Adjustable Robust Optimization for Scheduling of Batch Processes under Uncertainty

Hanyu Shi, Fengqi You

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this work, we hedge against the uncertainty in the of batch process scheduling by using a novel two-stage adjustable robust optimization (ARO) approach. We introduce symmetric uncertainty sets into the deterministic mixed-integer linear programming (MILP) model for batch scheduling problem and then reformulate it into a two-stage problem. The budgets of uncertainty is used to adjust the degree of conservatism. Since the resulting two-stage ARO problem cannot be solved directly by any existing optimizer, the column-and-constraint generation (C&CG) algorithm is then applied to solve it efficiently. One case study for batch manufacturing processes is considered to demonstrate the validation of the two-stage ARO model formulation and the efficiency of the C&CG algorithm.

Original languageEnglish (US)
Title of host publication26 European Symposium on Computer Aided Process Engineering, 2016
PublisherElsevier B.V.
Pages547-552
Number of pages6
Volume38
ISBN (Print)9780444634283
DOIs
StatePublished - Jan 1 2016

Publication series

NameComputer Aided Chemical Engineering
Volume38
ISSN (Print)1570-7946

Keywords

  • batch processes
  • column-and-constraint generation algorithm
  • scheduling
  • two-stage adaptive robust optimization

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

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