Hierarchical On-line Scheduling of Multiproduct Batch Plants with a Combined Approach of Mathematical Programming and Genetic Algorithm

• SYSTEM ENGINEERING • Previous Articles Next Articles

Hierarchical On-line Scheduling of Multiproduct Batch Plants with a Combined Approach of Mathematical Programming and Genetic Algorithm

CHEN Li^a,b; WANG Kefeng^a; XU Xiaoyu^c; YAO Pingjing^a

^a Institute of Process Systems Engineering, School of Chemical Engineering, Dalian
University of Technology,Dalian 116012, China;
^b Department of Chemistry & Chemical Engineering, Dalian University, Dalian 116622,China　
^c Department of Chemistry, University of UTAH, Salt Lake City, UT, 84112-2020, USA

Received:1900-01-01 Revised:1900-01-01 Online:2004-02-28 Published:2004-02-28
Contact: CHEN Li

多产品间歇过程的分层次在线调度——一种数学规划与遗传算法的混合算法

陈理^a,b; 王克峰^a; 徐霄羽^c; 姚平经^a

^a Institute of Process Systems Engineering, School of Chemical Engineering, Dalian
University of Technology,Dalian 116012, China;
^b Department of Chemistry & Chemical Engineering, Dalian University, Dalian 116622,China　
^c Department of Chemistry, University of UTAH, Salt Lake City, UT, 84112-2020, USA

通讯作者: 陈理

Abstract

Abstract: In this contribution we present an online scheduling algorithm for a real world
multiproduct batch plant.The overall mixed integer nonlinear programming (MINLP) problem is
hierarchically structured into a mixed integer linear programming (MILP) problem first and
then a reduced dimensional MINLP problem, which are optimized by mathematical programming
(MP) and genetic algorithm (GA) respectively. The basis idea relies on combining MP with GA
to exploit their complementary capacity. The key features of the hierarchical model are
explained and illustrated with some real world cases from the multiproduct batch plants.

Key words: online scheduling, multiproduct batch plant, mixed integer nonlinear programming, mathematicalprogramming, genetic algorithm

摘要： In this contribution we present an online scheduling algorithm for a real world
multiproduct batch plant.The overall mixed integer nonlinear programming (MINLP) problem is
hierarchically structured into a mixed integer linear programming (MILP) problem first and
then a reduced dimensional MINLP problem, which are optimized by mathematical programming
(MP) and genetic algorithm (GA) respectively. The basis idea relies on combining MP with GA
to exploit their complementary capacity. The key features of the hierarchical model are
explained and illustrated with some real world cases from the multiproduct batch plants.

关键词: online scheduling;multiproduct batch plant;mixed integer nonlinear programming; mathematicalprogramming;genetic algorithm

CHEN Li, WANG Kefeng, XU Xiaoyu, YAO Pingjing. Hierarchical On-line Scheduling of Multiproduct Batch Plants with a Combined Approach of Mathematical Programming and Genetic Algorithm[J]. .

陈理, 王克峰, 徐霄羽, 姚平经. 多产品间歇过程的分层次在线调度——一种数学规划与遗传算法的混合算法[J]. CIESC Journal.

References

[1] Honkomp, S.J., Lombardo, S., Rosen, O., Pekey, J.F., "The curse of reality-why process
scheduling optimization problems are difficult in practice", Computers Chem. Eng., 24
(suppl.), 323-328 (2000).
[2] Reklaitis, G.V., "Overview of scheduling and planning of batch process operations", In:
Proceedings of Batch Processing Systems Engineering, Reklaitis, G.V., Sunol, A.K.,Rippin,
D.W.T., Horascu, O., Eds., Springer, Berlin (1996).
[3] Floudas, C.A., Nonlinear and Mixed-Integer Optimization:Fundamentals and Applications,
Oxford University Press,New York (1995).
[4] Sand, G., Engell, S., Markert, A., Schultz, R., Schulz, C.,"Approximation of an ideal
online scheduler for a multiproduct batch plant", Computers Chem. Eng., 24 (suppl.),361-367
(2000).
[5] Goldberg, D.E., Genetic Algorithms in Search, Optimization and Machine Learning,
Addison Wesley, Now York (1989).
[6] Davis, L., Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York (1991).
[7] Lohl, T., Schulz, C., Engell, S., "Sequencing of batch operations for a highly coupled
production process: Genetic algorithms versus mathematical programming", Computers Chem.
Eng., 22 (suppl.), 579-585 (1998).
[8] Wang, K.F., Lohl, T., Stobbe, M., Engell, S., "A genetic algorithm for online-
scheduling of a multiproduct polymer batch plant", Computers Chem. Eng., 24 (suppl.), 393-
400 (2000).
[9] Corne, D., Ross, P., "Practical issues and recent advanced in job and open shop
scheduling", In: Proceedings of the AISB Evolutionary Computing Workshop, Dasgupta,
D.,Michalewiz, Z., Eds., Springer, Heidelberg (1997).
[10] Wall, M., GALIB: A C++ Library of Genetic Algorithm Components, Version 2.4,
http://lancet.mit.edu/ga (1996).
[11] Applequist, G., Samikoglu, O., Pekny, J., Reklaitis, G., "Issues in the sue, design
and evolution of process scheduling and planning systems", ISA Transactions, 2, 81-121
(1997).
[12] Harjunkoski, I., Jain, V., Grossmann, I.E., "Hybrid mixed integer/constraint logic
programming strategies for solving scheduling and combinatorial optimization
problems",Computers Chem. Eng., 24 (suppl.), 337-343 (2000).
[13] Schulz, C., Engell, S., Rudolf, R., "Scheduling of a multi-product polymer batch
plant", In: Proceeding for Foundations of Computer-aided Process Operations (FOCAPO’98),
Pekny, J.F., Blau, G.E., Carnahan, B., Eds.,CACHE Publications, Snowbird (1998).

