Can algorithms solve transportation puzzles? This research tackles the NP-hard problem of public transport driver scheduling by presenting a hybrid approach incorporating a genetic algorithm (GA). The GA's role is to derive a small selection of good shifts to seed a greedy schedule construction heuristic. This seeding process directs the heuristic towards more promising areas of the solution space, accelerating the search for optimal schedules. A group of shifts called a relief chain is identified and recorded. The relief chain is then inherited by the offspring and used by the GA for schedule construction. The new approach has been tested using real-life data sets, some of which represent very large problem instances. The results are generally better than those compiled by experienced schedulers and are comparable to solutions found by integer linear programming (ILP). In some cases, solutions were obtained when the ILP failed within practical computational limits. This hybrid approach, combining the strengths of evolutionary algorithms and heuristics, offers a powerful tool for addressing complex scheduling challenges in public transportation. This research contributes to the optimization of transportation systems and resource management.
Published in Evolutionary Computation, this paper fits perfectly with the journal's focus. It presents a novel application of a genetic algorithm to solve a complex optimization problem. This directly aligns with the journal's scope of publishing research on evolutionary computation and its applications.