A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery

›› 2008, Vol. 16 ›› Issue (2): 241-246.

• SYSTEM ENGINEERING • Previous Articles Next Articles

A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery

LI Chufu¹, HE Xiaorong¹, CHEN Bingzhen¹, XU Qiang², LIU Chaowei²

1. Department of Chemical Engineering, Tsinghua University, Beijing 100084, China;
2. Department of Chemical Engineering, Lamar University, Beaumont 77710, USA

Received:2007-05-08 Revised:2007-09-20 Online:2008-04-28 Published:2008-04-28
Supported by:
the Specialized Research Fund for Doctoral Program of Higher Education of China(20060003087)

A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery

李初福¹, 何小荣¹, 陈丙珍¹, 徐强², 刘朝玮²

1. Department of Chemical Engineering, Tsinghua University, Beijing 100084, China;
2. Department of Chemical Engineering, Lamar University, Beaumont 77710, USA

通讯作者: HE Xiaorong,E-mail:hexr@tsinghua.edu.cn
基金资助:
the Specialized Research Fund for Doctoral Program of Higher Education of China(20060003087)

Abstract

Abstract: Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization.In this article,first,a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty,and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently,piecewise linear approximation functions are derived and applied to solve the hybrid programming model under uniform distribution assumption.Case studies show that the linear approximation algorithm is effective to solve the hybrid programming model,along with an error≤0.5% when the deviation/mean≤20%.The simulation results indicate that the hybrid programming model with an appropriate weight factor(0.1-0.2)can effectively improve the optimal operational strategies under demand uncertainty,achieving higher profit than the lin-ear programming model and the stochastic programming one with about 1.5% and 0.4% enhancement,respectively.

Key words: production planning, demand uncertainty, stochastic programming, linear programming, hybrid pro-gramming

摘要： Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization.In this article,first,a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty,and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently,piecewise linear approximation functions are derived and applied to solve the hybrid programming model under uniform distribution assumption.Case studies show that the linear approximation algorithm is effective to solve the hybrid programming model,along with an error≤0.5% when the deviation/mean≤20%.The simulation results indicate that the hybrid programming model with an appropriate weight factor(0.1-0.2)can effectively improve the optimal operational strategies under demand uncertainty,achieving higher profit than the lin-ear programming model and the stochastic programming one with about 1.5% and 0.4% enhancement,respectively.

关键词: production planning, demand uncertainty, stochastic programming, linear programming, hybrid pro-gramming

LI Chufu, HE Xiaorong, CHEN Bingzhen, XU Qiang, LIU Chaowei. A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery[J]. , 2008, 16(2): 241-246.

李初福, 何小荣, 陈丙珍, 徐强, 刘朝玮. A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery[J]. , 2008, 16(2): 241-246.

References

1 Moro, L.F.L., Zanin, A.C., Pinto, J.M., “A planning model for refinery diesel production”, Comp. Chem. Eng., 22, 1039-1042 (1998).
2 Pinto, J.M., Joly, M., Moro, L.F.L., “Planning and scheduling models for refinery operations”, Comp. Chem. Eng., 24 (9/10), 2259-2276 (2000).
3 Li, C.F., He, X.R., Zhang, Q.Y., Gong, Z.Z., “A graphic I/O petrochemical industry modeling system and its application”, Petroleum Processing and Petrochemicals, 36 (10), 45-48 (2005).
4 Li, C.F., He, X.R., Chen, B.Z., Gong, Z.Z., Chen, B., Zhang, Q.Y., “Infeasibility diagnosis on the linear programming model of production planning in refinery”, Chin. J. Chem. Eng., 14 (5), 569-573 (2006).
5 Zhang, B.J., Hua, B., “Effective MILP model for oil refinery-wide production planning and better energy utilization”, Journal of Clear Production, 15 (5), 439-448 (2007).
6 Gupta, A., Maranas, C.D., “Managing demand uncertainty in supply chain planning”, Comp. Chem. Eng., 27 (8/9), 1219-1227 (2003).
7 Pongsakdi, A., Rangsunvigit, P., Siemanond, K., Bagajewicz, M.J., “Financial risk management in the planning of refinery operations”, International Journal of Production Economics, 103 (1), 64-86 (2006).
8 Ierapetritou, M.G., Pistikopoulos, E.N., “Batch plant design and operations under uncertainty”, Ind. Eng. Chem. Res., 35 (3), 772-787 (1996).
9 Wang, R.C., Liang, T.F., “Application of fuzzy multi-objective linear programming to aggregate production planning”, Comp. Ind. Eng., 46 (1), 17-41 (2004).
10 Li, P., Wendt, M., Wozny, G., “Optimal production planning for chemical processes under uncertain market conditions”, Chem. Eng.Tech., 27 (6), 641-651 (2004).
11 Mula, J., Poler, R., Garcia-Sabater, J.P., Lario, F.C., “Models for production planning under uncertainty: A review”, Prodcution Economics, 103, 271-285 (2006).
12 Sahinidis, N.V., “Optimization under uncertainty: State-of-the-art and opportunities”, Comp. Chem. Eng., 28, 971-983 (2004).
13 Clay, R.L., Grossmann, I.E., “A disaggregation algorithm for the optimization of stochastic planning models”, Comp. Chem. Eng., 21, 751 (1997).
14 Li, S.J., Lin, H.B., Yang, Y.R., “Application of fuzzy programming for optimization of refinery production planning”, Acta Petrolei Sinica (Petroleum Processing Section), 18 (1), 79-83 (2002).
15 Liao, G.Q., Li, S.J., Yang, Y.R., Lü, D.W., “Production planning optimization of a refinery about price uncertainty”, Acta Petrolei Sinica (Petroleum Processing Section), 18 (4), 47-52 (2002).
16 Neiro, S.M.S., Pinto, J.M., “Multiperiod optimization for production planning of petroleum refineries”, Chem. Eng. Commun., 192 (1), 62-88 (2005).
17 Neiro, S.M.S., Pinto, J.M., “Lagrangean decomposition applied to multiperiod planning of petroleum refineries under uncertainty”, Latin American Applied Research, 36 (4), 213-220 (2006).
18 Li, W.K., Hui, C.W., Li, P., Li, A.X., “Refinery planning under uncertainty”, Ind. Eng. Chem. Res., 43, 6742-6755 (2004).
19 Gupta, A., Maranas, C.D., “A Two-stage modeling and solution framework for multisite midterm planning under demand uncertainty”, Ind. Eng. Chem. Res., 39, 3799-3813 (2000).
20 Liu, M.L., Sahinidis, N.V., “Optimization in process planning under uncertainty”, Ind. Eng. Chem. Res., 35, 4154 (1996).
21 Duran, M.A., Grossmann, I.E., “An outer approximation algorithm for a class of mixed-integers for nonlinear programs”, Math. Prog., 36, 307 (1986).

