Capacity planning under clinical trials uncertainty in continuous pharmaceutical manufacturing, 1 / Arul Sundaramoorthy in Industrial & engineering chemistry research, Vol. 51 N° 42 (Octobre 2012)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 51 N° 42 (Octobre 2012) . - pp. 13692–13702 Titre : Capacity planning under clinical trials uncertainty in continuous pharmaceutical manufacturing, 1 : Mathematical framework Type de document : texte imprimé Auteurs : Arul Sundaramoorthy, Auteur ; James M. B. Evans, Auteur ; Paul I. Barton, Auteur Année de publication : 2012 Article en page(s) : pp. 13692–13702 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Pharmaceutical manufacturing Résumé : Unlike traditional batch-based pharmaceutical manufacturing, where the active pharmaceutical ingredient (API) and the final drug product are often produced in different facilities at different locations, novel continuous pharmaceutical manufacturing strategies enable the production of both the API and the final drug product in the same integrated facility. The capacities of such integrated continuous facilities must be determined for potential products in the face of clinical trials uncertainty. Given a portfolio consisting of potential products in the development stage, the goal of capacity planning is to ensure the availability of enough production capacity to meet the projected demands of products, which vary from the launch to the peak-demand periods. To address this problem, we propose a multiscenario, multiperiod, mixed-integer linear programming (MILP) formulation that takes into account uncertainty in the outcome of clinical trials. We illustrate the proposed framework using several examples. The exponential increase in problem size with the number of products motivates us to develop an efficient solution method, which is discussed in Part 2 of this paper. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie300324h [article] Capacity planning under clinical trials uncertainty in continuous pharmaceutical manufacturing, 1 : Mathematical framework [texte imprimé] / Arul Sundaramoorthy, Auteur ; James M. B. Evans, Auteur ; Paul I. Barton, Auteur . - 2012 . - pp. 13692–13702. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 42 (Octobre 2012) . - pp. 13692–13702 Mots-clés : Pharmaceutical manufacturing Résumé : Unlike traditional batch-based pharmaceutical manufacturing, where the active pharmaceutical ingredient (API) and the final drug product are often produced in different facilities at different locations, novel continuous pharmaceutical manufacturing strategies enable the production of both the API and the final drug product in the same integrated facility. The capacities of such integrated continuous facilities must be determined for potential products in the face of clinical trials uncertainty. Given a portfolio consisting of potential products in the development stage, the goal of capacity planning is to ensure the availability of enough production capacity to meet the projected demands of products, which vary from the launch to the peak-demand periods. To address this problem, we propose a multiscenario, multiperiod, mixed-integer linear programming (MILP) formulation that takes into account uncertainty in the outcome of clinical trials. We illustrate the proposed framework using several examples. The exponential increase in problem size with the number of products motivates us to develop an efficient solution method, which is discussed in Part 2 of this paper. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie300324h

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Capacity planning under clinical trials uncertainty in continuous pharmaceutical manufacturing, 2 / Arul Sundaramoorthy in Industrial & engineering chemistry research, Vol. 51 N° 42 (Octobre 2012)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 51 N° 42 (Octobre 2012) . - pp. 13703–13711 Titre : Capacity planning under clinical trials uncertainty in continuous pharmaceutical manufacturing, 2 : Solution method Type de document : texte imprimé Auteurs : Arul Sundaramoorthy, Auteur ; Xiang Li, Auteur ; James M. B. Evans, Auteur Année de publication : 2012 Article en page(s) : pp. 13703–13711 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Pharmaceutical manufacturing Résumé : In Part 1 of this paper, we presented a scenario-based multiperiod mixed-integer linear programming (MILP) formulation for a capacity planning problem in continuous pharmaceutical manufacturing under clinical trials uncertainty. The number of scenarios and, thus, the formulation size grows exponentially with the number of products. The model size easily becomes intractable for conventional algorithms for more than 8 products. However, industrial-scale problems often involve 10 or more products, and thus a scalable solution algorithm is essential to solve such large-scale problems in reasonable times. In this part of the paper, we develop a rigorous decomposition strategy that exploits the underlying problem structure. We demonstrate the effectiveness of the proposed algorithm using several examples containing up to 16 potential products and over 65 000 scenarios. With the proposed decomposition algorithm, the solution time scales linearly with the number of scenarios, whereby a 16-product example with over 65 million binary variables, nearly 240 million continuous variables, and over 250 million constraints was solved in less than 6 h of solver time. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie3003254 [article] Capacity planning under clinical trials uncertainty in continuous pharmaceutical manufacturing, 2 : Solution method [texte imprimé] / Arul Sundaramoorthy, Auteur ; Xiang Li, Auteur ; James M. B. Evans, Auteur . - 2012 . - pp. 13703–13711. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 42 (Octobre 2012) . - pp. 13703–13711 Mots-clés : Pharmaceutical manufacturing Résumé : In Part 1 of this paper, we presented a scenario-based multiperiod mixed-integer linear programming (MILP) formulation for a capacity planning problem in continuous pharmaceutical manufacturing under clinical trials uncertainty. The number of scenarios and, thus, the formulation size grows exponentially with the number of products. The model size easily becomes intractable for conventional algorithms for more than 8 products. However, industrial-scale problems often involve 10 or more products, and thus a scalable solution algorithm is essential to solve such large-scale problems in reasonable times. In this part of the paper, we develop a rigorous decomposition strategy that exploits the underlying problem structure. We demonstrate the effectiveness of the proposed algorithm using several examples containing up to 16 potential products and over 65 000 scenarios. With the proposed decomposition algorithm, the solution time scales linearly with the number of scenarios, whereby a 16-product example with over 65 million binary variables, nearly 240 million continuous variables, and over 250 million constraints was solved in less than 6 h of solver time. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie3003254

