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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
We observe that the heuristic performed well on the experienced instances.
📌 3 Key Takeaways
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Objective & Hypothesis:In this study we extend and generalize a locating and routing problem for UAVs, with an objective of maximization of the total score collected from interest points visited.
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Key Findings & Data:By solving the problem we determine simultaneously take-off and landing stations and visit order of interest points for each UAV.
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Conclusion & Impact:We observe that the heuristic performed well on the experienced instances.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Location and Routing Problem · Ant Colony Optimization · UAV
Abstract
In this study we extend and generalize a locating and routing problem for UAVs, with an objective of maximization of the total score collected from interest points visited. By solving the problem we determine simultaneously take-off and landing stations and visit order of interest points for each UAV. The problem is defined by an integer linear programming (ILP) formulation. An ant colony optimization approach is altered for the introduced problem. Computational experiments are performed to compare CPLEX solver and the heuristic. We observe that the heuristic performed well on the experienced instances.
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Cite This Article
Yakıcı, E. (2016). GENERALIZATION OF A UAV LOCATION AND ROUTING PROBLEM BY TIME WINDOWS. Journal of Naval Sciences and Engineering, Cilt: 12 - Sayı: 2, 67–78. https://ajindex.com/articles/238015