Multiobjective evolutionary optimization of batch process scheduling under environmental and economic concerns

The simultaneous consideration of economic and environmental objectives in batch production scheduling is today a subject of major concern. However, it constitutes a complex problem whose solution necessarily entails production trade‐offs. Unfortunately, a rigorous multiobjective optimization approa...

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Bibliographic Details
Published in:AIChE journal 2013-02, Vol.59 (2), p.429-444
Main Authors: Capón-García, Elisabet, Bojarski, Aarón D., Espuña, Antonio, Puigjaner, Luis
Format: Article
Language:English
Subjects:
Aparells i instruments
Aspectes econòmics
Batch production
Chemical engineering
Data processing
Economics
Enginyeria química
Environmental objective
Evolutionary
Evolutionary algorithms
Genetic algorithms
Indústria dels processos químics
LCA
Multiobjective optimization
Multiproduct batch scheduling
Optimization
Optimization techniques
Plant chemical ecology
Plantes químiques
Product changeover
Production scheduling
Scheduling
Sequence dependency
Stands
Strategy
Tradeoffs
Àrees temàtiques de la UPC
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Summary:The simultaneous consideration of economic and environmental objectives in batch production scheduling is today a subject of major concern. However, it constitutes a complex problem whose solution necessarily entails production trade‐offs. Unfortunately, a rigorous multiobjective optimization approach to solve this kind of problem often implies high computational effort and time, which seriously undermine its applicability to day‐to‐day operation in industrial practice. Hence, this work presents a hybrid optimization strategy based on rigorous local search and genetic algorithm to efficiently deal with industrial scale batch scheduling problems. Thus, a deeper insight into the combined environmental and economic issues when considering the trade‐offs of adopting a particular schedule is provided. The proposed methodology is applied to a case study concerning a multiproduct acrylic fiber production plant, where product changeovers influence the problem results. The proposed strategy stands for a marked improvement in effectively incorporating multiobjective optimization in short‐term plant operation. © 2012 American Institute of Chemical Engineers AIChE J, 59: 429–444, 2013
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.13841