Abstract

Bus driver scheduling problem is one of most important and complex problem faced by many companies and bus terminals. This paper attempts to solve this problem using paramterless evolutionary algorithms, TLBO and JAYA algorithm. The objective of this paper is to assign the drivers to duty on a particular day and block duty by satisfying the constraints. Algorithms are tested on four randomly generated datasets. In the work solution is obtained with no zero constraint violations. JAYA algorithm gives better results than TLBO algorithm.

References

• Chun, H.W., 1997, December. A distributed constraint-based search architecture for bus timetabling and duty assignment. In Software Engineering Conference, 1997. Asia Pacific... and International Computer Science Conference 1997. APSEC'97 and ICSC'97. Proceedings (pp. 82-88). IEEE.
• Ma, J., Liu, T. and Song, C., 2012, December. A Lagrangian relaxation-based heuristic for the bus driver scheduling problem: A case study of beijing. In Information Science and Control Engineering 2012 (ICISCE 2012), IET International Conference on (pp. 1-5). IET.
• Dias, T.G., Sousa, J.P. and Cunha, J.F., 2001, July. A genetic algorithm for the bus driver scheduling problem. In 4th Metaheuristics International Conference.
• De Leone, R., Festa, P. and Marchitto, E., 2011. A Bus Driver Scheduling Problem: a new mathematical model and a GRASP approximate solution. Journal of Heuristics, 17(4), pp.441-466.
• De Leone, R., Festa, P. and Marchitto, E., 2011. Solving a bus driver scheduling problem with randomized multistart heuristics. International Transactions in Operational Research, 18(6), pp.707-727.
• Goel, A., 2012. The Canadian minimum duration truck driver scheduling problem. Computers & Operations Research, 39(10), pp.2359-2367.
• Valouxis, C. and Housos, E., 2002. Combined bus and driver scheduling. Computers & Operations Research, 29(3), pp.243-259.
• http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.41.2393&rep=rep1&type=pdf
• Adamuthe, A.C. and Bichkar, R., 2011. Hybrid Genetic Algorithmic Approaches for Personnel Timetabling and Scheduling Problems in Healthcare. In International Conference on Technology Systems and Management.
• Adamuthe, A.C. and Bichkar, R.S., 2012, October. Tabu search for solving personnel scheduling problem. In Communication, Information & Computing Technology (ICCICT), 2012 International Conference on (pp. 1-6). IEEE.
• Adamuthe, A.C., Mane, S.U. and Thampi, G.T., 2012, October. Genetic algorithmic approach for security personnel scheduling. In Communication, Information & Computing Technology (ICCICT), 2012 International Conference on (pp. 1-6). IEEE.
• Rao, R.V., Savsani, V.J. and Vakharia, D.P., 2012. Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Information Sciences, 183(1), pp.1-15.
• Rao, R.V., Savsani, V.J. and Vakharia, D.P., 2011. Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), pp.303-315.
• Rao, R., 2016. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), pp.19-34.
• Moz, M., Respício, A. and Pato, M.V., 2009. Bi-objective evolutionary heuristics for bus driver rostering. Public Transport, 1(3), pp.189-210.