Towards A Fair Mobility: An Evolutionary Algorithm for Customers-Dependent Transport on Demand Problems

In a highly connected world like smart cities, smart mobility must be inclusive to be relevant. We must consider all citizens, including people with reduced mobility and residents without transportation coverage. That is why smart mobility is changing to fair mobility. In this paper, we address a transport on-demand problem named Customers-Dependent Dial-A-Ride Problem. This problem incorporates customers' preferences into the transportation system. The aim is the passenger transportation with minimal costs. It is formulated taking into account specific customers' requirements to meet their own expected needs. A mixed-integer linear programming formulation is proposed for this problem. To solve it, we propose a new genetic approach. Lower bounds are produced through the commercial solver CPLEX. Experiments are investigated on real-life instances of transport on-demand problems. To better evaluate the results of our method, they are both compared to exact solutions and those of an existing metaheuristic method. Good and promising transport on demand plans are produced.