A neural network algorithm for the traveling salesman problem with backhauls
Computers and Industrial Engineering - Special issue: Focussed issue on applied meta-heuristics
Multi-Agent Patrolling with Reinforcement Learning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
A Theoretical Analysis of Multi-Agent Patrolling Strategies
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Priority-based assignment and routing of a fleet of unmanned combat aerial vehicles
Computers and Operations Research
Probabilistic Multiagent Patrolling
SBIA '08 Proceedings of the 19th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Survey: The vehicle routing problem: A taxonomic review
Computers and Industrial Engineering
Multi-agent patrolling: an empirical analysis of alternative architectures
MABS'02 Proceedings of the 3rd international conference on Multi-agent-based simulation II
Proceedings of the 2010 ACM Symposium on Applied Computing
Computers and Operations Research
Towards optimal police patrol routes with genetic algorithms
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
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We consider a problem of finding a path of an unmanned combat vehicle that patrols a given area by visiting a given set of checkpoints with the objective of minimizing possibility of enemy's infiltration. In this study, we focus on a situation in which the possibility of enemy's infiltration at (through) each checkpoint is increased nonlinearly as time passes and the checkpoint may be patrolled multiple times during a planning horizon. We develop two-phase heuristics in which an initial path is constructed in the first phase and then it is improved in the second phase. For evaluation of the performance of the proposed heuristics, computational experiments are performed on randomly generated problem instances, and results show that the heuristics give good solutions in a reasonably short time.