TY - GEN
T1 - A Robust optimization approach for the Vehicle Routing problem with uncertain travel cost
AU - Solano-Charris, Elyn L.
AU - Prins, Christian
AU - Santos, Andrea Cynthia
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/23
Y1 - 2014/12/23
N2 - The Robust Vehicle Routing problem (RVRP) with discrete scenarios is studied here to handle uncertain traveling time, where a scenario represents a possible discretization of the travel time observed on each arc at a given traffic hour. The goal is to build a set of routes considering the minimization of the worst total cost over all scenarios. A Genetic Algorithm (GA) is proposed for the RVRP considering a bounded set of discrete scenarios and the asymmetric arc costs on the transportation network. Tests on small and medium size instances are presented to evaluate the performance of the proposed GA for the RVRP. On small-size instances, a maximum of 20 customers, 3 vehicles and 30 discrete scenarios are handled. For medium-size instances, 100 customers, 20 vehicles and 20 scenarios are tested. Computational results indicate the GA produces good solutions and retrives the majority of proven optima in a moderate computational time.
AB - The Robust Vehicle Routing problem (RVRP) with discrete scenarios is studied here to handle uncertain traveling time, where a scenario represents a possible discretization of the travel time observed on each arc at a given traffic hour. The goal is to build a set of routes considering the minimization of the worst total cost over all scenarios. A Genetic Algorithm (GA) is proposed for the RVRP considering a bounded set of discrete scenarios and the asymmetric arc costs on the transportation network. Tests on small and medium size instances are presented to evaluate the performance of the proposed GA for the RVRP. On small-size instances, a maximum of 20 customers, 3 vehicles and 30 discrete scenarios are handled. For medium-size instances, 100 customers, 20 vehicles and 20 scenarios are tested. Computational results indicate the GA produces good solutions and retrives the majority of proven optima in a moderate computational time.
UR - https://www.scopus.com/pages/publications/84921371480
U2 - 10.1109/CoDIT.2014.6996875
DO - 10.1109/CoDIT.2014.6996875
M3 - Proceedings
AN - SCOPUS:84921371480
T3 - Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014
SP - 98
EP - 103
BT - Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014
A2 - Kacem, Imed
A2 - Laroche, Pierre
A2 - Roka, Zsuzsanna
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014
Y2 - 3 November 2014 through 5 November 2014
ER -