(90b) Integrated Operational Planning And Medium-Term Scheduling Of A Large-Scale Industrial Batch Plant | AIChE

(90b) Integrated Operational Planning And Medium-Term Scheduling Of A Large-Scale Industrial Batch Plant

Authors 

Floudas, C. A. - Presenter, Princeton University
Verderame, P. M. - Presenter, Princeton University
Shaik, M. A. - Presenter, Princeton University


The operational planning and the medium-term scheduling of a chemical plant are interrelated activities dealing with the allocation of plant resources. Due to their disparate time scales, the effective integration of planning and scheduling has proven to be a formidable task. Shah [1] and Kallrath [2] both presented excellent reviews highlighting the challenges surrounding the integration of planning and scheduling. Operational planning typically occurs over a time horizon of between one to three months and determines the daily production targets for the plant in question. Medium-term scheduling occurs over a shorter time horizon of between two to four weeks and deals in greater detail with the allocation of plant resources such as process units. Given the production targets supplied by the operational planning model, the medium-term scheduling model attempts to determine when certain units should be utilized and the sequencing of these units. Floudas and Lin [3,4] have presented extensive reviews outlining the various objectives and modeling aspects surrounding the scheduling of chemical plants. The lack of an integrative framework for planning and scheduling will invariably cause the planning model to provide unrealistic production targets leading to the misallocation of plant resources. Industrial case studies have indicated that the effective integration of planning and scheduling will increase profits by concurrently minimizing inventory and maximizing customer satisfaction levels [5]. In response to these financial incentives and the lack of effective integration approaches, a novel framework for the integration of planning and scheduling for a multipurpose and multiproduct batch plant has been generated.

A novel discrete-time mixed-integer linear programming (MILP) planning model which still captures the continuous-time nature of a multipurpose and multiproduct batch plant has been developed. The planning model in question is a unit aggregation model having the explicit objective of providing a daily production profile (e.g., how much of each product should be produced at a facility on a daily basis) for a batch chemical plant. The daily production profile serves as a tight upper bound on the production capacity of the plant and provides the medium-term scheduling model with necessary input data. The framework entails integrating the novel planning model with the medium-term scheduling model developed by Janak et al. [6] through a forward rolling horizon approach. The forward rolling horizon framework facilitates the two-way interaction between the planning and scheduling models allowing the planning model to reflect more accurately the production capacity of the plant. The integrated planning and scheduling framework has been applied to an industrial case study of a multipurpose and multiproduct batch plant producing sixty-three different products over a time horizon of three months. The plant's production capacity is represented by its thirteen batch reactors. Computational results will be presented which demonstrate that the proposed integrated approach yielded greater aggregate production totals as well as a higher degree of daily demand satisfaction than when compared to the approach of isolated planning and scheduling, which does not allow for the two-way interaction between planning and scheduling.

[1] Shah, N. Process Industry Supply Chains: Advances and Challenges. Computers Chem. Eng. 2005, 29, 1225. [2] Kallrath, J. Planning and Scheduling in the Process Industry. OR Spectrum, 2002, 24, 219. [3] Floudas, C.A.; Lin, X. Continuous-time versus Discrete-time Approaches for Scheduling of Chemical Processes: A review. Computers Chem. Eng. 2004, 28, 2109. [4] Floudas, C.A.; Lin, X. Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications. Annals of Operations Research. 2005, 139, 131. [5] Shobrys, D.E.; White, D.E. Planning, Scheduling and Control Systems: Why cannot they work together. Computers Chem. Eng. 2002, 26, 149. [6] Janak, S.L.; Floudas C.A.; Vormbrock, N. Production Scheduling of a Large-Scale Industrial Batch Plant. I. Short-Term and Medium-Term Scheduling. Ind. Eng. Chem. Res. 2006, 25, 8234.