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Supply chain optimization for refinery with considerations of operation mode changeover and yield fluctuations / Jiali Yang in Industrial & engineering chemistry research, Vol. 49 N° 1 (Janvier 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 1 (Janvier 2010) . - pp. 276–287 Titre : Supply chain optimization for refinery with considerations of operation mode changeover and yield fluctuations Type de document : texte imprimé Auteurs : Jiali Yang, Auteur ; Haijie Gu, Auteur ; Rong, Gang, Auteur Année de publication : 2010 Article en page(s) : pp. 276–287 Note générale : Industrail chemistry Langues : Anglais (eng) Mots-clés : Supply--Chain--Optimization--Refinery--Considerations--Operation--Changeover--Yield  Fluctuations Résumé : Stochastic programming is employed to achieve optimization for the multiperiod supply chain problem in a refinery with multiple operation modes under the uncertainty of product yields. With dramatic fluctuations of product yields at the beginning of operation mode changeover, the product yields tends to stabilize after the changeover is finished. Markov chain is utilized here to describe the dynamic characteristic of product yield fluctuations. The distribution of yield fluctuation in each period is usually unknown since it depends on the decision variable of operation mode changeover. Therefore, the resulting chance constrained programming is more complicated than general situations where the distribution characteristic of stochastic variable is known in each period. This problem can be solved by the big-M method and by transforming chance constrained inequalities into a group of equivalent deterministic inequalities. This method provides a universal approach for similar chance constrained programming in which the distribution of stochastic variable depends on binary decision variables. Case studies show that the proposed modeling and solving approach can provide an effective decision-making guidance that balances confidence level and economic interests for supply chain optimization problems with multiple operation modes under yield uncertainty. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900968x [article] Supply chain optimization for refinery with considerations of operation mode changeover and yield fluctuations [texte imprimé] / Jiali Yang, Auteur ; Haijie Gu, Auteur ; Rong, Gang, Auteur . - 2010 . - pp. 276–287. Industrail chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 1 (Janvier 2010) . - pp. 276–287 Mots-clés : Supply--Chain--Optimization--Refinery--Considerations--Operation--Changeover--Yield  Fluctuations Résumé : Stochastic programming is employed to achieve optimization for the multiperiod supply chain problem in a refinery with multiple operation modes under the uncertainty of product yields. With dramatic fluctuations of product yields at the beginning of operation mode changeover, the product yields tends to stabilize after the changeover is finished. Markov chain is utilized here to describe the dynamic characteristic of product yield fluctuations. The distribution of yield fluctuation in each period is usually unknown since it depends on the decision variable of operation mode changeover. Therefore, the resulting chance constrained programming is more complicated than general situations where the distribution characteristic of stochastic variable is known in each period. This problem can be solved by the big-M method and by transforming chance constrained inequalities into a group of equivalent deterministic inequalities. This method provides a universal approach for similar chance constrained programming in which the distribution of stochastic variable depends on binary decision variables. Case studies show that the proposed modeling and solving approach can provide an effective decision-making guidance that balances confidence level and economic interests for supply chain optimization problems with multiple operation modes under yield uncertainty. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900968x