Improved Steel Production Planning through Data Analysis and Optimization

  • The following thesis addresses several shortcomings in the academic literature surrounding steel production planning. These shortcomings are summarized as (i) failure to incorporate tacit production knowledge into steel decision support systems, (ii) negligence of physical boundaries / limitations of the steel manufacturing process, and (iii) lack of global (as opposed to local) optimization approaches in steel scheduling algorithms. Not incorporating tacit production knowledge typically leads to wrong decision advice and sub-optimal decision-making, whereas neglecting the theoretical output limits of the steel production process can cause unnecessary machine breakdowns and diminished productivity; also, if production scheduling algorithms focus too much on specific sub-elements of the optimization problem (e.g. upstream production lines), this will adverse effects on the remaining elements (e.g. downstream production lines). To overcome these shortcomings, different methodologies are applied: (i) Through a mixture of association rules mining and complex network analysis, we extract production knowledge hidden in historical production data that documents the decisions of a human expert planner; this extracted knowledge could now be utilized by decision support systems. (ii) In historical production data, we identify a theoretical upper limit of the casting speed that mitigates the productivity of continuous casters; adapting the average casting speed to this limit could increase the production output. This phenomenon is further investigated through minimal models of production systems which are characterized by (a) stochastic production inputs and (b) disruptive thresholds on the production output. (iii) We develop two genetic algorithms out of which the first algorithm optimizes multiple production sequences at the same time as opposed to one after another, while the second algorithm simultaneously generates schedules for two production processes.

Download full text

Cite this publication

  • Export Bibtex
  • Export RIS

Citable URL (?):

Search for this publication

Search Google Scholar Search Catalog of German National Library Search OCLC WorldCat Search Bielefeld Academic Search Engine
Meta data
Publishing Institution:IRC-Library, Information Resource Center der Constructor University
Granting Institution:Constructor Univ.
Author:Daniel Christopher Merten
Referee:Yilmaz Uygun, Stanislav Chankov, Atilla Özgür
Advisor:Marc-Thorsten Hütt
Persistent Identifier (URN):urn:nbn:de:gbv:579-opus-1011405
Document Type:PhD Thesis
Date of Successful Oral Defense:2023/02/02
Date of First Publication:2023/02/17
Academic Department:Computer Science & Electrical Engineering
PhD Degree:Data Engineering
Call No:2023/2

$Rev: 13581 $