Solving a bi-objective vehicle routing problem by Pareto-ant colony optimization

Authors:
Joseph M. Pasia;Karl F. Doerner;Richard F. Hartl;Marc Reimann
Affiliations:
Department of Mathematics, University of the Philippines-Diliman, Quezon City, Philippines;Department of Business Administration, University of Vienna, Vienna, Austria and Salzburg Research Forschungsgesellschaft, Salzburg, Austria;Department of Business Administration, University of Vienna, Vienna, Austria;Institute for Operations Research, ETH Zurich, Zurich, Switzerland
Venue:
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
Year:
2007

Citing 5
Cited 0

SavingsAnts for the Vehicle Routing Problem

Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Parallel and Hybrid Models for Multi-objective Optimization: Application to the Vehicle Routing Problem

PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A population-based local search for solving a bi-objective vehicle routing problem

EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach

IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we propose the application of Pareto ant colony optimization (PACO) in solving a bi-objective capacitated vehicle routing problem with route balancing (CVRPRB). The objectives of the problem are minimization of the tour length and balancing the routes. We propose PACO as our response to the deficiency of the Pareto-based local search (P-LS) approach, which we also developed to solve CVRPRB. The deficiency of P-LS is the lack of information flow among its pools of solutions. PACO is a natural choice in addressing this deficiency since PACO and P-LS are similar in structure. It resolves the absence of information flow through its pheromone values. Several test instances are used to demonstrate the contribution and importance of information flow among the pools of solutions. Computational results show that PACO improves P-LS in most instances with respect to different performance metrics.