Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


March Edition 2023

International Journal of Applied Information Systems solicits high quality original research papers for the March 2023 Edition of the journal. The last date of research paper submission is February 15, 2023.

Application of Multi Objective Optimization in Prioritizing and Machine Scheduling: A Mobile Scheduler Toolkit

Sunita Bansal, Manuj Darbari Published in Architecture

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
Download full text
  1. Sunita Bansal and Manuj Darbari. Article: Application of Multi Objective Optimization in Prioritizing and Machine Scheduling: A Mobile Scheduler Toolkit. International Journal of Applied Information Systems 3(2):24-28, July 2012. BibTeX

    	author = "Sunita Bansal and Manuj Darbari",
    	title = "Article: Application of Multi Objective Optimization in Prioritizing and Machine Scheduling: A Mobile Scheduler Toolkit",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 3,
    	number = 2,
    	pages = "24-28",
    	month = "July",
    	note = "Published by Foundation of Computer Science, New York, USA"


This paper presents a new multi objective algorithm to determine optimal configurations of multi-state, multi-task production systems based on availability analysis. A multi-task production system is one in which different subsets of machines can be used to perform distinct functions or tasks. The performance of a manufacturing system is greatly influenced by its configuration. Availability can be used in the context of multi-task production systems to select a particular configuration that maximizes the probability of meeting a required demand for each specific task, or the expected productivity for each task. A particular configuration may not simultaneously maximize the probability of meeting demand for each of the individual tasks, and thus, the problem is treated as a multi-objective optimization problem. The solution to this problem is a set of promising solutions that provides a trade-off among the different objective functions considered.


  1. Altenberg, L. 1994. The evolution of evolvability in genetic programming. In Kinnear, Jr. , E. , editor, Advances in Genetic Programming, complex Adaptive Systems, chapter 3, pages 47-74. MIT Press, Cambridge, Massachusetts.
  2. Bently, P. J. and Wakefield, J. P. 1997. Finding acceptable solutions in the Pareto optimal range using multi objective genetic algorithms. In P. K. Chawdhry, R. Roy and R. K. Pant (Eds), Soft Computing in Engineering Design and Manufacturing, Part 5, pp. 231-240. London, UK: Springer-Verlag
  3. Ben-Tal, A. 1980. Characterization of Pareto and lexicographic optimal solutions Booker, L. 1987. Improving search in genetic algorithms. In Davis, L. , editor, Genetic Algorithms and Simulated Annealing, Research Notes in Artificial Intelligence, chapter 5, pages 61-73. Pitman, London.
  4. Box. , M. J. 1965. A new method of constrained optimization and a comparison with other methods. Computer Journal 8(1), 42-52
  5. Coelio, C. A. C 1999. A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information Systems 1(3), 269-308
  6. Coello, C. A. C and Christiansen, A. D 1999 MOSES: A multi objective optimization tool for engineering design. Engineering Optimization 31(3), 337-368
  7. Deb, K Multi-objective evolutionary algorithms: 2001 Introducing bias among Pareto optimal solutions. In A. Ghosh and S. Tsutsui (Eds) theory and Application of Evolutionary Computation Recent trends London: Springer Verlag
  8. Deb, K. and Goel, T. 2001. A hybrid multi-objective evolutionary approach to engineering shape design. In Proceedings of the First International Conference on Evolutionary Multi – Criterion Optimization (EMO-2001), pp. 385-399
  9. Fandel, G. and Gal, T. , editors 1980. Multiple Criteria Decision Making Theory and Application, volume 177 of Lecture Notes in Economics and Mathematical Systems. Springer-Verlag, Berlin. Gembicki, F. W. (1974). Vector Optimization for Control with Performance and Parameter Sensitivity Indices. PhD thesis, Case Western Reserve University, Cleveland, Ohio, USA.
  10. Horn. J. 1997. Multi criterion decision making. In T. Back, D. Fogen and Z. Michalewicz (Eds), Handbook of Evolutionary computation, pp. F1. 9: 1-15. Bristol: Institute of Physics Publishing and New York Oxford University Press.
  11. Louis, S. J. and Rawlins, G. J. E. 1993. Pareto optimality, GA-easiness and deception. In (Forrest, 1993), pages 118-123.
  12. Parmee, I. C. Cevkovic, D. Watson, A. W. and Bonham, C. R. 2000. Multiobjective satisfaction within an interactive evolutionary design environment. Evolutionary Computation Journal 8(2), 197-222
  13. Rajeshwar S. Kadadevaramath, K. M. Mohanasudaram 2007, "Multi-Objective Trade-off Analysis: State of art: Methods, Applications, and future Research Directions in Production and Operations Management"
  14. Schaffer, J. D. 1985. Multiple objective optimization with vector evaluated genetic algorithms. In (Grefenstette, 1985), pages 93-100.


Multi-state, Multi-task, Manufacturing Systems, Performance, Availability, Feasibility, Optimization, Priority Scheduling