Computational Study of Network-Based Mixed-Integer Programming Approaches for Chemical Production Scheduling

The goal of this paper is to discuss the modeling aspects and study the computational performance of scheduling approaches for batch process networks that are based on discrete-time and continuous-time representations. First, we compare the above two modeling approaches in terms of formulation size and modeling capabilities; we briefly review their main characteristics and outline their advantages and disadvantages. Second, we perform an extensive computational comparison between the two methods using a collection of more than 100 problem instances and 800 optimization runs covering five different process networks, various objective functions, different scheduling horizons, and a wide range of features (fixed and variable processing times, utilities, holding and backlog costs, intermediate shipments, and setups). We show that the computational requirements of discrete-time models increase moderately with the incorporation of these additional features, something that cannot be said for continuous-time mode...

[1]  R. Sargent,et al.  A general algorithm for short-term scheduling of batch operations */I , 1993 .

[2]  R. Sargent,et al.  A general algorithm for short-term scheduling of batch operations—II. Computational issues , 1993 .

[3]  R. Sargent,et al.  The optimal operation of mixed production facilities—a general formulation and some approaches for the solution , 1996 .

[4]  C. Pantelides,et al.  A simple continuous-time process scheduling formulation and a novel solution algorithm , 1996 .

[5]  Matthew H. Bassett,et al.  Decomposition techniques for the solution of large-scale scheduling problems , 1996 .

[6]  C. Pantelides,et al.  Optimal Campaign Planning/Scheduling of Multipurpose Batch/Semicontinuous Plants. 2. A Mathematical Decomposition Approach , 1996 .

[7]  Gintaras V. Reklaitis,et al.  Using Detailed Scheduling To Obtain Realistic Operating Policies for a Batch Processing Facility , 1997 .

[8]  C. Floudas,et al.  Effective Continuous-Time Formulation for Short-Term Scheduling. 1. Multipurpose Batch Processes , 1998 .

[9]  G. Reklaitis,et al.  Continuous Time Representation Approach to Batch and Continuous Process Scheduling. 1. MINLP Formulation , 1999 .

[10]  A. Barbosa‐Póvoa,et al.  An Improved RTN Continuous-Time Formulation for the Short-term Scheduling of Multipurpose Batch Plants , 2001 .

[11]  N. Giannelos,et al.  A Simple New Continuous-Time Formulation for Short-Term Scheduling of Multipurpose Batch Processes , 2002 .

[12]  Josef Kallrath,et al.  Planning and scheduling in the process industry , 2002, OR Spectr..

[13]  Ignacio E. Grossmann,et al.  New general continuous-time state-task network formulation for short-term scheduling of multipurpose batch plants , 2003 .

[14]  J. D. Kelly,et al.  Crude oil blend scheduling optimization: an application with multimillion dollar benefits. Part 2 , 2003 .

[15]  Ignacio E. Grossmann,et al.  A hybrid MILP/CP decomposition approach for the continuous time scheduling of multipurpose batch plants , 2004, Comput. Chem. Eng..

[16]  Christodoulos A. Floudas,et al.  Continuous-time versus discrete-time approaches for scheduling of chemical processes: a review , 2004, Comput. Chem. Eng..

[17]  Christodoulos A. Floudas,et al.  Enhanced Continuous-Time Unit-Specific Event-Based Formulation for Short-Term Scheduling of Multipurpose Batch Processes: Resource Constraints and Mixed Storage Policies. , 2004 .

[18]  Christos T. Maravelias,et al.  Mixed-Time Representation for State-Task Network Models , 2005 .

[19]  Iftekhar A. Karimi,et al.  A simpler better slot-based continuous-time formulation for short-term scheduling in multipurpose batch plants , 2005 .

[20]  Jaime Cerdá,et al.  State-of-the-art review of optimization methods for short-term scheduling of batch processes , 2006, Comput. Chem. Eng..

[21]  Ignacio E. Grossmann,et al.  On the relation of continuous‐ and discrete‐time state–task network formulations , 2006 .

[22]  P. Castro,et al.  Two New Continuous-Time Models for the Scheduling of Multistage Batch Plants with Sequence Dependent Changeovers , 2006 .

[23]  Jeffrey D. Kelly,et al.  An Improved MILP Modeling of Sequence-Dependent Switchovers for Discrete-Time Scheduling Problems , 2007 .

[24]  Danielle Zyngier,et al.  Hierarchical decomposition heuristic for scheduling: Coordinated reasoning for decentralized and distributed decision-making problems , 2008, Comput. Chem. Eng..

[25]  Christos T. Maravelias,et al.  Polyhedral results for discrete-time production planning MIP formulations for continuous processes , 2009, Comput. Chem. Eng..

[26]  C. Maravelias,et al.  Scheduling of Multistage Batch Processes under Utility Constraints , 2009 .

[27]  Christos T. Maravelias,et al.  Integration of production planning and scheduling: Overview, challenges and opportunities , 2009, Comput. Chem. Eng..

[28]  John M. Wassick,et al.  Enterprise-wide optimization in an integrated chemical complex , 2009, Comput. Chem. Eng..

[29]  Jie Li,et al.  A novel approach to scheduling multipurpose batch plants using unit-slots , 2009 .