Abstract
We present a novel parameter space exploration algorithm for three classes of multiparametric problems, namely linear (mpLP), quadratic (mpQP), and mixed-integer linear (mpMILP). We construct subsets of the parameter space in the form of simplices through Delaunay triangulation to facilitate identification of the optimal partitions that describe the solution space. The presented exploration strategy prioritizes identifying volumetrically larger critical regions compared to existing methods. We demonstrate the exploration algorithm on an illustrative example, and compare the volumetrically identified parameter space against existing solvers on randomly generated problems in all three classes.
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Notes
The example problem and its exact solution can be downloaded at http://paroc.tamu.edu/Examples/.
The Chebyshev center is defined as the center of the largest “ball” that can fit in a polytope. The interested reader is refered to Boyd and Vandenberghe (2004) for details regarding the Chebyshev center.
Recall \(\overrightarrow{\mathcal {A}}\) only includes the strongly active set.
In most practical applications, \(\varTheta \) is usually described by box constraints, which yield \(2^q\) vertex points.
Problems can be downloaded at http://paroc.tamu.edu/Examples/.
References
Ahmadi-Moshkenani P, Johansen TA, Olaru S (2018) Combinatorial approach toward multiparametric quadratic programming based on characterizing adjacent critical regions. IEEE Trans Autom Control 63(10):3221–3231. https://doi.org/10.1109/TAC.2018.2791479
Akbari A, Barton PI (2018) An improved multi-parametric programming algorithm for flux balance analysis of metabolic networks. J Optim Theory Appl 178(2):502–537. https://doi.org/10.1007/s10957-018-1281-x
Alessio A, Bemporad A (2009) A survey on explicit model predictive control. Lect Notes Control Inf Sci 384:345–369. https://doi.org/10.1007/978-3-642-01094-1_29
Avraamidou S, Pistikopoulos EN (2018) Multi-parametric global optimization approach for tri-level mixed-integer linear optimization problems. J Glob Optim. https://doi.org/10.1007/s10898-018-0668-4
Bemporad A, Borrelli F, Morari M (2002) Model predictive control based on linear programming—the explicit solution. IEEE Trans Autom Control 47(12):1974–1985. https://doi.org/10.1109/TAC.2002.805688
Bemporad A, Morari M, Dua V, Pistikopoulos EN (2002) The explicit linear quadratic regulator for constrained systems. Automatica 38(1):3–20. https://doi.org/10.1016/S0005-1098(01)00174-1
Borrelli F, Bemporad A, Morari M (2003) Geometric algorithm for multiparametric linear programming. J Optim Theory Appl 118(3):515–540. https://doi.org/10.1023/B:JOTA.0000004869.66331.5c
Boyd S, Vandenberghe L (2004) Convex Optim. Cambridge University Press, New York
Burnak B, Katz J, Diangelakis NA, Pistikopoulos EN (2018) Simultaneous process scheduling and control: a multiparametric programming-based approach. Ind Eng Chem Res 57(11):3963–3976. https://doi.org/10.1021/acs.iecr.7b04457
Burnak B, Diangelakis NA, Katz J, Pistikopoulos EN (2019) Integrated process design, scheduling, and control using multiparametric programming. Comput Chem Eng 125:164–184. https://doi.org/10.1016/j.compchemeng.2019.03.004
Charitopoulos VM, Papageorgiou LG, Dua V (2018) Closed-loop integration of planning, scheduling and multi-parametric nonlinear control. Comput Chem Eng. https://doi.org/10.1016/j.compchemeng.2018.06.021
de Berg M, Cheong O, van Kreveld M, Overmars M (2008) Delaunay triangulations. In: Van Kreveld M, Schwarzkopf O, de Berg M, Overmars M (eds) Computational geometry: algorithms and applications. Springer, Berlin, pp 191–218. https://doi.org/10.1007/978-3-540-77974-2_9
Diangelakis NA, Burnak B, Katz J, Pistikopoulos EN (2017) Process design and control optimization: a simultaneous approach by multi-parametric programming. AIChE J 63(11):4827–4846. https://doi.org/10.1002/aic.15825
Drgoňa J, Klauňo M, Janeňek F, Kvasnica M (2017) Optimal control of a laboratory binary distillation column via regionless explicit MPC. Comput Chem Eng 96:139–148. https://doi.org/10.1016/j.compchemeng.2016.10.003
Dua P, Georgiadis MC (2011) Multiparametric mixed-integer linear programming, chap 3. Wiley, Hoboken, pp 53–71. https://doi.org/10.1002/9783527631216.ch3
Dua V, Bozinis NA, Pistikopoulos EN (2002) A multiparametric programming approach for mixed-integer quadratic engineering problems. Comput Chem Eng 26(4):715–733. https://doi.org/10.1016/S0098-1354(01)00797-9
Dua P, Kouramas K, Dua V, Pistikopoulos E (2008) MPC on a chip—recent advances on the application of multi-parametric model-based control. Comput Chem Eng 32(4):754–765. https://doi.org/10.1016/j.compchemeng.2007.03.008
Fiacco AV (1983) Chapter 2—basic sensitivity and stability results. In: Fiacco AV (ed) Introduction to sensitivity and stability analysis in nonlinear programming, vol 165. Mathematics in science and engineering. Elsevier, Amsterdam, pp 8–64. https://doi.org/10.1016/S0076-5392(08)60651-7
