Advertisement

A New Global Optimization Algorithm for Batch Process Scheduling

  • Linas Mockus
  • Gintaras V. Reklaitis
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 7)

Abstract

A general framework for handling a wide range of short-term scheduling problems arising in multiproduct/multi-purpose batch chemical plants is presented. Time events arising in the schedule are modeled directly and thus the use of binary variables over periods during which no changes in system state occur is avoided. The problem is formulated as a mixed integer nonlinear program (MINLP). The Bayesian Heuristic (BH) approach is used to implement a global optimization algorithm which effectively solves the resulting model. Computational comparisons using two test examples are made against a UDM (uniform discretization model) formulation. The results suggest that the BH approach combined with the nonuniform time discretization formulation shows promise for the solution of batch scheduling problems.

Keywords

Optimization global batch scheduling event timing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Das H., P.T. Cummings, find M.D. La Van, Scheduling of serial multiproduct batch processes via simulated annealing. Computers Chem. Engng, 14, 1990, pp. 1351–1362.CrossRefGoogle Scholar
  2. 2.
    Floquet P., P. Pibouleau, and S. Domenech, Scheduling and simulated annealing application to a semiconductor circuit fabrication plant. Computers Chem. Engng, 17, 1993, pp. 39–44.Google Scholar
  3. 3.
    Mockus J., Bayesian approach to global optimization. Kluwer academic publishers, Dordrecht-London-Boston, 1989.zbMATHCrossRefGoogle Scholar
  4. 4.
    Mockus A., J. Mockus and L. Mockus, Bayesian approach adapting stochastic and heuristic methods of global and discrete optimization. INFORMATICA, 5, 1994, pp. 123–166.MathSciNetzbMATHGoogle Scholar
  5. 5.
    Mockus L. and G.V. Reklaitis, Mathematical programming formulation for scheduling of batch operations using nonuniform time discretization. AIChE annual meeting, Paper No. 235d, San-Francisco, California, 1994.Google Scholar
  6. 6.
    Patel A.N., R.S.H. Mah, and I.A. Karimi, Preliminary design of multiproduct noncontinuous plants using simulated annealing. Computers Chem. Engng, 15, 1991, pp. 451–469.CrossRefGoogle Scholar
  7. 7.
    Sahinidis N.V. and I.E. Grossmann, Reformulation of multiperiod MILP models for planning and scheduling of chemical processes. Computers Chem. Engng 15, 1991, pp. 255–272.CrossRefGoogle Scholar
  8. 8.
    Tandon M., P.T. Cummings, and M.D. La Van, Flowshop sequencing with non-permutation schedules. Computers Chem. Engng, 15, 1991, pp. 601–607.CrossRefGoogle Scholar
  9. 9.
    Xueya Z. and R.W.H. Sargent, A new unified formulation for process scheduling. AIChE annual meeting, Paper No. 144c, St.Louis, Missouri, 1993.Google Scholar
  10. 10.
    Zentner M.G. and G.V. Reklaitis, An interval-based mathematical model for the scheduling of resource-constrained batch chemical processes. Proc. NATO A SI on Batch Processing Systems Engineering, Antalya, Turkey, 1992.Google Scholar
  11. 11.
    Zentner M.G., J.F. Pekny, D.L. Miller and G.V. Reklaitis, RCSP++: A scheduling system for the chemical process industry. Proc. PSE’94, Kyongju, Korea, 1994, pp. 491–495.Google Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Linas Mockus
    • 1
  • Gintaras V. Reklaitis
    • 1
  1. 1.School of Chemical EngineeringPurdue UniversityWest LafayetteUSA

Personalised recommendations