TY - GEN
T1 - Coupling ant colony optimization and discrete-event simulation to solve a stochastic location-routing problem
AU - Herazo-Padilla, Nilson
AU - Montoya-Torres, Jairo R.
AU - Munoz-Villamizar, Andres
AU - Isaza, Santiago Nieto
AU - Polo, Luis Ramirez
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - This paper considers the stochastic version of the location-routing problem (SLRP) in which transportation cost and vehicle travel speeds are both stochastic. A hybrid solution procedure based on Ant Colony Optimization (ACO) and Discrete-Event Simulation (DES) is proposed. After using a sequential heuristic algorithm to solve the location subproblem, ACO is employed to solve the corresponding vehicle routing problem. DES is finally used to evaluate such vehicle routes in terms of their impact on the expected total costs of location and transport to customers. The approach is tested using random-generated data sets. because there are no previous works in literature that considers the same stochastic location-routing problem, the procedure is compared against the deterministic version of the problem. Results show that the proposed approach is very efficient and effective.
AB - This paper considers the stochastic version of the location-routing problem (SLRP) in which transportation cost and vehicle travel speeds are both stochastic. A hybrid solution procedure based on Ant Colony Optimization (ACO) and Discrete-Event Simulation (DES) is proposed. After using a sequential heuristic algorithm to solve the location subproblem, ACO is employed to solve the corresponding vehicle routing problem. DES is finally used to evaluate such vehicle routes in terms of their impact on the expected total costs of location and transport to customers. The approach is tested using random-generated data sets. because there are no previous works in literature that considers the same stochastic location-routing problem, the procedure is compared against the deterministic version of the problem. Results show that the proposed approach is very efficient and effective.
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U2 - 10.1109/WSC.2013.6721699
DO - 10.1109/WSC.2013.6721699
M3 - Conference contribution
AN - SCOPUS:84894205084
SN - 9781479939503
T3 - Proceedings of the 2013 Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013
SP - 3352
EP - 3362
BT - Proceedings of the 2013 Winter Simulation Conference - Simulation
T2 - 2013 43rd Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013
Y2 - 8 December 2013 through 11 December 2013
ER -