Lagrangian decomposition approach for solving continuous-time scheduling models of refinery production problems

doi:10.16511/j.cnki.qhdxxb.2016.24.016

Download: PDF(1398 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract Continuous-time models need more computational effort to solve refinery production scheduling problems as the scheduling problem size increases. A new Lagrangian decomposition approach was used which divides the whole scheduling problem into nine subproblems. The convergence of Lagrange multipliers is accelerated by adding auxiliary constraints to the subproblems. This paper gives an initialization scheme for the Lagrange multipliers, a hybrid method to update the Lagrange multipliers and a heuristic algorithm to find feasible solutions. Computational results for three cases with different time horizons and different numbers of orders show that the Lagrangian scheme improves the computational efficiency and obtains optimal or near-optimal solutions.

Keywords refinery scheduling continuous-time representation Lagrangian decomposition

ZTFLH:

TP273

Issue Date: 15 April 2016

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	SHI Lei
	JIANG Yongheng
	WANG Ling
	HUANG Dexian

Cite this article:

SHI Lei,JIANG Yongheng,WANG Ling, et al. Lagrangian decomposition approach for solving continuous-time scheduling models of refinery production problems[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(4): 437-447.

URL:

http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2016.24.016 OR http://jst.tsinghuajournals.com/EN/Y2016/V56/I4/437

[1] Pinto J M, Joly M, Moro L F L. Planning and scheduling models for refinery operations[J]. Computers & Chemical Engineering, 2000, 24(9-10), 2259-2276.
[2] Göthe-Lundgren M, Lundgren J T, Persson JA. An optimization model for refinery production scheduling[J]. International Journal of Production Economics, 2002, 78(3), 255-270.
[3] JIA Zhenya, Ierapetritou M. Efficient short-term scheduling of refinery operations based on a continuous time formulation[J]. Computers & Chemical Engineering, 2004, 28(6-7), 1001-1019.
[4] LUO Chunpeng, RONG Gang. Hierarchical approach for short-term scheduling in refineries[J]. Industrial & Engineering Chemistry Research, 2007, 46(11), 3656-3668.
[5] Mouret S, Grossmann I E, Pestiaux P. A new Lagrangian decomposition approach applied to the integration of refinery planning and crude-oil scheduling[J]. Computers & Chemical Engineering, 2011, 35(12), 2750-2766.
[6] CAO Cuiwen, GU Xingsheng, XIN Zhong. A data-driven rolling-horizon online scheduling model for diesel production of a real-world refinery[J]. AIChE Journal, 2013, 59(4), 1160-1174.
[7] Shah N K, LI Zukui, Ierapetritou M G. Petroleum refining operations:Key issues, advances, and opportunities[J]. Industrial & Engineering Chemistry Research, 2011, 50(3), 1161-1170.
[8] Joly M. Refinery production planning and scheduling:The refining core business[J]. Brazilian Journal of Chemical Engineering, 2012, 29(2), 371-384.
[9] SHI Lei, JIANG Yongheng, WANG Ling, et al. Refinery production scheduling involving operational transitions of mode switching under predictive control system[J]. Industrial & Engineering Chemistry Research, 2014, 53(19), 8155-8170.
[10] Terrazas-Moreno S, Trotter P A, Grossmann I E. Temporal and spatial Lagrange an decompositions in multi-site, multi-period production planning problems with sequence-dependent changeovers[J]. Computers & Chemical Engineering, 2011, 35, 2913-2928.
[11] Neiro S M, Pinto J M. Langrange an decomposition applied to multiperiod planning of petroleum refineries under uncertainty[J]. Latin American Applied Research, 2006, 36(4), 213-220.
[12] Shah N, Saharidis G, JIA Zhenya, et al. Centralized-decentralized optimization for refinery scheduling[J]. Computers & Chemical Engineering, 2009, 33(12):2091-2105.
[13] TANG Lixin, Luh P B, LIU Jiyin, et al. Steel-making process scheduling using Lagrangian relaxation[J]. International Journal of Production Research, 2002, 40(1), 55-70.
[14] LI Zukui, Ierapetritou M. Production planning and scheduling integration through augmented Lagrangian optimization[J]. Computers & Chemical Engineering, 2010, 34, 996-1006.
[15] JIANG Yongheng, Rodriguez M A, Harjunkoski I, et al. Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part Ⅱ:A Lagrangean decomposition algorithm[J]. Computers & Chemical Engineering, 2014, 62, 211-224.
[16] Knudsen B R, Grossmann I E, Foss B, et al. Lagrangian relaxation based decomposition for well scheduling in shale-gas systems[J]. Computers & Chemical Engineering, 2014, 63, 234-249.
[17] Held M, Karp R M. The traveling-salesman problem and minimum spanning trees:Part Ⅱ[J]. Mathematical Programming, 1971, 1(1):6-25.
[18] Held M, Wolfe P, Crowder H P. Validation of subgradient optimization[J]. Mathematical Programming, 1974, 6(1), 62-88.
[19] Cheney E W, Goldstein A A. Newton's method for convex programming and Tchebycheff approximation[J]. Numerische Mathematik, 1959, 1(1), 253-268.
[20] Kelley J J E. The cutting-plane method for solving convex programs[J]. Journal of the Society for Industrial & Applied Mathematics, 1960, 8(4), 703-712.
[21] Marsten R E, Hogan W W, Blankenship J W. The boxstep method for large-scale optimization[J]. Operations Research, 1975, 23(3), 389-405.
[22] Baker B M, Sheasby, J. Accelerating the convergence of subgradient optimization[J]. European Journal of Operational Research, 1999, 117, 136-144.

[1]	WANG Zhenlei, LIU Xueyan, WANG Xin. Economic performance design based on adaptive iterative learning control of MPC systems[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(9): 1016-1024.
[2]	WANG Zhenlei, MAO Fuxing, WANG Xin. Temperature control of an acrylonitrile polymerization kettle using multiple models with second level adaptation[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(7): 707-716.
[3]	WANG Zhenlei, CHEN Dengqian, WANG Xin. Optimal control of distillation columns based on a multiple model switching strategy[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(4): 430-436.
[4]	HUANG Tao, YANG Kaiming, ZHU Yu, JIANG Yi, HU Chuxiong. Online recursive estimation of 3-DOF wafer stage decoupling time-varying parameters[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(2): 185-191.