A systematic framework for enterprise-wide optimization: Synthesis and design of processing networks under uncertainty

• Published in: Computers & chemical engineering Vol. 59; pp. 47 - 62
• Main Authors: Quaglia, Alberto, Sarup, Bent, Sin, Gürkan, Gani, Rafiqul
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Kidlington Elsevier Ltd 05.12.2013; Elsevier
• Subjects: Applied sciences; Biological and medical sciences; Decision theory. Utility theory; Decision-making under uncertainty; Enterprise-wide optimization; Exact sciences and technology; Fat industries; Firm modelling; Food industries; Fundamental and applied biological sciences. Psychology; Integrated business and engineering; Mathematical programming; Mixed-integer non-linear program (MINLP); Operational research and scientific management; Operational research. Management science; Process synthesis; Product portfolio management; Vegetable oil; Integrated business and engineering; Enterprise-wide optimization; Process synthesis; Decision-making under uncertainty; Vegetable oil; Mixed-integer non-linear program (MINLP); Product portfolio management; Performance evaluation; Firm development; Decomposition method; Non linear programming; Uncertain system; Efficiency; Database; Tool design; Soybean oil; Numerical integration; Decision support system; Decision making; Benchmarking; Linear programming; Mixed integer programming; Stochastic programming; Case study; Integer programming; Product structure; industrial engineering; Non linear effect; Portfolio management
• ISSN: 0098-1354; 1873-4375; 1873-4375
• DOI: 10.1016/j.compchemeng.2013.03.018

•Development of a systematic synthesis/design methodology for processing networks.•Integration of different uncertainty analysis tools in the decision making.•The business and the engineering aspects of the problem are solved simultaneously.•A network benchmark problem is proposed and solved.•The methodology is applied to synthesis and design of soybean processing networks. In this paper, a systematic framework for synthesis and design of processing networks under uncertainty is presented. Through the framework, an enterprise-wide optimization problem is formulated and solved under uncertain conditions, to identify the network (composed of raw materials, process technologies and product portfolio) which is feasible and have optimal performances over the entire uncertainty domain. Through the integration of different methods, tools, algorithms and databases, the framework guides the user in dealing with the mathematical complexity of the problems, allowing efficient formulation and solution of large and complex enterprise-wide optimization problem. Tools for the analysis of the uncertainty, of its consequences on the decision-making process and for the identification of strategies to mitigate its impact on network performances are integrated in the framework. A decomposition-based approach is employed to deal with the added complexity of the optimization under uncertainty. A network benchmarking problem is proposed as a benchmark for further development of methods, tools and solution approaches. To highlight the features of the framework, a large industrial case study dealing with soybean processing is formulated and solved.