Summary of Exact Algorithms and Heuristics For Capacitated Covering Salesman Problems, by Lucas Porto Maziero et al.
Exact algorithms and heuristics for capacitated covering salesman problems
by Lucas Porto Maziero, Fábio Luiz Usberti, Celso Cavellucci
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces the Capacitated Covering Salesman Problem (CCSP), a novel optimization challenge that aims to service customers through a fleet of vehicles based in a depot while minimizing the total distance traveled. Unlike traditional vehicle routing problems, CCSP assumes that some customers can be serviced without being visited by a vehicle if they are within its coverage area. The authors propose two optimization methodologies for CCSP: ILP (Integer Linear Programming) and BRKGA (Biased Random-Key Genetic Algorithm) metaheuristic. Computational experiments evaluate the performance of these methodologies in terms of primal bounds, demonstrating their effectiveness. Furthermore, the authors extend their ILP formulation to create a novel MILP (Mixed Integer Linear Programming) for the Multi-Depot Covering Tour Vehicle Routing Problem (MDCTVRP), which outperforms the previous state-of-the-art exact approach with respect to optimality gaps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a new way of planning routes for vehicles that need to visit certain places. It’s like delivering packages or taking people somewhere. Instead of visiting every place, it’s okay if some places are visited indirectly by being near where the vehicle is already going. This makes it easier and more efficient. The authors came up with two ways to solve this problem: one using math formulas and another using a computer program that tries different solutions. They tested these methods on some sample problems and found they worked well. Then, they took their first method and made it even better by combining it with another idea. |
Keywords
» Artificial intelligence » Optimization
