Expanding scope and computational challenges in process scheduling

Pedro M. Castro, Ignacio E. Grossmann, Qi Zhang

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

In this paper, we present a brief overview of enterprise-wide optimization and challenges in multiscale temporal modeling and integration of different models for the levels of planning, scheduling and control. Next, we review Generalized Disjunctive Programming (GDP), as a new modeling paradigm for scheduling problems that are illustrated with the STN and RTN models. We then address scheduling problems that expand the scope of the area: simultaneous scheduling and heat integration, pipeline scheduling, crude oil and refined products blending, and demand side management. We illustrate the advantage of the GDP modeling framework, describe effective strategies for global optimization, and describe multistage affinely adjustable robust optimization for uncertain interruptible load. We address integration of planning and scheduling, for which several approaches are reviewed, including use of traveling salesman constraints for multiperiod refinery planning, and multisite planning and scheduling of multiproduct batch plants. We report computational results to highlight the challenges.

Original languageEnglish (US)
Pages (from-to)14-42
Number of pages29
JournalComputers and Chemical Engineering
Volume114
DOIs
StatePublished - Jun 9 2018
Externally publishedYes

Keywords

  • Demand side management
  • Generalized disjunctive programming
  • Mixed-integer programming
  • Planning
  • Scheduling

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