it to three optimization problems, including facility location, vehicle routing, and path planning, using different techniques to demonstrate the dataset’s effectiveness and operational viability. |
| format | Article |
| id | doaj-art-a63d8a5efdfc47afb7fd3891bf35f6b5 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-a63d8a5efdfc47afb7fd3891bf35f6b52024-12-27T14:21:46ZengMDPI AGDrones2504-446X2024-11-0181272410.3390/drones8120724Synthetic Dataset Generation for Optimizing Multimodal Drone Delivery SystemsDiyar Altinses0David Orlando Salazar Torres1Asrat Mekonnen Gobachew2Stefan Lier3Andreas Schwung4Department of Automation Technology and Learning Systems, South Westphalia University of Applied Sciences, 59494 Soest, GermanyDepartment of Automation Technology and Learning Systems, South Westphalia University of Applied Sciences, 59494 Soest, GermanyDepartment of Logistics and Supply Chain Management, South Westphalia University of Applied Sciences, 59872 Meschede, GermanyDepartment of Logistics and Supply Chain Management, South Westphalia University of Applied Sciences, 59872 Meschede, GermanyDepartment of Automation Technology and Learning Systems, South Westphalia University of Applied Sciences, 59494 Soest, GermanyStreet delivery faces significant challenges due to outdated road infrastructure, which was not designed to handle current vehicle volumes, leading to congestion and inefficiencies, especially in last-mile delivery. Integrating drones into the delivery system offers a promising solution by bypassing congested roads, thereby enhancing delivery speed and reducing infrastructure strain. However, optimizing this multimodal delivery system is complex and data-driven, with real-world data often being costly and restricted. To address this, we propose a synthetic dataset generator that creates diverse and realistic delivery scenarios, incorporating environmental variables, customer profiles, and vehicle characteristics. The key contribution of our work is the development of a dynamic generator for multiple optimization problems with diverse complexities or even combinations of optimization problems. This generator allows for the incorporation of real-world factors such as traffic congestion and synthetically generated factors such as wind conditions and communication constraints, as well as others. The primary objective is to establish a foundation for creating benchmark scenarios that enable the comparison of existing and new approaches. We evaluate the generated dataset by applying it to three optimization problems, including facility location, vehicle routing, and path planning, using different techniques to demonstrate the dataset’s effectiveness and operational viability.https://www.mdpi.com/2504-446X/8/12/724multimodal delivery systemsynthetic datasetoptimization problemsdynamic data modeling |
| spellingShingle | Diyar Altinses David Orlando Salazar Torres Asrat Mekonnen Gobachew Stefan Lier Andreas Schwung Synthetic Dataset Generation for Optimizing Multimodal Drone Delivery Systems Drones multimodal delivery system synthetic dataset optimization problems dynamic data modeling |
| title | Synthetic Dataset Generation for Optimizing Multimodal Drone Delivery Systems |
| title_full | Synthetic Dataset Generation for Optimizing Multimodal Drone Delivery Systems |
| title_fullStr | Synthetic Dataset Generation for Optimizing Multimodal Drone Delivery Systems |
| title_full_unstemmed | Synthetic Dataset Generation for Optimizing Multimodal Drone Delivery Systems |
| title_short | Synthetic Dataset Generation for Optimizing Multimodal Drone Delivery Systems |
| title_sort | synthetic dataset generation for optimizing multimodal drone delivery systems |
| topic | multimodal delivery system synthetic dataset optimization problems dynamic data modeling |
| url | https://www.mdpi.com/2504-446X/8/12/724 |
| work_keys_str_mv | AT diyaraltinses syntheticdatasetgenerationforoptimizingmultimodaldronedeliverysystems AT davidorlandosalazartorres syntheticdatasetgenerationforoptimizingmultimodaldronedeliverysystems AT asratmekonnengobachew syntheticdatasetgenerationforoptimizingmultimodaldronedeliverysystems AT stefanlier syntheticdatasetgenerationforoptimizingmultimodaldronedeliverysystems AT andreasschwung syntheticdatasetgenerationforoptimizingmultimodaldronedeliverysystems |