Solving Multi-objective MILP Problems in Process Synthesis using the Multi-Criteria Branch and Bound Algorithm

The paper briefly describes the problem of process synthesis in the area of chemical engineering, and suggests its formulation as a Multi‐Objective Programming problem. Process synthesis optimization is usually modeled as Mixed Integer Linear Programming (MILP) or Mixed Integer Non‐Linear Programmin...

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Veröffentlicht in:Chemical engineering & technology Jg. 28; H. 12; S. 1500 - 1510
Hauptverfasser: Mavrotas, G., Diakoulaki, D.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Weinheim WILEY-VCH Verlag 01.12.2005
WILEY‐VCH Verlag
Wiley-VCH
Schlagworte:
Applications of mathematics to chemical engineering. Modeling. Simulation. Optimization
Applied sciences
Chemical engineering
Exact sciences and technology
Optimization
Process engineering
Process optimization
Chemical engineering
Branch and bound method
Linear programming
Mixed integer programming
Non linear programming
Objective function
Combinatorial optimization
Algorithm
Modeling
Mathematical programming
ISSN:0930-7516, 1521-4125
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Zusammenfassung:The paper briefly describes the problem of process synthesis in the area of chemical engineering, and suggests its formulation as a Multi‐Objective Programming problem. Process synthesis optimization is usually modeled as Mixed Integer Linear Programming (MILP) or Mixed Integer Non‐Linear Programming (MINLP) with an economic objective function. We claim that incorporating more criteria (e.g., environmental criteria) in this kind of combinatorial optimization problem offers the decision makers the opportunity to refine their final decision by examining more than one solution (a set of efficient or Pareto optimal solutions instead of one optimal solution). For solving the multi‐objective process synthesis problem, an improved version of the Multi‐Criteria Branch and Bound (MCBB) algorithm, which has been developed by the same authors, is used. MCBB is a vector maximization algorithm capable of deriving all efficient points (supported and unsupported), for small and medium sized Multi‐Objective MILP problems. The application of MCBB in two examples from process synthesis is also presented. The formulation of process synthesis in chemical engineering as a Multi‐Objective Programming is discussed. An improved version of the Multi‐Criteria Branch and Bound (MCBB) algorithm is used, which is a vector maximization algorithm capable of deriving all efficient points (supported and unsupported) for small and medium sized Multi‐Objective MILP problems. The application of MCBB in two examples from process synthesis is presented.
Bibliographie:ark:/67375/WNG-HJ8BVBLX-5
istex:6CA38A0CC40B0748E46195B87EDE49ACDC1BBA05
ArticleID:CEAT200500135
ISSN:0930-7516
1521-4125
DOI:10.1002/ceat.200500135