Tightening methods for continuous-time mixed-integer programming models for chemical production scheduling
Significance Important advances in modeling chemical production scheduling problems have been made in recent years, yet effective solution methods are still required. We use an algorithm that uses process network and customer demand information to formulate powerful valid inequalities that substanti...
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| Published in | AIChE journal Vol. 59; no. 12; pp. 4461 - 4467 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Blackwell Publishing Ltd
01.12.2013
American Institute of Chemical Engineers |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0001-1541 1547-5905 |
| DOI | 10.1002/aic.14249 |
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| Abstract | Significance
Important advances in modeling chemical production scheduling problems have been made in recent years, yet effective solution methods are still required. We use an algorithm that uses process network and customer demand information to formulate powerful valid inequalities that substantially improve the solution process. In particular, we extend the ideas recently developed for discrete‐time formulations to continuous‐time models and show that these tightening methods lead to a significant decrease in computational time, up to more than three orders of magnitude for some instances. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4461–4467, 2013 |
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| AbstractList | Important advances in modeling chemical production scheduling problems have been made in recent years, yet effective solution methods are still required. We use an algorithm that uses process network and customer demand information to formulate powerful valid inequalities that substantially improve the solution process. In particular, we extend the ideas recently developed for discrete-time formulations to continuous-time models and show that these tightening methods lead to a significant decrease in computational time, up to more than three orders of magnitude for some instances. [PUBLICATION ABSTRACT] Important advances in modeling chemical production scheduling problems have been made in recent years, yet effective solution methods are still required. We use an algorithm that uses process network and customer demand information to formulate powerful valid inequalities that substantially improve the solution process. In particular, we extend the ideas recently developed for discrete‐time formulations to continuous‐time models and show that these tightening methods lead to a significant decrease in computational time, up to more than three orders of magnitude for some instances. © 2013 American Institute of Chemical Engineers AIChE J , 59: 4461–4467, 2013 Significance Important advances in modeling chemical production scheduling problems have been made in recent years, yet effective solution methods are still required. We use an algorithm that uses process network and customer demand information to formulate powerful valid inequalities that substantially improve the solution process. In particular, we extend the ideas recently developed for discrete‐time formulations to continuous‐time models and show that these tightening methods lead to a significant decrease in computational time, up to more than three orders of magnitude for some instances. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4461–4467, 2013 |
| Author | Velez, Sara Merchan, Andres F. Maravelias, Christos T. |
| Author_xml | – sequence: 1 givenname: Andres F. surname: Merchan fullname: Merchan, Andres F. organization: Dept. of Chemical and Biological Engineering, University of Wisconsin-Madison, Engineering Dr., WI, 141553706, Madison – sequence: 2 givenname: Sara surname: Velez fullname: Velez, Sara organization: Dept. of Chemical and Biological Engineering, University of Wisconsin-Madison, Engineering Dr., WI, 141553706, Madison – sequence: 3 givenname: Christos T. surname: Maravelias fullname: Maravelias, Christos T. email: maravelias@wisc.edu organization: Dept. of Chemical and Biological Engineering, University of Wisconsin-Madison, Engineering Dr., WI, 141553706, Madison |
