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Solving a dial-a-ride problem with a hybrid evolutionary multi-objective 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.

An evolutionary multi-objective approach for speed tuning optimization with energy saving in railway management

An approach for speed tuning in railway management is presented for optimizing both travel duration and energy saving. This approach is based on a state-of-the-art evolutionary algorithm with Pareto approach. This algorithm provides a set of diversified non-dominated solutions to the decision-maker. A case study on Gonesse junction (France) is also reported and analyzed.

Comparison of three algorithms for solving the convergent demand responsive transportation problem

Led by computer science and geography laboratories, this paper presents three algorithms for solving the convergent demand responsive transport problem (CDRTP). Two of them are exact: the first one is based on a dynamic programming algorithm to enumerate exhaustively the sprawling spanning trees and the second one is based on a depth first search algorithm. The third one is stochastic and uses a steady state genetic algorithm. These approaches address the problems of scalability and flexibility, are compared and discussed

On optimizing a demand responsive transport with an evolutionary multi-objective approach

This paper deals with a dial-a-ride problem with time windows applied to a demand responsive transport service. An evolutionary approach as well as new original representation and variation operators are proposed and detailed. Such mechanisms are used with three state-of-the-art multi-objective evolutionary algorithms: NSGA-II, IBEA and SPEA2. After introducing the general problem, the solution encoding and the algorithm mechanisms are depicted. The approach is assessed by applying the algorithms to both random and realistic dial-a-ride instances. Then a statistical comparison is provided in order to highlight the most suited evolutionary algorithms to optimize real-life transportation problems.