Mixed-Integer Multiobjective Process Planning under Uncertainty

This paper presents a methodology for addressing investment planning in the process industry using a mixed-integer multiobjective approach. The classic single-objective MILP stochastic model is treated as a multiobjective programming problem by the use of multiparametric decomposition. To ensure com...

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Bibliographic Details
Published in:Industrial & engineering chemistry research 2002-08, Vol.41 (16), p.4075-4084
Main Authors: Rodera, Hernán, Bagajewicz, Miguel J., Trafalis, Theodore B.
Format: Article
Language:English
Subjects:
Applied sciences
Exact sciences and technology
Operational research and scientific management
Operational research. Management science
Planning. Forecasting
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Summary:This paper presents a methodology for addressing investment planning in the process industry using a mixed-integer multiobjective approach. The classic single-objective MILP stochastic model is treated as a multiobjective programming problem by the use of multiparametric decomposition. To ensure computation of the entire efficient frontier, use of the augmented Tchebycheff algorithm is proposed. This allows for the decision making to be based on the suggested solutions and their neighbor solutions, a feature that other stochastic programming models fail to capture. An iterative procedure is proposed to help the decision maker visualize the efficient solutions in the multidimensional space and facilitate the assessment of the economical risk of the project.
ISSN:0888-5885
1520-5045
DOI:10.1021/ie010530j