A new time-bucket MILP formulation for optimal lot-sizing and scheduling of an industrial make-and-fill process

Roderich Wallrath, Florian Seanner, Matthias Lampe, Meik Franke

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

We propose a new time-bucket MILP model for lot-sizing and scheduling problems arising in multistage production processes. The time-bucket model benefits from advantages of both continuous and discrete time representations while overcoming their shortcomings. In particular, we show how the time-bucket model allows to easily include a variety of typical, important real-world constraints, can be solved with moderate computer effort, and thus promotes MILP for large-scale, industrial problems. To illustrate that, we apply the time-bucket approach to the flow shop problem of a batch formulation and filling process from an industrial pesticide production. We reconcile the MILP solution with a validated discrete event simulation (DES) model of the process to obtain optimal and real-world feasible results. A comparison of the MILP-DES solution to a manually optimized solution for a one-month production data set shows that more than 17% of production capacity can be freed up and significant improvement in on-time delivery.

Original languageEnglish
Title of host publication33rd European Symposium on Computer Aided Process Engineering
PublisherElsevier
Pages1853-1859
Number of pages7
DOIs
Publication statusPublished - 18 Jul 2023
Event33rd European Symposium on Computer Aided Process Engineering, ESCAPE 2023 - Athens, Greece
Duration: 18 Jun 202321 Jun 2023
Conference number: 33

Publication series

NameComputer Aided Chemical Engineering
Volume52
ISSN (Print)1570-7946

Conference

Conference33rd European Symposium on Computer Aided Process Engineering, ESCAPE 2023
Abbreviated titleESCAPE 2023
Country/TerritoryGreece
CityAthens
Period18/06/2321/06/23

Keywords

  • discrete event systems in manufacturing
  • mixed-integer linear programming
  • modeling of manufacturing operations
  • Production planning and scheduling
  • NLA

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