Variable Bound Tightening and Valid Constraints for Multiperiod Blending

References

• Achterberg T (2007) Constraint integer programming. Accessed January 16, 2022, https://depositonce.tu-berlin.de/handle/11303/1931.Google Scholar
• Achterberg T, Bixby RE, Gu Z, Rothberg E, Weninger D (2020) Presolve reductions in mixed integer programming. INFORMS J. Comput. 32(2):473–506.Link, Google Scholar
• Alfaki M, Haugland D (2013) Strong formulations for the pooling problem. J. Global Optim. 56:897–916.Crossref, Google Scholar
• Bagajewicz M (2000) A review of recent design procedures for water networks in refineries and process plants. Comput. Chemical Engrg. 24(9–10):2093–2113.Crossref, Google Scholar
• Belotti P (2013) Bound reduction using pairs of linear inequalities. J. Global Optim. 56(3):787–819.Crossref, Google Scholar
• Belotti P, Lee J, Liberti L, Margot F, Wächter A (2009) Branching and bounds tightening techniques for non-convex MINLP. Optim. Methods Software 24(4–5):597–634.Crossref, Google Scholar
• Blom ML, Pearce AR, Stuckey PJ (2016) A decomposition-based algorithm for the scheduling of open-pit networks over multiple time periods. Management Sci. 62(10):3059–3084.Link, Google Scholar
• Blom ML, Burt CN, Pearce AR, Stuckey PJ (2014) A decomposition-based heuristic for collaborative scheduling in a network of open-pit mines. INFORMS J. Comput. 26(4):658–676.Link, Google Scholar
• Boland N, Kalinowski T, Rigterink F (2016a) New multi-commodity flow formulations for the pooling problem. J. Global Optim. 66(4):669–710.Crossref, Google Scholar
• Boland N, Kalinowski T, Rigterink F, Savelsbergh M (2016b) A special case of the generalized pooling problem arising in the mining industry. Accessed January 16, 2022, http://www.optimization-online.org/DB_FILE/2015/07/5025.pdf.Google Scholar
• Castillo PA, Mahalec V (2014) Inventory pinch based, multiscale models for integrated planning and scheduling. Part II: Gasoline blend scheduling. AIChE J. 60(6):2158–2178.Crossref, Google Scholar
• Castro PM (2016) Source-based discrete and continuous-time formulations for the crude oil pooling problem. Comput. Chemical Engrg. 93(4):382–401.Crossref, Google Scholar
• Castro PM, Grossmann IE (2014) Global optimal scheduling of crude oil blending operations with RTN continuous-time and multiparametric disaggregation. Industrial Engrg. Chemical Res. 53(39):15127–15145.Crossref, Google Scholar
• Chen Y, Maravelias CT (2020) Preprocessing algorithm and tightening constraints for multiperiod blend scheduling: Cost minimization. J. Global Optim. 77(3):603–625.Crossref, Google Scholar
• Chen Y, Maravelias CT (2021a) Multiperiod blending problem instances version v2020.0210. Accessed January 16, 2022, https://github.com/INFORMSJoC/2020.0210.Google Scholar
• Chen Y, Maravelias CT (2021b) Tightening methods based on nontrivial bounds on bilinear terms. Optim. Engrg. July:1–38.Google Scholar
• D’Ambrosio C, Linderoth J, Luedtke J (2011) Valid inequalities for the pooling problem with binary variables. Günlük O, Woeginger GJ, eds. Integer Programming and Combinatoral Optimization (Springer, Berlin), 117–129.Crossref, Google Scholar
• DeWitt CW, Lasdon LS, Waren AD, Brenner DA, Melhem SA (1989) OMEGA: An improved gasoline blending system for Texaco. Interfaces 19(1):85–101.Google Scholar
• Domes F, Neumaier A (2016) Constraint aggregation for rigorous global optimization. Math. Programming 155(1–2):375–401.Crossref, Google Scholar
• Furini F, Traversi E, Belotti P, Frangioni A, Gleixner A, Gould N, Liberti L, et al. (2019) QPLIB: A library of quadratic programming instances. Math. Programming Comput. 11(2):237–265.Crossref, Google Scholar
• Gleixner AM, Berthold T, Müller B, Weltge S (2017) Three enhancements for optimization-based bound tightening. J. Global Optim. 67(4):731–757.Crossref, Google Scholar
• Gounaris CE, Misener R, Floudas CA (2009) Computational comparison of piecewise: Linear relaxations for pooling problems. Industrial Engrg. Chemical Res. 48(12):5742–5766.Crossref, Google Scholar
• Gupte A, Ahmed S, Dey SS, Cheon MS (2017) Relaxations and discretizations for the pooling problem. J. Global Optim. 67(3):631–669.Crossref, Google Scholar
• Haverly CA (1978) Studies of the behavior of recursion for the pooling problem. ACM SIGMAP Bull. 25:19–28.Crossref, Google Scholar
