Decision-Aiding Methodology for the School Bus Routing and Scheduling Problem

Abstract

We consider the school bus routing and scheduling problem, where transportation demand is known and bus scheduling can be planned in advance. We present a comprehensive methodology designed to support the decision of practitioners. We first propose a modeling framework where the focus is on optimizing the level of service for a given number of buses. Then, we describe an automatic procedure generating a solution to the problem. It first builds a feasible solution, which is subsequently improved using a heuristic. We analyze two important issues associated with this methodology. On the one hand, we analyze the performance of three types of heuristics both on real and synthetic data. We recommend the use of a simulated annealing technique exploring infeasible solutions, which performs slightly better than all others. More importantly, we find that the performance of all heuristics is not globally affected by the choice of the parameters. This is important from a practitioner viewpoint, as the fine tuning of algorithm parameters is not critical for its performance. On the other hand, we propose an interactive tool allowing the practitioner to visualize the proposed solution, to test its robustness, and to dynamically rebuild new solutions if the data of the original problem are modified.

The School Bus Problem on Trees

“…We refer to [10] for a thorough survey of possible formulations and heuristic solution methods. Our notion of regret was first introduced as a vehicle routing objective in [11]. They considered a more general problem involving timing windows for customers and applied metaheuristics to produce solutions to real-life instances.…”

Operational Research in education

“…These problems are invariably set up in an optimization framework. Small-scale problems can be solved using, for example, integer programming methods (Ward 1964;Bektaş and Elmastaş 2007); more often, though, the complexity of these multi-objective leads to use of heuristics in finding a solution (Bodin and Berman 1979;Schultz 1979;Gochenour Jr. et al 1980;Corberán et al 2002;Li and Fu 2002;Spada et al 2005). Similar approaches can be applied to the transportation of pupils with special educational needs for whom the gains in terms of reducing the duration of journeys can be particularly beneficial (Russell and Morrel 1986;Sutcliffe and Boardman 1990).…”

Chapter 7: Pickup-and-Delivery Problems for People Transportation

“…Spada, Bierlaire, and Liebling [73] propose a modeling framework where the focus is on optimizing the level of service for a given number of buses, using an automatic procedure to generate a solution to the problem. The procedure first builds a feasible solution, which then can be improved using a heuristic.…”