Abstract
In this paper, a bi-objective job sequencing problem is proposed. The objectives are – 1) total weighted tardiness of jobs and 2) total deterioration cost. The proposed problem has been solved by a modified version of the original Nondominated Sorting Genetic Algorithm-II (NSGA-II) which is one of the commonly applied Multi-Objective Evolutionary Algorithm in the existing literature. NSGA-II has been modified by introducing a novel mutation algorithm that has been embedded in the original NSGA-II. The experimental results show the Pareto optimal solutions and conclusions are drawn based on the results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multi-objective Genetic Algorithm. NSGA–II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Safa, E.F., Ihsan, S., Ays_egül, T., Manoj Kumar, T.: New solution methods for single machine bicriteria scheduling problem: Minimization of average flowtime and number of tardy jobs. European Journal of Operational Research 201, 89–98 (2010)
Karimi, N., Zandieh, M., Karamooz, H.R.: Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach. Expert Systems with Applications 37, 4024–4032 (2010)
Yazdani, S.M.T., Jolai, F.: Optimal methods for batch processing problem with makespan and maximum lateness objectives. Applied Mathematical Modelling 34, 314–324 (2010)
Sha, D.Y., Lin, H.-H.: A multi-objective PSO for job-shop scheduling problems. Expert Systems with Applications 37, 1065–1070 (2010)
Reza, T.-M., Babak, J., Fariborz, J., Ali, G.: The use of a fuzzy multi-objective linear programming for solving a multi-objective single-machine scheduling problem. Applied Soft Computing 10, 919–925 (2010)
Huynh, T.N., Ameur, S.: Due dates assignment and JIT scheduling with equalsize jobs. European Journal of Operational Research 205, 280–289 (2010)
Luca, V., Dario, P., Carlo, M.: A tabu search algorithm for scheduling pharmaceutical packaging operations. European Journal of Operational Research 202, 538–546 (2010)
Wanga, L., Pan, Q.-K., Suganthan, P.N., Wang, W.-H., Wang, Y.-M.: A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems. Computers & Operations Research 37, 509–520 (2010)
Betul, Y., Mutlu, Y.M.: A multi-objective ant colony system algorithm for flow shop scheduling problem. Expert Systems with Applications 37, 1361–1368 (2010)
Zandieh, M., Karimi, N.: An adaptive multi-population genetic algorithm to solve the multi-objective group scheduling problem in hybrid flexible flowshop with sequence-dependent setup times. Journal of Intelligent Manufacturing (2010), doi:10.1007/s10845-009-03747
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer India
About this paper
Cite this paper
Bandyopadhyay, S., Das, A. (2013). Proposing Modified NSGA-II to Solve a Job Sequencing Problem. In: Kumar M., A., R., S., Kumar, T. (eds) Proceedings of International Conference on Advances in Computing. Advances in Intelligent Systems and Computing, vol 174. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0740-5_46
Download citation
DOI: https://doi.org/10.1007/978-81-322-0740-5_46
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-0739-9
Online ISBN: 978-81-322-0740-5
eBook Packages: EngineeringEngineering (R0)