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Redefining Event Variables for Efficient Modeling of Continuous-Time Batch Processing

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  • Siqun Wang
  • Monique Guignard

Abstract

We define events so as to reduce the number of events and decision variables needed for modeling batch-scheduling problems such as described in [15]. We propose a new MILP formulation based on this concept, defining non-uniform time periods as needed and decision variables that are not time-indexed. It can handle complicated multi-product/multi-stage machine processes, with production lines merging and diverging, and with minimum and maximum batch sizes. We compare it with earlier models and show that it can solve problems with small to medium demands relative to batch sizes in reasonable computer times. Copyright Kluwer Academic Publishers 2002

Suggested Citation

  • Siqun Wang & Monique Guignard, 2002. "Redefining Event Variables for Efficient Modeling of Continuous-Time Batch Processing," Annals of Operations Research, Springer, vol. 116(1), pages 113-126, October.
    • Handle: RePEc:spr:annopr:v:116:y:2002:i:1:p:113-126:10.1023/a:1021372029962
    DOI: 10.1023/A:1021372029962
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    Cited by:

    1. Christodoulos Floudas & Xiaoxia Lin, 2005. "Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications," Annals of Operations Research, Springer, vol. 139(1), pages 131-162, October.
    2. Sumit Bose & Subir Bhattacharya, 2008. "A two pass heuristic algorithm for scheduling ‘blocked out’ units in continuous process industry," Annals of Operations Research, Springer, vol. 159(1), pages 293-313, March.

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