Computational enhancements of continuous production scheduling MILPs using tightening constraints

Research output: Contribution to journalArticlepeer-review


In this paper, we propose employing tightening constraints, based on known system information, into discrete-time continuous production scheduling models to enhance their computational performance. We first establish a model with transient operations such as startups, shutdowns, and direct transitions. We propose a demand propagation algorithm (DPA), implemented as a preprocessing step, that utilizes demand information and other known system parameters to calculate parameters which are later used in a series of constraints to tighten the feasible space of the linear programming (LP) relaxation of the mixed-integer linear programming (MILP) problem. This reduces the branching required to close the optimality gap which consequently decreases solution times. We present computational results that show our proposed method can lead to over an order of magnitude reduction in solution times.

Original languageEnglish (US)
Article number108609
JournalComputers and Chemical Engineering
StatePublished - May 2024
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Chemical Engineering
  • Computer Science Applications


  • Bound propagation
  • Continuous processes
  • Discrete-time
  • Preprocessing algorithm


Dive into the research topics of 'Computational enhancements of continuous production scheduling MILPs using tightening constraints'. Together they form a unique fingerprint.

Cite this