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Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications

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  • Christodoulos Floudas
  • Xiaoxia Lin

Abstract

This paper reviews the advances of mixed-integer linear programming (MILP) based approaches for the scheduling of chemical processing systems. We focus on the short-term scheduling of general network represented processes. First, the various mathematical models that have been proposed in the literature are classified mainly based on the time representation. Discrete-time and continuous-time models are presented along with their strengths and limitations. Several classes of approaches for improving the computational efficiency in the solution of MILP problems are discussed. Furthermore, a summary of computational experiences and applications is provided. The paper concludes with perspectives on future research directions for MILP based process scheduling technologies. Copyright Springer Science + Business Media, Inc. 2005

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  • 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.
    • Handle: RePEc:spr:annopr:v:139:y:2005:i:1:p:131-162:10.1007/s10479-005-3446-x
    DOI: 10.1007/s10479-005-3446-x
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    4. 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.
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