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Mathematical formulation. Ind Eng Chem Res. 1996;35(2):488-509. Pochet Y, Warichet F. A tighter continuous time formulation for the cyclic scheduling of a mixed plant. Comput Chem Eng. 2008;32(11):2723-2744. BarbosaPovoa APFD, Pantelides CC. Design of multipurpose plants using the resource-task network unified framework. Comput Chem Eng. 1997;21:S703-S708. Gimenez DM, Henning GP, Maravelias CT. A novel network-based continuous-time representation for process scheduling: Part I. Main concepts and mathematical formulation. Comput Chem Eng. 2009;33(9):1511-1528. Maravelias CT, Papalamprou K. Polyhedral results for discrete-time production planning MIP formulations for continuous processes. Comput Chem Eng. 2009;33(11):1890-1904. Castro P, Barbosa-Povoa APFD, Matos H. An improved RTN continuous-time formulation for the short-term scheduling of multipurpose batch plants. Ind Eng Chem Res. 2001;40(9):2059-2068. Maravelias CT, Grossmann IE. New general continuous-time state-task network formulation for short-term scheduling of multipurpose batch plants. Ind Eng Chem Res. 2003;42(13):3056-3074. Maravelias CT. On the combinatorial structure of discrete-time MIP formulations for chemical production scheduling. Comput Chem Eng. 2012;38:204-212. Velez S, Sundaramoorthy A, Maravelias CT. Valid inequalities based on demand propagation for chemical production scheduling MIP models. AIChE J. 2013;59(3):872-887. Bassett MH, Pekny JF, Reklaitis GV. Decomposition techniques for the solution of large-scale scheduling problems. AIChE J. 1996;42(12):3373-3387. Maravelias CT. Mixed-time representation for state-task network models. Ind Eng Chem Res. 2005;44(24):9129-9145. Sundaramoorthy A, Maravelias CT. A general framework for process scheduling. AIChE J. 2011;57(3):695-710. Velez S, Maravelias CT. A branch-and-bound algorithm for the solution of chemical production scheduling MIP models using parallel computing. Comput Chem Eng. 2013;55(0):28-39. Kondili E, Pantelides CC, Sargent RWH. A general algorithm for short-term scheduling of batch-operations.1. Milp Formulation. Comput Chem Eng. 1993;17(2):211-227. Maravelias CT. A decomposition framework for the scheduling of single- and multi-stage processes. Comput Chem Eng. 2006;30(3):407-420. Shah N, Pantelides CC, Sargent RWH. A general algorithm for short-term scheduling of batch-operations.2. Computational issues. Comput Chem Eng. 1993;17(2):229-244. Maravelias CT, Grossmann IE. Minimization of the makespan with a discrete-time state-task network formulation. Ind Eng Chem Res. 2003;42(24):6252-6257. Sahinidis NV, Grossmann IE. Reformulation of multiperiod MILP models for planning and scheduling of chemical processes. Comput Chem Eng. 1991;15(4):255-272. Papageorgiou LG, Pantelides CC. Optimal campaign planning scheduling of multipurpose batch semicontinuous plants.2. A mathematical decomposition approach. Ind Eng Chem Res. 1996;35(2):510-529. Schilling G, Pantelides CC. A simple continuous-time process scheduling formulation and a novel solution algorithm. Comput Chem Eng. 1996;20:S1221-S1226. Velez S, Maravelias CT. Reformulations and branching methods for mixed-integer programming chemical production scheduling models. Ind Eng Chem Res. 2013;52(10):3832-3841. Velez S, Maravelias CT. Mixed-integer programming model and tightening methods for scheduling in general chemical production environments. Ind Eng Chem Res. 2013;52(9):3407-3423. Castro PM, Harjunkoski I, Grossmann IE. Greedy algorithm for scheduling batch plants with sequence-dependent changeovers. AIChE J. 2011;57(2):373-387. Wu D, Ierapetritou MG. Decomposition approaches for the efficient solution of short-term scheduling problems. Comput Chem Eng. 2003;27(8-9):1261-1276. Sundaramoorthy A, Maravelias CT. Computational study of network-based mixed-integer programming approaches for chemical production scheduling. Ind Eng Chem Res. 