- The paper provides an extensive review of MTSP by outlining its formulation, challenges, and a novel taxonomy for classifying solution approaches.
- It compares deterministic and meta-heuristic methods, including GA, ACO, and PSO, highlighting their efficiencies and computational trade-offs.
- The survey identifies key MTSP applications in logistics, UAV data collection, rescue missions, and precision agriculture, urging future research in energy-efficient routing.
Overview of the Multiple Travelling Salesman Problem Survey
The paper "A Comprehensive Survey on the Multiple Travelling Salesman Problem: Applications, Approaches, and Taxonomy" by Omar Cheikhrouhou and Ines Khoufi addresses the deficiency of comprehensive reviews on the Multiple Travelling Salesman Problem (MTSP). This work serves as an extensive survey of the MTSP, compiling and assessing recent contributions, application domains, approaches for solutions, and proposing a new taxonomy for classifying MTSP studies.
MTSP, a complex combinatorial optimization problem, extends the classical Travelling Salesman Problem (TSP) by incorporating multiple salesmen who need to visit a series of cities exactly once while minimizing the overall travel cost. This complexity makes MTSP relatable to other optimization problems such as the Vehicle Routing Problem (VRP) and Task Assignment Problems. However, unlike VRP, MTSP does not consider vehicle capacities or customer demand, nor does it allow multiple visits and subtours.
MTSP Applications
The survey outlines numerous application fields for MTSP, including:
- Transportation and Delivery: This includes logistics and parcel delivery services where MTSP helps in route optimization for fleets involving ground vehicles and Unmanned Aerial Vehicles (UAVs).
- Wireless Sensor Network (WSN) Data Collection: MTSP is essential in optimal routing for mobile nodes to enhance data collection efficiency, reducing latency and energy consumption.
- Search and Rescue Operations: MTSP assists in determining optimal search patterns for UAVs or ground vehicles to maximize efficacy during disaster response.
- Precision Agriculture: Route optimization using MTSP facilitates activities like crop monitoring or irrigation management with UAVs and ground robots.
- Disaster Management and Monitoring: Here, MTSP contributes to efficient path planning for UAVs in post-disaster situations for tasks like surveillance and logistical support.
Classification and Approaches
The authors classify MTSP into various attributes based on the type of salesman (robots, vehicles, UAVs), depot specifications (single/multiple, fixed/mobile), cities specifications (standard, colored MTSP), constraints (energy, capacity), and the intended objective functions (single or multi-objective).
Central to the survey is the classification of approaches used to tackle MTSP. These include deterministic methods for exact solutions, though they are resource-intensive, and meta-heuristic approaches like Genetic Algorithms (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO), which provide flexibility and efficiency for larger problem instances. Moreover, market-based approaches exhibit efficacy in dynamic environments where real-time decision-making is critical.
Implications and Future Directions
The paper indicates the growing importance of hybrid algorithms that combine multiple techniques to tackle MTSP, offering robustness and enhanced performance. Market-based approaches demonstrate significant promise for real-time dynamic allocation problems. Furthermore, considering energy constraints and vehicle-specific dynamics becomes crucial with the integration of UAVs into broader applications.
Research into MTSP is anticipated to progress further, especially concerning UAV applications, which are gaining traction in both civilian and military contexts. Future research should focus on developing energy-efficient strategies and embracing emerging technologies, looking beyond traditional meta-heuristics and exploring areas such as machine learning integration for adaptive route optimization.
In conclusion, this survey provides an invaluable resource for researchers and practitioners aiming to engage with MTSP, offering a detailed landscape of current methodologies and application possibilities. By doing so, it lays the groundwork for future innovation and exploration in optimizing complex logistics and operational challenges across various sectors.