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A cutting plane method for risk-constrained traveling salesman problem with random arc costs

Author

Listed:
  • Zhouchun Huang

    (Nanjing University of Aeronautics and Astronautics)

  • Qipeng Phil Zheng

    (University of Central Florida)

  • Eduardo Pasiliao

    (Air Force Research Laboratory)

  • Vladimir Boginski

    (University of Central Florida)

  • Tao Zhang

    (Shanghai University of Finance and Economics)

Abstract

In this manuscript, we consider a stochastic traveling salesman problem with random arc costs and assume that the travel cost of each arc follows a normal distribution. All the other parameters in the problem are considered deterministic. In the presence of uncertainty, the optimal route achieved from solving the deterministic model might be exposed to a high risk that the actual cost exceeds the available resource. In this respect, we present the stochastic model incorporating risk management, and the Value at Risk and Conditional Value at Risk techniques are applied as the risk measures to assess and control the risk associated with the uncertainty. A novel cutting plane algorithm is developed to deal with the difficulty of solving such model, and exhibits superior computational performance in our numerical experiments over other solution approaches.

Suggested Citation

  • Zhouchun Huang & Qipeng Phil Zheng & Eduardo Pasiliao & Vladimir Boginski & Tao Zhang, 2019. "A cutting plane method for risk-constrained traveling salesman problem with random arc costs," Journal of Global Optimization, Springer, vol. 74(4), pages 839-859, August.
  • Handle: RePEc:spr:jglopt:v:74:y:2019:i:4:d:10.1007_s10898-018-0708-0
    DOI: 10.1007/s10898-018-0708-0
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