2011;50(9):5023-5040. Maravelias CT. General framework and modeling approach classification for chemical production scheduling. AIChE J. 2012;58(6):1812-1828. Ferris MC, Maravelias CT, Sundaramoorthy A. Simultaneous batching and scheduling using dynamic decomposition on a grid. INFORMS J Comput Sum 2009;21(3):398-410. Burkard RE, Hatzl J. Review, extensions and computational comparison of MILP formulations for scheduling of batch processes. Comput Chem Eng. 2005;29(8):1752-1769. Gimenez DM, Henning GP, Maravelias CT. A novel network-based continuous-time representation for process scheduling: Part II. General framework. Comput Chem Eng. 2009;33(10):1644-1660. Janak SL, Floudas CA. Improving unit-specific event based continuous-time approaches for batch processes: Integrality gap and task splitting. Comput Chem Eng. 2008;32(4-5):913-955. Mendez CA, Cerda J, Grossmann IE, Harjunkoski I, Fahl M. State-of-the-art review of optimization methods for short-term scheduling of batch processes. Comput Chem Eng. 2006;30(6-7):913-946. 2006; 30 2009; 21 1991; 15 1997; 21 2009 1994 2011; 57 2008; 32 2005; 60 2012; 38 2012; 58 1996; 35 2005; 29 2001; 40 2005; 44 52 1998; 37 2009; 33 2002; 26 2013; 59 1993; 17 2002; 41 2013; 55 1999; 38 2013; 52 2011; 50 2003; 27 2003; 42 1996; 20 1996; 42 e_1_2_9_30_1 e_1_2_9_31_1 e_1_2_9_11_1 e_1_2_9_34_1 e_1_2_9_10_1 e_1_2_9_35_1 e_1_2_9_13_1 e_1_2_9_32_1 e_1_2_9_12_1 Velez S (e_1_2_9_33_1); 52 e_1_2_9_15_1 e_1_2_9_38_1 e_1_2_9_14_1 e_1_2_9_39_1 e_1_2_9_17_1 e_1_2_9_36_1 e_1_2_9_16_1 e_1_2_9_37_1 e_1_2_9_18_1 e_1_2_9_20_1 e_1_2_9_22_1 e_1_2_9_21_1 e_1_2_9_24_1 e_1_2_9_23_1 e_1_2_9_8_1 e_1_2_9_7_1 e_1_2_9_6_1 e_1_2_9_5_1 e_1_2_9_4_1 e_1_2_9_3_1 e_1_2_9_2_1 Kelly JD (e_1_2_9_19_1) 2009 e_1_2_9_9_1 e_1_2_9_26_1 e_1_2_9_25_1 e_1_2_9_28_1 e_1_2_9_27_1 e_1_2_9_29_1 |
| References_xml | – reference: Gimenez DM, Henning GP, Maravelias CT. A novel network-based continuous-time representation for process scheduling: Part II. General framework. Comput Chem Eng. 2009;33(10):1644-1660. – reference: Maravelias CT, Papalamprou K. Polyhedral results for discrete-time production planning MIP formulations for continuous processes. Comput Chem Eng. 2009;33(11):1890-1904. – reference: Maravelias CT. A decomposition framework for the scheduling of single- and multi-stage processes. Comput Chem Eng. 2006;30(3):407-420. – reference: Velez S, Maravelias CT. Mixed-integer programming model and tightening methods for scheduling in general chemical production environments. Ind Eng Chem Res. 2013;52(9):3407-3423. – reference: Velez S, Sundaramoorthy A, Maravelias CT. Valid inequalities based on demand propagation for chemical production scheduling MIP models. AIChE J. 2013;59(3):872-887. – reference: Velez S, Maravelias CT. Reformulations and branching methods for mixed-integer programming chemical production scheduling models. Ind Eng Chem Res. 2013;52(10):3832-3841. – reference: Ferris MC, Maravelias CT, Sundaramoorthy A. Simultaneous batching and scheduling using dynamic decomposition on a grid. INFORMS J Comput Sum 2009;21(3):398-410. – reference: Harjunkoski I, Grossmann IE. Decomposition techniques for multistage scheduling problems using mixed-integer and constraint programming methods. Comput Chem Eng. 2002;26(11):1533-1552. – reference: Janak SL, Floudas CA. Improving unit-specific event based continuous-time approaches for batch processes: Integrality gap and task splitting. Comput Chem Eng. 2008;32(4-5):913-955. – reference: BarbosaPovoa APFD, Pantelides CC. Design of multipurpose plants using the resource-task network unified framework. Comput Chem Eng. 1997;21:S703-S708. – reference: Gimenez DM, Henning GP, Maravelias CT. A novel network-based continuous-time representation for process scheduling: Part I. Main concepts and mathematical formulation. Comput Chem Eng. 2009;33(9):1511-1528. – reference: Sundaramoorthy A, Karimi IA. A simpler better slot-based continuous-time formulation for short-term scheduling in multipurpose batch plants. Chem Eng Sci. 2005;60(10):2679-2702. – reference: Schilling G, Pantelides CC. A simple