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Computational study of network-based mixed-integer programming approaches for chemical production scheduling / Arul Sundaramoorthy in Industrial & engineering chemistry research, Vol. 50 N° 9 (Mai 2011)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 50 N° 9 (Mai 2011) . - pp. 5023-5040 Titre : Computational study of network-based mixed-integer programming approaches for chemical production scheduling Type de document : texte imprimé Auteurs : Arul Sundaramoorthy, Auteur ; Christos T. Maravelias, Auteur Année de publication : 2011 Article en page(s) : pp. 5023-5040 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Mathematical programming Scheduling Production Mixed integer Résumé : The goal of this paper is to discuss the modeling aspects and study the computational performance of scheduling approaches for batch process networks that are based on discrete-time and continuous-time representations. First, we compare the above two modeling approaches in terms of formulation size and modeling capabilities; we brietly review their main characteristics and outline their advantages and disadvantages. Second, we perform an extensive computational comparison between the two methods using a collection of more than 100 problem instances and 800 optimization runs covering five different process networks, various objective functions, different scheduling horizons, and a wide range of features (fixed and variable processing times, utilities, holding and backlog costs, intermediate shipments, and setups). We show that the computational requirements of discrete-time models increase moderately with the incorporation of these additional features, something that cannot be said for continuous-time models. We close with a number of conclusions that we believe will lead to fruitful discussions in the area and foster further development of modeling and solution methods for chemical production scheduling problems. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24128631 [article] Computational study of network-based mixed-integer programming approaches for chemical production scheduling [texte imprimé] / Arul Sundaramoorthy, Auteur ; Christos T. Maravelias, Auteur . - 2011 . - pp. 5023-5040. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 9 (Mai 2011) . - pp. 5023-5040 Mots-clés : Mathematical programming Scheduling Production Mixed integer Résumé : The goal of this paper is to discuss the modeling aspects and study the computational performance of scheduling approaches for batch process networks that are based on discrete-time and continuous-time representations. First, we compare the above two modeling approaches in terms of formulation size and modeling capabilities; we brietly review their main characteristics and outline their advantages and disadvantages. Second, we perform an extensive computational comparison between the two methods using a collection of more than 100 problem instances and 800 optimization runs covering five different process networks, various objective functions, different scheduling horizons, and a wide range of features (fixed and variable processing times, utilities, holding and backlog costs, intermediate shipments, and setups). We show that the computational requirements of discrete-time models increase moderately with the incorporation of these additional features, something that cannot be said for continuous-time models. We close with a number of conclusions that we believe will lead to fruitful discussions in the area and foster further development of modeling and solution methods for chemical production scheduling problems. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24128631

