Biased Random-Key Genetic Algorithm Applied to the Vehicle Routing Problem with Private Fleet and Common Carrier

Abstract

Among the different classes of Vehicle Routing Problems are the Vehicle Routing Problems with Profits (VRPPs), where it is not mandatory to service all the customers. A relatively new VRPP is the VRPPFCC (Vehicle Routing Problem with Private Fleet and Common Carrier). In this problem, it is sometimes useful to directly serve only part of the shipping demand, outsourcing the rest of it to other companies. This paper presents the combination between the Biased Random-key Genetic Algorithm (BRKGA) and Random Variable Neighborhood Descent (RVND), a local search procedure, in the solution of the VRPPFCC. The implementation uses a vector of random keys as solution representation; thus a decoding heuristic is also developed, converting random keys to actual solutions for the VRPPFCC. Computational tests and conclusions focus on the comparison of the effectiveness of the methods, comparing their obtained solutions to the best known solutions for the problem.