Floudas CA (1995) Nonlinear and mixed-integer optimization: fundamentals and applications. Topics in chemical engineering. Oxford University Press, Oxford
Gal T (1995) Postoptimal analyses, parametric programming, and related topics: degeneracy, multicriteria decision making, redundancy. Walter de Gruyter, Berlin
Gal T, Nedoma J (1972) Multiparametric linear programming. Manag Sci 18(7):406–422
Grünbaum B (2003) Convex polytopes. Springer, New York. https://doi.org/10.1007/978-1-4613-0019-9
Gupta A, Bhartiya S, Nataraj PSV (2011) A novel approach to multiparametric quadratic programming. Automatica 47(9):2112–2117. https://doi.org/10.1016/j.automatica.2011.06.019
Herceg M, Kvasnica M, Jones C, Morari M (2013) Multi-parametric toolbox 3.0. In: Proceedings of the European control conference, pp 502–510. Zürich, Switzerland
Kopanos GM, Pistikopoulos EN (2014) Reactive scheduling by a multiparametric programming rolling horizon framework: a case of a network of combined heat and power units. Ind Eng Chem Res 53(11):4366–4386. https://doi.org/10.1021/ie402393s
Köppe M, Queyranne M, Ryan CT (2010) Parametric integer programming algorithm for bilevel mixed integer programs. J Optim Theory Appl 146(1):137–150. https://doi.org/10.1007/s10957-010-9668-3
Lee J, Chang HJ (2018) Explicit model predictive control for linear time-variant systems with application to double-lane-change maneuver. PLoS One. https://doi.org/10.1371/journal.pone.0208071
Mid EC, Dua V (2019) Parameter estimation using multiparametric programming for implicit Euler’s method based discretization. Chem Eng Res Des. https://doi.org/10.1016/j.cherd.2018.11.032
Oberdieck R, Wittmann-Hohlbein M, Pistikopoulos EN (2014) A branch and bound method for the solution of multiparametric mixed integer linear programming problems. J Glob Optim 59(2–3):527–543. https://doi.org/10.1007/s10898-014-0143-9
Oberdieck R, Diangelakis NA, Papathanasiou MM, Nascu I, Pistikopoulos EN (2016) Pop—parametric optimization toolbox. Ind Eng Chem Res 55(33):8979–8991. https://doi.org/10.1021/acs.iecr.6b01913
Oberdieck R, Diangelakis NA, Pistikopoulos EN (2017) Explicit model predictive control: a connected-graph approach. Automatica 76:103–112. https://doi.org/10.1016/j.automatica.2016.10.005
Papathanasiou MM, Quiroga-Campano AL, Steinebach F, Elviro M, Mantalaris A, Pistikopoulos EN (2017) Advanced model-based control strategies for the intensification of upstream and downstream processing in mab production. Biotechnol Prog 33(4):966–988. https://doi.org/10.1002/btpr.2483
Pistikopoulos EN (2009) Perspectives in multiparametric programming and explicit model predictive control. AIChE J 55(8):1918–1925. https://doi.org/10.1002/aic.11965
Shokry A, Dombayci C, Espuña A (2016) Multiparametric metamodels for model predictive control of chemical processes. Comput Aided Chem Eng 38:937–942. https://doi.org/10.1016/B978-0-444-63428-3.50161-2
Spjøtvold J, Kerrigan EC, Jones CN, Tøndel P, Johansen TA (2006) On the facet-to-facet property of solutions to convex parametric quadratic programs. Automatica 42(12):2209–2214. https://doi.org/10.1016/j.automatica.2006.06.026
Tøndel P, Johansen TA, Bemporad A (2003) An algorithm for multi-parametric quadratic programming and explicit mpc solutions. Automatica 39(3):489–497. https://doi.org/10.1016/S0005-1098(02)00250-9
Wittmann-Hohlbein M, Pistikopoulos EN (2013) Proactive scheduling of batch processes by a combined robust optimization and multiparametric programming approach. AIChE J 59(11):4184–4211. https://doi.org/10.1002/aic.14140
Wittmann-Hohlbein M, Pistikopoulos EN (2014) Approximate solution of mp-MILP problems using piecewise affine relaxation of bilinear terms. Comput Chem Eng 61:136–155. https://doi.org/10.1016/j.compchemeng.2013.10.009
Zhuge J, Ierapetritou MG (2014) Integration of scheduling and control for batch processes using multi-parametric model predictive control. AIChE J 60(9):3169–3183. https://doi.org/10.1002/aic.14509
Acknowledgements
We acknowledge the financial support from the Texas A&M Energy Institute and the NSF Projects SusChEM (Grant No. 1705423) and INFEWS (Grant No. 1739977). We also acknowledge our colleague William W. Tso for his fruitful discussions.
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Appendix
Appendix
1.1 Details of the motivating example
The multiparametric quadratic programming problem used as the motivating example is defined as follows.
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Burnak, B., Katz, J. & Pistikopoulos, E.N. A space exploration algorithm for multiparametric programming via Delaunay triangulation. Optim Eng 22, 555–579 (2021). https://doi.org/10.1007/s11081-020-09535-6
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DOI: https://doi.org/10.1007/s11081-020-09535-6