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Modeling of storage in batching and scheduling of multistage processes / Arul Sundaramoorthy in Industrial & engineering chemistry research, Vol. 47 N°17 (Septembre 2008)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 47 N°17 (Septembre 2008) . - p. 6648–6660 Titre : Modeling of storage in batching and scheduling of multistage processes Type de document : texte imprimé Auteurs : Arul Sundaramoorthy, Auteur ; Christos T. Maravelias, Auteur Année de publication : 2008 Article en page(s) : p. 6648–6660 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Multistage processes Storage vessels Batching Scheduling Résumé : The goal of this paper is the development of a mixed-integer programming formulation for the simultaneous batching and scheduling in multiproduct multistage processes with storage constraints. Storage vessels are modeled as additional processing units for which assignment and sequencing constraints are expressed. Our formulation involves selection (batching), assignment and precedence (scheduling) binary variables as well as batch-size and timing continuous variables. We also discuss a general classification of storage policies in multistage processes and show how the proposed formulation can be readily modified to address all classes of problems. Finally, we extend our approach to address problems with sequence-dependent changeovers and stand-alone scheduling problems, and we present a set of tightening constraints that enhance the solution of the proposed models. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie701737a [article] Modeling of storage in batching and scheduling of multistage processes [texte imprimé] / Arul Sundaramoorthy, Auteur ; Christos T. Maravelias, Auteur . - 2008 . - p. 6648–6660. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 N°17 (Septembre 2008) . - p. 6648–6660 Mots-clés : Multistage processes Storage vessels Batching Scheduling Résumé : The goal of this paper is the development of a mixed-integer programming formulation for the simultaneous batching and scheduling in multiproduct multistage processes with storage constraints. Storage vessels are modeled as additional processing units for which assignment and sequencing constraints are expressed. Our formulation involves selection (batching), assignment and precedence (scheduling) binary variables as well as batch-size and timing continuous variables. We also discuss a general classification of storage policies in multistage processes and show how the proposed formulation can be readily modified to address all classes of problems. Finally, we extend our approach to address problems with sequence-dependent changeovers and stand-alone scheduling problems, and we present a set of tightening constraints that enhance the solution of the proposed models. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie701737a

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Scheduling of multistage batch processes under utility constraints / Arul Sundaramoorthy in Industrial & engineering chemistry research, Vol. 48 N° 13 (Juillet 2009)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 48 N° 13 (Juillet 2009) . - pp. 6050–6058 Titre : Scheduling of multistage batch processes under utility constraints Type de document : texte imprimé Auteurs : Arul Sundaramoorthy, Auteur ; Christos T. Maravelias, Auteur ; Pradeep Prasad, Auteur Année de publication : 2009 Article en page(s) : pp. 6050–6058 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Mixed-integer programming formulation Multiproduct multistage processes Scheduling Simultaneous batching Storage vessels Résumé : We present a novel discrete-time mixed-integer programming (MIP) formulation for the simultaneous batching and scheduling in multiproduct multistage processes under utility constraints. In addition to processing units and storage vessels, we consider utilities such as cooling water, steam, and electricity that are available in limited quantities. Since different tasks often share the limited utilities at the same time, we use a common time-grid approach. Further, the proposed method handles the batching decisions (the number and sizes of batches) seamlessly without the usage of explicit batch-selection variables. To preserve batch identity in storage vessels, we introduce a new class of inventory variables and constraints. Our approach is the first to address limited utilities for simultaneous batching and scheduling in multistage processes. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801386m [article] Scheduling of multistage batch processes under utility constraints [texte imprimé] / Arul Sundaramoorthy, Auteur ; Christos T. Maravelias, Auteur ; Pradeep Prasad, Auteur . - 2009 . - pp. 6050–6058. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 13 (Juillet 2009) . - pp. 6050–6058 Mots-clés : Mixed-integer programming formulation Multiproduct multistage processes Scheduling Simultaneous batching Storage vessels Résumé : We present a novel discrete-time mixed-integer programming (MIP) formulation for the simultaneous batching and scheduling in multiproduct multistage processes under utility constraints. In addition to processing units and storage vessels, we consider utilities such as cooling water, steam, and electricity that are available in limited quantities. Since different tasks often share the limited utilities at the same time, we use a common time-grid approach. Further, the proposed method handles the batching decisions (the number and sizes of batches) seamlessly without the usage of explicit batch-selection variables. To preserve batch identity in storage vessels, we introduce a new class of inventory variables and constraints. Our approach is the first to address limited utilities for simultaneous batching and scheduling in multistage processes. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801386m

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