Solving a Dial-a-Ride Problem with a Hybrid Multi-objective Evolutionary Approach: Application to Demand Responsive Transport

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.

[1]  Nicolas Jozefowiez,et al.  The vehicle routing problem: Latest advances and new challenges , 2007 .

[2]  M. Sol The general pickup and delivery problem , 2010 .

[3]  Lothar Thiele,et al.  A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers , 2006 .

[4]  Eckart Zitzler,et al.  Indicator-Based Selection in Multiobjective Search , 2004, PPSN.

[5]  Nicolas Jozefowiez,et al.  An evolutionary algorithm for the vehicle routing problem with route balancing , 2009, Eur. J. Oper. Res..

[6]  Pablo Moscato,et al.  A Gentle Introduction to Memetic Algorithms , 2003, Handbook of Metaheuristics.

[7]  Broderick Crawford,et al.  A Study on Genetic Algorithms for the DARP Problem , 2007, IWINAC.

[8]  Matthias Ehrgott,et al.  Multicriteria Optimization (2. ed.) , 2005 .

[9]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[10]  Xavier Gandibleux,et al.  A heuristic two‐phase solution approach for the multi‐objective dial‐a‐ride problem , 2009, Networks.

[11]  Arnaud Liefooghe,et al.  On optimizing a demand responsive transport with an evolutionary multi-objective approach , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[12]  Gilbert Laporte,et al.  The dial-a-ride problem: models and algorithms , 2006, Ann. Oper. Res..

[13]  Anthony Przybylski,et al.  Two phase algorithms for the bi-objective assignment problem , 2008, Eur. J. Oper. Res..

[14]  Gilbert Laporte,et al.  Quality of service in dial-a-ride operations , 2009, Comput. Ind. Eng..

[15]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[16]  Giselher Pankratz,et al.  A Grouping Genetic Algorithm for the Pickup and Delivery Problem with Time Windows , 2005, OR Spectr..

[17]  Clarisse Dhaenens,et al.  K-PPM: A new exact method to solve multi-objective combinatorial optimization problems , 2010, Eur. J. Oper. Res..

[18]  P. Haggett Locational analysis in human geography , 1967 .

[19]  E. Talbi,et al.  A Unified Model for Evolutionary Multiobjective Optimization and its Implementation in a General Purpose Software Framework: ParadisEO-MOEO , 2009, 0904.2987.

[20]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[21]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..