A multivariate migrating birds optimization algorithm based on disjunctive graph neighborhood for scenic spot vehicle scheduling

For the group travel, reasonable vehicle scheduling needs to consider constraints such as traffic distance and route conflict, which is common in many vehicle transportation scheduling systems, and it is crucial for operators to improve operational efficiency and provide a higher quality service experience. In this paper, a scenic spot vehicle scheduling problem is designed based the real-world scenario. In order to improve the search efficiency and maintain the diversity of solutions , an intelligent scheduling algorithm based on improved migrating birds optimization(gMBO) is proposed. The gMBO applies a neighborhood structure based on disjunctive graph to accelerate the solution search in the touring phase by avoiding redundancy. Besides, leveraging the left–right sequential queue characteristics of the MBO algorithm, gMBO utilizes two mechanisms to enhance the interaction between queues in the leader replacement stage, which can expand the search space of solutions and at the same time to maintain population diversity . Finally, we consider using the POX crossover operator in the individual, it is well adapted to the characteristics of the problem can reduce the generation of unreasonable solutions. The computational results show that the neighborhood structure is feasible. Considering the problem of scenic spot vehicle scheduling in practical urban applications, the multivariate migrating birds optimization algorithm based on disjunctive graph neighborhood is more effective than the MILP and other three meta-heuristic algorithms, and the optimal solution is obtained under the same stopping criteria, with an average RPD of 2.16. It has the advantages of fast convergence and good robustness.