A GRASP-based Approach for Demand Responsive Transportation

Abstract

Demand Responsive Transportation (DRT) systems try to provide quality public transportation in low, variable and unpredictable demand scenarios, with routes and frequencies that may vary according to observed demand, possibly in real-time. The design and operation of DRTs involve multiple criteria and have a combinatorial nature that prevents the use of traditional optimization methods. To obtain an approximation of the Pareto solution set, we have designed a heuristic approach involving the construction of a feasible route through a greedy randomized procedure, followed by a local search phase, latter embedded in a Decision Support System that also uses simulation. The goal is not only to minimize operating costs but also to maximize the quality of the service. Experiments with simple cases, inspired in real problems, have shown the potential of this approach for efficiently designing and managing DRT services.