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Intelligent Service Robotics

, Volume 11, Issue 4, pp 355–369 | Cite as

An analytical hierarchy process-based approach to solve the multi-objective multiple traveling salesman problem

  • Sahar TriguiEmail author
  • Omar Cheikhrouhou
  • Anis Koubaa
  • Anis Zarrad
  • Habib Youssef
Original Research Paper
  • 123 Downloads

Abstract

We consider the problem of assigning a team of autonomous robots to target locations in the context of a disaster management scenario while optimizing several objectives. This problem can be cast as a multiple traveling salesman problem, where several robots must visit designated locations. This paper provides an analytical hierarchy process (AHP)-based approach to this problem, while minimizing three objectives: the total traveled distance, the maximum tour, and the deviation rate. The AHP-based approach involves three phases. In the first phase, we use the AHP process to define a specific weight for each objective. The second phase consists in allocating the available targets, wherein we define and use three approaches: market-based, robot and task mean allocation-based, and balanced-based. Finally, the third phase involves the improvement in the solutions generated in the second phase. To validate the efficiency of the AHP-based approach, we used MATLAB to conduct an extensive comparative simulation study with other algorithms reported in the literature. The performance comparison of the three approaches shows a gap between the market-based approach and the other two approaches of up to 30%. Further, the results show that the AHP-based approach provides a better balance between the objectives, as compared to other state-of-the-art approaches. In particular, we observed an improvement in the total traveled distance when using the AHP-based approach in comparison with the distance traveled when using a clustering-based approach.

Keywords

Assignment MTSP Multiple depots Multi-objective problem AHP 

Notes

Acknowledgements

The authors would like to thank the Center of Excellence and the Robotics and Internet-of-Things Unit at Prince Sultan University, Saudi Arabia, for their support of this work. The work is also supported by Gaitech Robotics.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.ENSIUniversity of ManoubaManoubaTunisia
  2. 2.Cooperative Intelligent Networked Systems (COINS) Research GroupRiyadhSaudi Arabia
  3. 3.Taif UniversityTaifSaudi Arabia
  4. 4.Computer and Embedded Systems LabUniversity of SfaxSfaxTunisia
  5. 5.Prince Sultan UniversityRiyadhSaudi Arabia
  6. 6.CISTER/INESC-TEC, ISEPPolytechnic Institute of PortoPortoPortugal
  7. 7.School of Computer ScienceUniversity of BirminghamBirminghamUK
  8. 8.PRINCE Research LabUniversity of SousseSousseTunisia

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