Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Preserving population diversity for the multi-objective vehicle routing problem with time windows
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Fifty Years of Vehicle Routing
Transportation Science
An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows
Computers and Operations Research
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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In the Vehicle Routing Problem with Backhauls there are linehaul customers, who demand products, and backhaul customers, who supply products, and there is a fleet of vehicles available for servicing customers. The problem consists in finding a set of routes with the minimum cost, such that all customers are serviced. A generalization of this problem considers the collection from the backhaul customers optional. If the number of vehicles, the cost, and the uncollected demand are assumed to be equally important objectives, the problem can be tackled as a multi-objective optimization problem. In this paper, we solve these as multi-objective problems with an adapted previously proposed evolutionary algorithm and evaluate its performance with proper tools.