continuous-time process scheduling formulation and a novel solution algorithm. Comput Chem Eng. 1996;20:S1221-S1226. – reference: Burkard RE, Hatzl J. Review, extensions and computational comparison of MILP formulations for scheduling of batch processes. Comput Chem Eng. 2005;29(8):1752-1769. – reference: Papageorgiou LG, Pantelides CC. Optimal campaign planning scheduling of multipurpose batch semicontinuous plants.1. Mathematical formulation. Ind Eng Chem Res. 1996;35(2):488-509. – reference: Maravelias CT. General framework and modeling approach classification for chemical production scheduling. AIChE J. 2012;58(6):1812-1828. – reference: Maravelias CT. Mixed-time representation for state-task network models. Ind Eng Chem Res. 2005;44(24):9129-9145. – reference: Castro P, Barbosa-Povoa APFD, Matos H. An improved RTN continuous-time formulation for the short-term scheduling of multipurpose batch plants. Ind Eng Chem Res. 2001;40(9):2059-2068. – reference: Sundaramoorthy A, Maravelias CT. Computational study of network-based mixed-integer programming approaches for chemical production scheduling. Ind Eng Chem Res. 2011;50(9):5023-5040. – reference: Wu D, Ierapetritou MG. Decomposition approaches for the efficient solution of short-term scheduling problems. Comput Chem Eng. 2003;27(8-9):1261-1276. – reference: Maravelias CT, Grossmann IE. New general continuous-time state-task network formulation for short-term scheduling of multipurpose batch plants. Ind Eng Chem Res. 2003;42(13):3056-3074. – reference: Papageorgiou LG, Pantelides CC. Optimal campaign planning scheduling of multipurpose batch semicontinuous plants.2. A mathematical decomposition approach. Ind Eng Chem Res. 1996;35(2):510-529. – reference: Shah N, Pantelides CC, Sargent RWH. A general algorithm for short-term scheduling of batch-operations.2. Computational issues. Comput Chem Eng. 1993;17(2):229-244. – reference: Sundaramoorthy A, Maravelias CT. A general framework for process scheduling. AIChE J. 2011;57(3):695-710. – reference: Mendez CA, Cerda J, Grossmann IE, Harjunkoski I, Fahl M. State-of-the-art review of optimization methods for short-term scheduling of batch processes. Comput Chem Eng. 2006;30(6-7):913-946. – reference: Castro PM, Harjunkoski I, Grossmann IE. Greedy algorithm for scheduling batch plants with sequence-dependent changeovers. AIChE J. 2011;57(2):373-387. – reference: Kondili E, Pantelides CC, Sargent RWH. A general algorithm for short-term scheduling of batch-operations.1. Milp Formulation. Comput Chem Eng. 1993;17(2):211-227. – reference: Maravelias CT. On the combinatorial structure of discrete-time MIP formulations for chemical production scheduling. Comput Chem Eng. 2012;38:204-212. – reference: Sahinidis NV, Grossmann IE. Reformulation of multiperiod MILP models for planning and scheduling of chemical processes. Comput Chem Eng. 1991;15(4):255-272. – reference: Pochet Y, Warichet F. A tighter continuous time formulation for the cyclic scheduling of a mixed plant. Comput Chem Eng. 2008;32(11):2723-2744. – reference: Ierapetritou MG, Floudas CA. Effective continuous-time formulation for short-term scheduling. 1. Multipurpose batch processes. Ind Eng Chem Res. 1998;37(11):4341-4359. – reference: Mockus L, Reklaitis GV. Continuous time representation approach to batch and continuous process scheduling. 2. Computational issues. 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Important advances in modeling chemical production scheduling problems have been made in recent years, yet effective solution methods are still... Important advances in modeling chemical production scheduling problems have been made in recent years, yet effective solution methods are still required. We... |
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| SubjectTerms | Algorithms demand propagation Integer programming Mixed-integer programming Production scheduling valid inequalities |
| Title | Tightening methods for continuous-time mixed-integer programming models for chemical production scheduling |
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