Research On Public Traffic Driver Scheduling Problem Based On EMBA

Public transport plays an indispensable role in urban transportation, even in the entire city system. Nevertheless, city traffic congestion become increasingly serious, which makes developing public transport with great endeavor is a valid solution to the congestion problem, where driver scheduling is important part in public traffic system and is relation to the operation cost of the whole scheduling plan and the usage of employees. Good driver schedule not only be more fair, reasonable and effective for drivers, but also improve the entire efficiency in scheduling plan and reduce the operation cost greatly.Bat algorithm is a new heuristic intelligent algorithm that employs echolocation principle, which has great potentiality in searching for optimal solution for problem such as combinatorial optimization problems and scheduling. This paper aims to figure out the limitation problem that basic bat algorithm turns out to have when it is applied to solve the driver scheduling. We induce attraction-exclusion mechanism into basic bat algorithm, and propose a bat algorithm based on electromagnetism-like mechanism algorithm, which is fit for the problem of driver scheduling. The experimental result demonstrates that the choice of algorithm parameters can influence on the algorithm performance, so the rational opt for parameters can receive optimal scheduling plan in shorter term.To begin with, this paper introduces the basic concepts in driver scheduling, and design the model of this problem for domestic situation. Furthermore, we use the improved bat algorithm to construct the coding form and fitness function for driver scheduling, and implement the programming works, which solves the problem of driver scheduling in public transport. Finally, for the problem model, we select example data to conduct the experiment for the designed model and improved algorithm, in which we compare with the existed improved genetic algorithm and basic bat algorithm, the result shows that our algorithm can solve the optimal problem of driver scheduling with efficiency.