Challenges in the application of mathematical programming in the enterprise-wide optimization of process industries

被引:31
作者
Grossmann, Ignacio E. [1 ]
机构
[1] Carnegie Mellon Univ, Dept Chem Engn, Ctr Adv Proc Decis Making, Pittsburgh, PA 15213 USA
基金
美国安德鲁·梅隆基金会;
关键词
mathematical programming; enterprise-wide optimization; process industries; modeling; planning; scheduling; real-time optimization; control; mixed-integer linear and nonlinear optimization methods; decomposition methods; stochastic programming; CONTINUOUS-TIME FORMULATION; OF-THE-ART; MIXED-INTEGER; SUPPLY CHAINS; GENERAL ALGORITHM; CONTINUOUS PLANTS; BATCH-OPERATIONS; BOUND ALGORITHM; LINEAR-PROGRAMS; OPTIMAL-DESIGN;
D O I
10.1134/S0040579514050182
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Enterprise-wide optimization (EWO) has become a major goal in the process industries due to the increasing pressures for remaining competitive in the global marketplace. EWO involves optimizing the supply, manufacturing and distribution activities of a company to reduce costs, inventories and environmental impact, and to maximize profits and responsiveness. Major operational items include planning, scheduling, real-time optimization and control. We provide an overview of EWO in terms of a mathematical programming framework. We first provide a brief overview of mathematical programming techniques (mixed-integer linear and nonlinear optimization methods), as well as decomposition methods, stochastic programming and modeling systems. We then address some of the major challenges involved in the modeling and solution of these problems. Finally, we describe several applications to show the potential of this area.
引用
收藏
页码:555 / 573
页数:19
相关论文
共 149 条
[31]   Progress in computational mixed integer programming - A look back from the other side of the tipping point [J].
Bixby, Robert ;
Rothberg, Edward .
ANNALS OF OPERATIONS RESEARCH, 2007, 149 (01) :37-41
[32]   Supply chain optimization in continuous flexible process networks [J].
Bok, JK ;
Grossmann, IE ;
Park, S .
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2000, 39 (05) :1279-1290
[33]   An algorithmic framework for convex mixed integer nonlinear programs [J].
Bonami, Pierre ;
Biegler, Lorenz T. ;
Conna, Andrew R. ;
Cornuejols, Gerard ;
Grossmann, Ignacio E. ;
Laird, Carl D. ;
Lee, Jon ;
Lodi, Andrea ;
Margot, Francois ;
Sawaya, Nicolas ;
Wachter, Andreas .
DISCRETE OPTIMIZATION, 2008, 5 (02) :186-204
[34]  
Brooke Anthony., 1998, A User's Guide
[35]   An interior point algorithm for large-scale nonlinear programming [J].
Byrd, RH ;
Hribar, ME ;
Nocedal, J .
SIAM JOURNAL ON OPTIMIZATION, 1999, 9 (04) :877-900
[36]   Generalized Disjunctive Programming as a Systematic Modeling Framework to Derive Scheduling Formulations [J].
Castro, Pedro M. ;
Grossmann, Ignacio E. .
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2012, 51 (16) :5781-5792
[37]   Greedy Algorithm for Scheduling Batch Plants with Sequence-Dependent Changeovers [J].
Castro, Pedro M. ;
Harjunkoski, Iiro ;
Grossmann, Ignacio E. .
AICHE JOURNAL, 2011, 57 (02) :373-387
[38]   New Continuous-Time Scheduling Formulation for Continuous Plants under Variable Electricity Cost [J].
Castro, Pedro M. ;
Harjunkoski, Iiro ;
Grossmann, Ignacio E. .
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2009, 48 (14) :6701-6714
[39]  
Cheney EW., 1959, NUMER MATH, V1, P253, DOI [10.1007/bf01386389, DOI 10.1007/BF01386389]
[40]   A stochastic programming approach for clinical trial planning in new drug development [J].
Colvin, Matthew ;
Maravelias, Christos T. .
COMPUTERS & CHEMICAL ENGINEERING, 2008, 32 (11) :2626-2642