First Competitive Ant Colony Scheme for the CARP
This provides a competitive method for solving large-scale CARP, an optimization problem in logistics and routing, though it is incremental compared to existing approaches.
The paper tackles the Capacitated Arc Routing Problem (CARP) using an Ant Colony Optimization scheme, achieving solutions as profitable as CARPET and competitive with Genetic Algorithms on large-scale instances with over 140 nodes and 190 arcs.
This paper addresses the Capacitated Arc Routing Problem (CARP) using an Ant Colony Optimization scheme. Ant Colony schemes can compute solutions for medium scale instances of VRP. The proposed Ant Colony is dedicated to large-scale instances of CARP with more than 140 nodes and 190 arcs to service. The Ant Colony scheme is coupled with a local search procedure and provides high quality solutions. The benchmarks we carried out prove possible to obtain solutions as profitable as CARPET ones can be obtained using such scheme when a sufficient number of iterations is devoted to the ants. It competes with the Genetic Algorithm of Lacomme et al. regarding solution quality but it is more time consuming on large scale instances. The method has been intensively benchmarked on the well-known instances of Eglese, DeArmon and the last ones of Belenguer and Benavent. This research report is a step forward CARP resolution by Ant Colony proving ant schemes can compete with Taboo search methods and Genetic Algorithms