[1]	Shumin ZHENG, Pengcheng GUO, Jianguo YAN, Shuai WANG, Wenbo LI, Qi ZHOU. Experimental and predictive study on pressure drop of subcooled flow boiling in a mini-channel [J]. CIESC Journal, 2023, 74(4): 1549-1560.
[2]	Qucheng LIN, Zuwei LIAO. Multi-objective optimization of work and heat exchange networks based on a decomposition algorithm [J]. CIESC Journal, 2022, 73(11): 5047-5055.
[3]	Yilin ZHU, Xinjing ZHANG, Yujie XU, Jie DING, Huan GUO, Haisheng CHEN. Analysis and optimization of biomass pyrolysis and gasification based on genetic algorithm-comprehensive calculation method [J]. CIESC Journal, 2021, 72(9): 4910-4920.
[4]	SHI Xiangcheng, ZHAO Zhijian, GONG Jinlong. Application of genetic algorithm in the global structure optimization of catalytic system [J]. CIESC Journal, 2021, 72(1): 27-41.
[5]	Wei LI, Xiaoming WANG, Dongnian JIANG, Yajie LI, Chenglong LIANG. Prediction of octane number of finished gasoline blend based on SHPSO-GA-BP [J]. CIESC Journal, 2020, 71(7): 3191-3200.
[6]	Zhiqiang GENG, Shuai BI, Zun WANG, Qunxiong ZHU, Yongming HAN. Operation optimization of ethylene cracking furnace based on improved NSGA-Ⅱ algorithm [J]. CIESC Journal, 2020, 71(3): 1088-1094.
[7]	Zeyu SONG, Guoqing LI, Lingxuan LIU. New determination method of parameters for model-free adaptive control [J]. CIESC Journal, 2019, 70(9): 3430-3440.
[8]	Simin WANG, Lijuan SUN, Chen SONG, Zaoxiao ZHANG, Jian WEN. Multi-objective optimization on shell-side performance of rod-baffle heat exchangers with twisted oval tubes [J]. CIESC Journal, 2019, 70(9): 3353-3362.
[9]	Xin YANG, Zherui MA, Henan SHEN, Hongwei CHEN. Fault diagnosis of airflow jamming fault in double circulating fluidized bed based on multi-scale feature energy and KELM [J]. CIESC Journal, 2019, 70(7): 2616-2625.
[10]	Fei LI, Cuili YANG, Wenjing LI, Junfei QIAO. Optimal control of wastewater treatment process using NSGAII algorithm based on multi-objective uniform distribution [J]. CIESC Journal, 2019, 70(5): 1868-1878.
[11]	Qichao XU, Jinbo JIANG, Xudong PENG, Jiyun LI, Yuming WANG. Unified model and geometrical optimization of bi-directional groove of dry gas seal based on genetic algorithm [J]. CIESC Journal, 2019, 70(3): 995-1005.
[12]	Zheng FAN, Panpan JI, Chao LI, Zhuang LIU, Yigang ZHAO, Xiaoyan JING. Synthetic process optimization of benzyl acrylate using fuzzy neural networks-genetic algorithms [J]. CIESC Journal, 2019, 70(11): 4315-4324.
[13]	LI Fan, XU Feng, LUO Xionglin. Control configuration design with genetic algorithm for decentralized control system [J]. CIESC Journal, 2018, 69(S2): 266-273.
[14]	ZHANG Lei, ZHANG Xiaogang, CHEN Hua. Soft sensors for multi-stage batch processes based on Gath-Geva algorithm and kernel extreme learning machine [J]. CIESC Journal, 2018, 69(6): 2576-2585.
[15]	WANG Jie, CAO Daofan, LAN Xingying, GAO Jinsen. Select Filter-Wrapper characteristic variables for yield prediction of fluid catalytic cracking unit [J]. CIESC Journal, 2018, 69(1): 464-471.