Abstract
This paper adapts metaheuristic methods to develop Pareto optimal solutions to multi-criteria production scheduling problems. Approach is inspired by enhanced versions of genetic algorithms. Method first extends the Nondominated Sorting Genetic Algorithm (NSGA), a method recently proposed to produce Pareto-optimal solutions to numerical multi-objective problems. Multi-criteria flowshop scheduling is addressed next. Multi-criteria job shop scheduling is subsequently examined. Lastly the multi-criteria open shop problem is solved. Final solutions to each are Pareto optimal. The paper concludes with a statistical comparison of the performance of the basic NSGA to NSGA augmented by elitist selection.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Rerences
Adulbhan, P and Tabucanon, M T: Multicriteria Optimization in Industrial Systems, AIT, Thailand, (1980).
Bagchi, Tapan P and Deb, Kalyanmoy: Calibration of GA Parameters: The Design of Experiments Approach, Computer Science and Informatics, Vol 26, No. 3, (1996).
Baker, K R.: Introduction to Sequencing and Scheduling, Wiley, (1974)..
Carter, M: Proceedings, 2nd International Conference on the Practice and Theory of Automated Timetabling, University of Toronto, Aug 20-22, (1997).
Deb, K and Goldberg, D E: An Investigation of Niche and Species Formation in Genetic Function Optimization, Proceedings, International Conference on GA, (1989), 97–106.
French, L: Sequencing and Scheduling: An Introduction to the Mathematics of the Job Shop, Wiley, (1982).
Goldberg, D E: GAs in Search, Optimization and Machine Learning, Addison-Wesley, (1989).
Holland, J: Adaptation in Natural and Artificial Systems, University of Michigan Press, (1975).
Jayaram, K: Multi-objective Production Scheduling, M Tech Thesis, IME, IIT Kanpur, (1997).
Keeney, R: Decisions with Multiple Objectives—Preferences and Value Tradeoffs, John Wiley, (1983).
Lenstra, J et al.: Complexity of Machine Scheduling Problems, Annals of Discrete Mathematics, 1, (1977).
Murata, T H et al.: Multobjective GAs for Flowshop Scheduling Problems, Computers and IE, 30, (1996).
Pareto, V: Manual di Economia Politica, translated by A S Schwier (1971), MacMillan, (1906).
Pinedo, M: Scheduling Theory, Algorithms and Systems, Prentice-Hall, (1995).
Sannomiya, N and Iima, H: Application of GAs to Scheduling Problems in Manufacturing Processes, Proceedings, IEEE Conference on Evolutionary Computation, IEEE Press, (1996).
Seo, Fumiko and Sakawa, M: Multiple Criteria Decision Analysis in Regional Planning, Reidel, (1988).
Smith, John M: Theory of Evolution, Canto, Cambridge University Press, (1995).
Srinivas N and Deb K: Multiobjective Optimization using NSGA, Evolutionary Computing, 2(3), (1995).
Tamaki, H et al.: Multi-criteria Optimization by GA: Scheduling in Hot Rolling Process, APORS’94, (1994).
Uckun S et al.: Managing Genetic Search in Job Shop Scheduling, IEEE Expert, October, (1993).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bagchi, T.P. (2001). Pareto-Optimal Solutions for Multi-objective Production Scheduling Problems. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_32
Download citation
DOI: https://doi.org/10.1007/3-540-44719-9_32
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41745-3
Online ISBN: 978-3-540-44719-1
eBook Packages: Springer Book Archive