Fuzzy chance constrained linear programming model for optimizing the scrap charge in steel production

Optimizing the charge in secondary steel production is challenging because the chemical composition of the scrap is highly uncertain. The uncertainty can cause a considerable risk of the scrap mix failing to satisfy the composition requirements for the final product. In this paper, we represent the...

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Veröffentlicht in:European journal of operational research Jg. 186; H. 3; S. 953 - 964
Hauptverfasser: Rong, Aiying, Lahdelma, Risto
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.05.2008
Elsevier
Elsevier Sequoia S.A
Schriftenreihe:European Journal of Operational Research
Schlagworte:
Applied sciences
Chance constraint
Exact sciences and technology
Fuzzy sets
Linear programming
Operational research. Management science
Optimization techniques
Production methods
Scrap
Scrap charge optimization
Steel production
Studies
Steel production
Fuzzy sets
Linear programming
Scrap charge optimization
Chance constraint
Material costs
Product specification
Rupture
Fuzzy programming
Fuzzy set
Soft constraint
Failures
Modeling
Linear model
Optimization
Confidence interval
Fuzzy logic
Uncertain system
Model specification
Aspiration level
Fuzzy set theory
Fuzzy number
Production process
ISSN:0377-2217, 1872-6860
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Zusammenfassung:Optimizing the charge in secondary steel production is challenging because the chemical composition of the scrap is highly uncertain. The uncertainty can cause a considerable risk of the scrap mix failing to satisfy the composition requirements for the final product. In this paper, we represent the uncertainty based on fuzzy set theory and constrain the failure risk based on a possibility measure. Consequently, the scrap charge optimization problem is modeled as a fuzzy chance constrained linear programming problem. Since the constraints of the model mainly address the specification of the product, the crisp equivalent of the fuzzy constraints should be less relaxed than that purely based on the concept of soft constraints. Based on the application context we adopt a strengthened version of soft constraints to interpret fuzzy constraints and form a crisp model with consistent and compact constraints for solution. Simulation results based on realistic data show that the failure risk can be managed by proper combination of aspiration levels and confidence factors for defining fuzzy numbers. There is a tradeoff between failure risk and material cost. The presented approach applies also for other scrap-based production processes.
Bibliographie:SourceType-Scholarly Journals-1
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2007.02.017