Solving the multi‐objective bike routing problem by meta‐heuristic algorithms
Pedro Nunes, Ana Moura, José Santos
Abstract
Abstract The Multi‐Objective Bike Routing Problem (MOBRP) addressed in this paper consists in finding a set of good solutions that represents the trade‐off between cyclist preferences when choosing a route. We propose a new genetic approach to solve the MOBRP (NGA‐MOBRP) with a new mutation operator. To test the approach, we consider four conflicting criteria: safety, travel distance, travel time, and comfort. The well‐known Nondominated Sorting Genetic Algorithm II (NSGA‐II) is also implemented in the MOBRP context, and its weaknesses are identified and mitigated in the proposed NGA‐MOBRP. The developed approach is compared with other published works in this field, namely another genetic algorithm and a Multi‐Objective Simulated Annealing (MOSA) approach. The computational results show that the approach developed in this work obtains better results than the previously mentioned published approaches.