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A minimum variance control theory perspective on supply chain lead time uncertainty / Hua Xu in Industrial & engineering chemistry research, Vol. 51 N° 27 (Juillet 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 27 (Juillet 2012) . - pp. 9275-9286 Titre : A minimum variance control theory perspective on supply chain lead time uncertainty Type de document : texte imprimé Auteurs : Hua Xu, Auteur ; Rong, Gang, Auteur Année de publication : 2012 Article en page(s) : pp. 9275-9286 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Uncertainty Résumé : This paper addresses the lead time uncertainty problem in supply chain systems. In our previous paper [Xu et al. Ind. Eng. Chem. Res.2010,49,8644], we have investigated the impact of demand uncertainty on supply chains. Here we adopt a two-echelon supply chain model, which is basically the same as that used in the research of demand uncertainty. But the fixed lead time setting is replaced by a Markovian lead time model. Since the lead time varies with time, the dynamic characteristics of the supply chain model are different from that used in the demand uncertainty research. We make a comparison analysis of these differences from the view of dynamic systems. On the basis of the above analysis, we adopt two fundamental lemmas of the minimum variance control theory as the foundation for replenishment rules design and analysis. Then we derive formulas of the Order-up-to policy and the generalized Order-up-to policy with time-varying lead time. Moreover, we offer the variant forms of the above strategies when the lead time information is incomplete. Given the strategies, we analyze the influence of lead time information on the order and inventory variances and corresponding costs. This work, together with our previous paper on demand uncertainty, may provide a coherent control theory based perspective on these two different types of uncertainties in a supply chain. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26132264 [article] A minimum variance control theory perspective on supply chain lead time uncertainty [texte imprimé] / Hua Xu, Auteur ; Rong, Gang, Auteur . - 2012 . - pp. 9275-9286. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 27 (Juillet 2012) . - pp. 9275-9286 Mots-clés : Uncertainty Résumé : This paper addresses the lead time uncertainty problem in supply chain systems. In our previous paper [Xu et al. Ind. Eng. Chem. Res.2010,49,8644], we have investigated the impact of demand uncertainty on supply chains. Here we adopt a two-echelon supply chain model, which is basically the same as that used in the research of demand uncertainty. But the fixed lead time setting is replaced by a Markovian lead time model. Since the lead time varies with time, the dynamic characteristics of the supply chain model are different from that used in the demand uncertainty research. We make a comparison analysis of these differences from the view of dynamic systems. On the basis of the above analysis, we adopt two fundamental lemmas of the minimum variance control theory as the foundation for replenishment rules design and analysis. Then we derive formulas of the Order-up-to policy and the generalized Order-up-to policy with time-varying lead time. Moreover, we offer the variant forms of the above strategies when the lead time information is incomplete. Given the strategies, we analyze the influence of lead time information on the order and inventory variances and corresponding costs. This work, together with our previous paper on demand uncertainty, may provide a coherent control theory based perspective on these two different types of uncertainties in a supply chain. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26132264

Robust optimization model for crude oil scheduling under uncertainty / Wang, Jishuai in Industrial & engineering chemistry research, Vol. 49 N° 4 (Fevrier 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 4 (Fevrier 2010) . - pp 1737–1748 Titre : Robust optimization model for crude oil scheduling under uncertainty Type de document : texte imprimé Auteurs : Wang, Jishuai, Auteur ; Rong, Gang, Auteur Année de publication : 2010 Article en page(s) : pp 1737–1748 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Crude  oil  Optimization. Résumé : In this article, a two-stage robust model is proposed to solve the crude oil scheduling problem under uncertain conditions. The first stage of the model is developed using chance-constrained programming and fuzzy programming that can be transformed into the deterministic counterpart problem, whereas the second-stage is scenario-based. Through the combination of the approaches, the two-stage model can deal with uncertain parameters with both continuous and discrete probability distributions within a finite number of scenarios. The model was tested on several small examples and an industrial-size case. Uncertainties were introduced in ship arrival times and fluctuating product demands. The computational results demonstrate the effectiveness and robustness of the proposed approach. The tradeoff between solution robustness and model robustness was also analyzed. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900358z [article] Robust optimization model for crude oil scheduling under uncertainty [texte imprimé] / Wang, Jishuai, Auteur ; Rong, Gang, Auteur . - 2010 . - pp 1737–1748. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 4 (Fevrier 2010) . - pp 1737–1748 Mots-clés : Crude  oil  Optimization. Résumé : In this article, a two-stage robust model is proposed to solve the crude oil scheduling problem under uncertain conditions. The first stage of the model is developed using chance-constrained programming and fuzzy programming that can be transformed into the deterministic counterpart problem, whereas the second-stage is scenario-based. Through the combination of the approaches, the two-stage model can deal with uncertain parameters with both continuous and discrete probability distributions within a finite number of scenarios. The model was tested on several small examples and an industrial-size case. Uncertainties were introduced in ship arrival times and fluctuating product demands. The computational results demonstrate the effectiveness and robustness of the proposed approach. The tradeoff between solution robustness and model robustness was also analyzed. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900358z

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