In this work, the medium-term production scheduling of a multipurpose, multiproduct industrial batch plant is modeled using a novel continuous-time mathematical formulation first developed in -. The methodology consists of the decomposition of the whole scheduling period into successive short horizons of a few days. A decomposition model is implemented to determine each short horizon and the corresponding products to be included. Then, a novel continuous-time formulation for short-term scheduling of batch processes with multiple intermediate due dates is applied to each short horizon selected, leading to a large-scale mixed-integer linear programming (MILP) problem. The scheduling model includes over 80 pieces of equipment, both batch and continuous, and can take into account the processing recipes of hundreds of different products. Several characteristics of the production plant are incorporated into the scheduling model and actual plant data are used to model all parameters. Two different modes of operation are presented along with representative production schedules to demonstrate the effectiveness of medium-term scheduling techniques for industrial production facilities.
In addition, reactive scheduling is incorporated in the formulation in order to update the current production schedule to provide an immediate response to an unexpected event such as equipment breakdown or the addition of new orders. Reactive scheduling formulations should be fast and efficient and take into account the schedule currently in progress as well as planned productions that are not affected by the unexpected event. In this work, a reactive scheduling framework is developed which utilizes the efficient MILP mathematical framework developed for short-term and medium-term scheduling problems with modifications introduced to reflect the effects of the unforeseen event. To avoid full rescheduling of the current production horizon, the formulation determines tasks which are not affected by the unforeseen event, either directly or indirectly, and can be carried out as scheduled. The resulting tasks along with additional subsets of tasks are then fixed in the MILP problem and the rest of the horizon is rescheduled. We consider two types of unexpected events: unit shutdown and the addition or modification of orders. The formulation is then able to determine an updated production schedule for the remaining time horizon in a reasonable amount of CPU time. Reactive scheduling of a large-scale industrial batch plant is performed to demonstrate the effectiveness of the proposed approach.
- M.G. Ierapetritou and C.A. Floudas. "Effective Continuous-Time Formulation for Short-Term Scheduling: 1. Multipurpose Batch Processes." Ind. Eng. Chem. Res. 37 (1998): 4341-4359. - M.G. Ierapetritou and C.A. Floudas. "Effective Continuous-Time Formulation for Short-Term Scheduling: 2. Continuous and Semi-continuous Processes." Ind. Eng. Chem. Res. 37 (1998): 4360-4374. - M.G. Ierapetritou, T.S. Hene, and C.A. Floudas. "Effective Continuous-Time Formulation for Short-Term Scheduling: 3. Multiple Intermediate Due Dates." Ind. Eng. Chem. Res. 38 (1999): 3446-3461. - X. Lin and C.A. Floudas. "Design, Synthesis and Scheduling of Multipurpose Batch Plants via an Effective Continuous-Time Formulation." Comput. Chem. Engng. 25 (2001): 665-674. - X. Lin, C.A. Floudas, S. Modi, and N.M. Juhasz. "Continuous-Time Optimization Approach for Medium-Range Production Scheduling of a Multiproduct Batch Plant." Ind. Eng. Chem. Res. 41 (2002): 3884-3906. - S.L. Janak, C.A. Floudas, J. Kallrath, and N. Vormbrock. "Production Scheduling of a Large-Scale Industrial Batch Plant: I. Short-Term and Medium-Term Scheduling." Ind. Eng. Chem. Res. accepted for publication (2006). - S.L. Janak, C.A. Floudas, J. Kallrath, and N. Vormbrock. "Production Scheduling of a Large-Scale Industrial Batch Plant: II. Reactive Scheduling." Ind. Eng. Chem. Res. accepted for publication (2006).