State-of-the-art review of optimization methods for short-term scheduling of batch processes

Research output: Contribution to journalReview Article

Researchers

Research units

  • Carnegie Mellon University
  • Consejo Nacional de Investigaciones Científicas y Técnicas
  • ABB Group

Abstract

There has been significant progress in the area of short-term scheduling of batch processes, including the solution of industrial-sized problems, in the last 20 years. The main goal of this paper is to provide an up-to-date review of the state-of-the-art in this challenging area. Main features, strengths and limitations of existing modeling and optimization techniques as well as other available major solution methods are examined through this paper. We first present a general classification for scheduling problems of batch processes as well as for the corresponding optimization models. Subsequently, the modeling of representative optimization approaches for the different problem types are introduced in detail, focusing on both discrete and continuous time models. A comparison of effectiveness and efficiency of these models is given for two benchmarking examples from the literature. We also discuss two real-world applications of scheduling problems that cannot be readily accommodated using existing methods. For the sake of completeness, other alternative solution methods applied in the field of scheduling are also reviewed, followed by a discussion related to solving large-scale problems through rigorous optimization approaches. Finally, we list available academic and commercial software, and briefly address the issue of rescheduling capabilities of the various optimization approaches as well as important extensions that go beyond short-term batch scheduling.

Details

Original languageEnglish
Pages (from-to)913-946
Number of pages34
JournalComputers and Chemical Engineering
Volume30
Issue number6-7
Publication statusPublished - 15 May 2006
MoE publication typeA2 Review article in a scientific journal

    Research areas

  • Batch processes, MILP, Optimization models, Short-term scheduling

ID: 6322544