• Jeżowski J (2010) Review of water network design methods with literature annotations. Industrial Engrg. Chemical Res. 49(10):4475–4516.Crossref, Google Scholar
• Kelly JD, Mann JL (2003) Crude oil blend scheduling optimization: An application with multimillion dollar benefits - Part 2. Accessed January 15, 2022, https://www.hydrocarbonprocessing.com/magazine/2003/july-2003/instrumentationplant-optimiza tion/crude-oil-blend-scheduling-optimization-an-application-with-multimillion-dollar-benefits-part-2.Google Scholar
• Kolodziej SP, Castro PM, Grossmann IE (2013a) Global optimization of bilinear programs with a multiparametric disaggregation technique. J. Global Optim. 57(4):1039–1063.Crossref, Google Scholar
• Kolodziej SP, Grossmann IE, Furman KC, Sawaya NW (2013b) A discretization-based approach for the optimization of the multiperiod blend scheduling problem. Comput. Chemical Engrg. 53:122–142.Crossref, Google Scholar
• Li J, Karimi IA (2011) Scheduling gasoline blending operations from recipe determination to shipping using unit slots. Industrial Engrg. Chemical Res. 50(15):9156–9174.Google Scholar
• Li J, Misener R, Floudas CA (2012) Continuous-time modeling and global optimization approach for scheduling of crude oil operations. AIChE J. 58(1):205–226.Crossref, Google Scholar
• Li J, Xiao X, Floudas CA (2016) Integrated gasoline blending and order delivery operations: Part I. Short-term scheduling and global optimization for single and multi-period operations. AIChE J. 62(6):2043–2070.Crossref, Google Scholar
• Li J, Li W, Karimi IA, Srinivasan R (2007) Improving the robustness and efficiency of crude scheduling algorithms. AIChE J. 53(10):2659–2680.Crossref, Google Scholar
• Lotero I, Trespalacios F, Grossmann IE, Papageorgiou DJ, Cheon MS (2016) An MILP-MINLP decomposition method for the global optimization of a source based model of the multiperiod blending problem. Comput. Chemical Engrg. 87(6):13–35.Crossref, Google Scholar
• Maranas CD, Floudas CA (1997) Global optimization in generalized geometric programming. Comput. Chemical Engrg. 21(4):351–369.Crossref, Google Scholar
• Misener R, Floudas CA (2012) Global optimization of mixed-integer quadratically-constrained quadratic programs (MIQCQP) through piecewise-linear and edge-concave relaxations. Math. Programming 136(1):155–182.Crossref, Google Scholar
• Neiro SMS, Murata VV, Pinto JM (2014) Hybrid time formulation for diesel blending and distribution scheduling. Industrial Engrg. Chemical Res. 53(44):17124–17134.Crossref, Google Scholar
• Puranik Y, Sahinidis NV (2017) Domain reduction techniques for global NLP and MINLP optimization. Constraints 22(3):338–376.Crossref, Google Scholar
• Quesada I, Grossmann IE (1995) Global optimization of bilinear process networks with multicomponent flows. Comput. Chemical Engrg. 19(12):1219–1242.Crossref, Google Scholar
• Reddy P, Prakash C, Karimi IA, Srinivasan R (2004) Novel solution approach for optimizing crude oil operations. AIChE J. 50(6):1177–1197.Crossref, Google Scholar
• Ryoo HS, Sahinidis NV (1996) A branch-and-reduce approach to global optimization. J. Global Optim. 8(2):107–138.Crossref, Google Scholar
• Savelsbergh MWP (1994) Preprocessing and probing techniques for mixed integer programming problems. ORSA J. Comput. 6(4):445–454.Link, Google Scholar
• Shectman JP, Sahinidis NV (1998) A finite algorithm for global minimization of separable concave programs. J. Global Optim. 12(1):1–36.Crossref, Google Scholar
• Sherali HD, Adams WP (1999) A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems. Nonconvex Optimization and Its Applications, vol. 31 (Springer, Boston). https://doi.org/10.1007/978-1-4757-4388-3.Crossref, Google Scholar
• Smith EMB, Pantelides CC (1999) A symbolic reformulation/spatial branch-and-bound algorithm for the global optimisation of nonconvex MINLPs. Comput. Chemical Engrg. 23(4–5):457–478.Crossref, Google Scholar
• Street L (1989) Constraint propagation, relational arithmetic in AI systems and mathematical programs. Ann. Oper. Res. 21:143–148.Crossref, Google Scholar
• Tawarmalani M, Sahinidis NV (2002) Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming: Theory, Algorithms, Software, and Applications (Kluwer Academic Publishers).Crossref, Google Scholar
• Wicaksono DS, Karimi IA (2008) Piecewise MILP under- and overestimators for global optimization of bilinear programs. AIChE J. 54(4):991–1008.Crossref, Google Scholar