TY - JOUR

T1 - An optimal path model for the risk-averse traveler

AU - Zhang, Leilei

AU - Homem-De-Mellob, Tito

N1 - Funding Information:
This work has been supported in part by the National Science Foundation [Grant 1033048] and Conicyt-Chile [Grant Fondecyt 1120244].
Publisher Copyright:
© 2016 INFORMS.

PY - 2017

Y1 - 2017

N2 - In this paper we discuss stochastic optimal path problems where the goal is to find a path that has minimal expected cost and at the same time is less risky (in terms of travel time) than a given benchmark path. The model is suitable for a risk-averse traveler who prefers a path with a more guaranteed travel time to another path that could be faster but could also be slower. Such risk attitude is incorporated using the concept of second order stochastic dominance constraints. A recently developed theory for optimization problems with stochastic dominance constraints ensures that the resulting problem can be written as a large linear integer program with binary variables; for networks of realistic size, however, such a direct approach is not practical because of the size of the resulting optimization problem. Moreover, the solution returned by the model may contain cycles, which is clearly undesirable from a practical perspective. A number of strategies are explored to solve the problem. First, we prove that cycles can be prevented by a simple modification of the model if the arc travel times are mutually independent.We then propose a sample average approximation approach to the problem using samples from the distribution of travel times. Because of the randomness resulting from sampling, it is important that statistical guarantees for the solution returned by the algorithm be given, and we provide heuristic procedures to deal with stochastic constraints. We also incorporate a branch-and-cut approach that exploits the structure of the problem to deal with the integrality constraints more efficiently. We present some numerical experiments for a 1,522-arc system that corresponds to a large portion of the Chicago area network. The results show that our approach can solve the problem very effectively, producing solutions with statistical guarantees of optimality within a reasonable computational time.

AB - In this paper we discuss stochastic optimal path problems where the goal is to find a path that has minimal expected cost and at the same time is less risky (in terms of travel time) than a given benchmark path. The model is suitable for a risk-averse traveler who prefers a path with a more guaranteed travel time to another path that could be faster but could also be slower. Such risk attitude is incorporated using the concept of second order stochastic dominance constraints. A recently developed theory for optimization problems with stochastic dominance constraints ensures that the resulting problem can be written as a large linear integer program with binary variables; for networks of realistic size, however, such a direct approach is not practical because of the size of the resulting optimization problem. Moreover, the solution returned by the model may contain cycles, which is clearly undesirable from a practical perspective. A number of strategies are explored to solve the problem. First, we prove that cycles can be prevented by a simple modification of the model if the arc travel times are mutually independent.We then propose a sample average approximation approach to the problem using samples from the distribution of travel times. Because of the randomness resulting from sampling, it is important that statistical guarantees for the solution returned by the algorithm be given, and we provide heuristic procedures to deal with stochastic constraints. We also incorporate a branch-and-cut approach that exploits the structure of the problem to deal with the integrality constraints more efficiently. We present some numerical experiments for a 1,522-arc system that corresponds to a large portion of the Chicago area network. The results show that our approach can solve the problem very effectively, producing solutions with statistical guarantees of optimality within a reasonable computational time.

KW - Branch-and-cut

KW - Optimal path problem

KW - Sample average approximation

KW - Stochastic dominance

UR - http://www.scopus.com/inward/record.url?scp=85019715273&partnerID=8YFLogxK

U2 - 10.1287/trsc.2016.0685

DO - 10.1287/trsc.2016.0685

M3 - Article

AN - SCOPUS:85019715273

VL - 51

SP - 518

EP - 535

JO - Transportation Science

JF - Transportation Science

SN - 0041-1655

IS - 2

ER -