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Strategic capacity allocation under uncertainty by using a two-stage stochastic decomposition algorithm with incumbent solutions / Sergio Frausto-Hernandez in Industrial & engineering chemistry research, Vol. 49 N° 6 (Mars 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 6 (Mars 2010) . - pp. 2812–2821 Titre : Strategic capacity allocation under uncertainty by using a two-stage stochastic decomposition algorithm with incumbent solutions Type de document : texte imprimé Auteurs : Sergio Frausto-Hernandez, Auteur ; Rico-Ramirez, Vicente, Auteur ; Ignacio E. Grossmann, Auteur Année de publication : 2010 Article en page(s) : pp. 2812–2821 Note générale : Industrial Chemistry Langues : Anglais (eng) Mots-clés : Strategic  Capacity;  under  Uncertainty;  Stochastic;  Decomposition;  Algorithm;  Incumbent Résumé : This paper addresses the optimization of strategic capacity allocation problems under uncertainty. Uncertain parameters include the availabilities and the demands of chemicals. The optimization problem is formulated as a two-stage mixed-integer stochastic program (first-stage integer) that maximizes the expected net present value of the project over the time horizon. The net present value includes the discounted investment costs, operating costs, and revenues from sales in the markets. The decisions about the selection of processes and about the plant capacities are defined as the first stage decision variables; given such first stage decisions, recourse is then provided by calculating the operating costs in the second stage. The paper presents the two-stage model and the solution framework. A stochastic decomposition algorithm (SD) based on incumbent solution is used as the solution algorithm. The use of incumbent solutions as convergence criterion allows us to solve the problem in reasonable computational time. Two capacity allocation problems are used as case studies: the first one is a small size problem and is used to compare the stochastic optimal solution against other solution methods; the second one is a larger problem, with an increased number of variables and stochastic parameters. The results show that the stochastic decomposition algorithm with incumbent solutions is stable and robust and greatly reduces the number of samples required for convergence. Note de contenu : Bibiogr. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901021b [article] Strategic capacity allocation under uncertainty by using a two-stage stochastic decomposition algorithm with incumbent solutions [texte imprimé] / Sergio Frausto-Hernandez, Auteur ; Rico-Ramirez, Vicente, Auteur ; Ignacio E. Grossmann, Auteur . - 2010 . - pp. 2812–2821. Industrial Chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 6 (Mars 2010) . - pp. 2812–2821 Mots-clés : Strategic  Capacity;  under  Uncertainty;  Stochastic;  Decomposition;  Algorithm;  Incumbent Résumé : This paper addresses the optimization of strategic capacity allocation problems under uncertainty. Uncertain parameters include the availabilities and the demands of chemicals. The optimization problem is formulated as a two-stage mixed-integer stochastic program (first-stage integer) that maximizes the expected net present value of the project over the time horizon. The net present value includes the discounted investment costs, operating costs, and revenues from sales in the markets. The decisions about the selection of processes and about the plant capacities are defined as the first stage decision variables; given such first stage decisions, recourse is then provided by calculating the operating costs in the second stage. The paper presents the two-stage model and the solution framework. A stochastic decomposition algorithm (SD) based on incumbent solution is used as the solution algorithm. The use of incumbent solutions as convergence criterion allows us to solve the problem in reasonable computational time. Two capacity allocation problems are used as case studies: the first one is a small size problem and is used to compare the stochastic optimal solution against other solution methods; the second one is a larger problem, with an increased number of variables and stochastic parameters. The results show that the stochastic decomposition algorithm with incumbent solutions is stable and robust and greatly reduces the number of samples required for convergence. Note de contenu : Bibiogr. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901021b