[1]	Kexin Bi, Mingyu Yan, Shuyuan Zhang, Tong Qiu. Three-scale integrated optimization model of furnace simulation, cyclic scheduling, and supply chain of ethylene plants [J]. Chinese Journal of Chemical Engineering, 2022, 44(4): 29-40.
[2]	Fangyou Yan, Wei Li, Jinli Zhang. Simultaneous synthesis of heat-integrated water networks by a nonlinear program: Considering the wastewater regeneration reuse [J]. Chinese Journal of Chemical Engineering, 2022, 44(4): 402-411.
[3]	Tao Yang, Yiqing Luo, Yingjie Ma, Xigang Yuan. Optimal synthesis of compression refrigeration system using a novel MINLP approach [J]. Chin.J.Chem.Eng., 2018, 26(8): 1662-1669.
[4]	Danlei Chen, Xue Ma, Yiqing Luo, Yingjie Ma, Xigang Yuan. Synthesis of refrigeration system based on generalized disjunctive programming model [J]. Chin.J.Chem.Eng., 2018, 26(8): 1613-1620.
[5]	Ying Chen, Zhihong Yuan, Bingzhen Chen. Process optimization with consideration of uncertainties-An overview [J]. Chin.J.Chem.Eng., 2018, 26(8): 1700-1706.
[6]	Kang Cen, Ting Yao, Qingsheng Wang, Shengyong Xiong. A risk-based methodology for the optimal placement of hazardous gas detectors [J]. Chin.J.Chem.Eng., 2018, 26(5): 1078-1086.
[7]	Jin Lang, Jiao Zhao. Modeling and optimization for oil well production scheduling [J]. , 2016, 24(10): 1423-1430.
[8]	Zhen Gao. A planning model for multiple blending schemes [J]. Chin.J.Chem.Eng., 2015, 23(4): 675-680.
[9]	Shan Lu, Hongye Su, Charlotta Johnsson, YueWang, Lei Xie . Modeling and optimization methods of integrated production planning for steel plate mill with flexible customization [J]. Chin.J.Chem.Eng., 2015, 23(12): 2037-2047.
[10]	CHEN Guohui, YAN Liexiang, SHI Bin . Modeling and Optimization for Short-term Scheduling of Multipurpose Batch Plants [J]. Chin.J.Chem.Eng., 2014, 22(6): 682-689.
[11]	WEN Bo, LI Hongguang. An Approach to Formulation of FNLP with Complex Piecewise Linear Membership Functions [J]. Chin.J.Chem.Eng., 2014, 22(4): 411-417.
[12]	WU Xianli, HU Yangdong, WU Lianying, LI Hong. Model and Design of Cogeneration System for Different Demands of Desalination Water, Heat and Power Production [J]. Chin.J.Chem.Eng., 2014, 22(3): 330-338.
[13]	JIAO Yunqiang, SU Hongye, LIAO Zuwei, HOU Weifeng. Modeling and Multi-objective Optimization of Refinery Hydrogen Network [J]. , 2011, 19(6): 990-998.
[14]	WANG Ruqiang, LI Chufu, HE Xiaorong, CHEN Bingzhen. A Novel Close-loop Strategy for Integrating Process Operations of Fluidized Catalytic Cracking Unit with Production Planning Optimization [J]. , 2008, 16(6): 909-915.
[15]	Wolfgang Marquardt, Sven Kossack, Korbinian Kraemer. A Framework for the Systematic Design of Hybrid Separation Processes [J]. , 2008, 16(3): 333-342.

A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery

A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments