Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multicriteria Optimization
The bi-objective covering tour problem
Computers and Operations Research
On the Integration of a TSP Heuristic into an EA for the Bi-objective Ring Star Problem
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
Survey: Quality of service in dial-a-ride operations
Computers and Industrial Engineering
A heuristic two-phase solution approach for the multi-objective dial-a-ride problem
Networks - Route 2007
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
Demand responsive transport allows customers to be carried to their destination as with a taxi service, provided that the customers are grouped in the same vehicles in order to reduce operational costs. This kind of service is related to the dial-a-ride problem. However, in order to improve the quality of service, demand responsive transport needs more flexibility. This paper tries to address this issue by proposing an original evolutionary approach. In order to propose a set of compromise solutions to the decision-maker, this approach optimizes three objectives concurrently. Moreover, in order to intensify the search process, this multi-objective evolutionary approach is hybridized with a local search. Results obtained on random and realistic problems are detailed to compare three state-of-the-art algorithms and discussed from an operational point of view.