Science and Technology Production
Book Chapter
Authorship
C.A. Méndez
;
J. Cerdá
Date
2001
Publishing House and Editing Place
ELSEVIER
Book
Computer-Aided Chemical Engineering, 9
(pp. 693-698)
ELSEVIER
ELSEVIER
ISBN
0-444-50709-4
Summary
Information provided by the agent in
SIGEVA
This paper presents a unified MILP mathematical formulation for the scheduling of resourceconstrained multipurpose facilities involving continuous processes and intermediate storage tanks. Different sequences of processing steps can be carried out in the plant to produce a significant number of final products and required intermediates. In order to reduce equipment idle time due to unbalanced stages' capacity, storage tanks are available for temporary inventory of intermediates. The proble...
This paper presents a unified MILP mathematical formulation for the scheduling of resourceconstrained multipurpose facilities involving continuous processes and intermediate storage tanks. Different sequences of processing steps can be carried out in the plant to produce a significant number of final products and required intermediates. In order to reduce equipment idle time due to unbalanced stages' capacity, storage tanks are available for temporary inventory of intermediates. The problem goal is to maximize the plant economic output while satisfying specified minimum product requirements. The proposed approach relies on a continuous time domain representation that accounts for unit-dependent processing rates, sequence-dependent changeover times and storage limitations. Rather than relying on the notion of time-slots or event points, the model basic block is the set of (direct/non-direct) predecessors for any campaign at each processing/storage unit. The approach was applied to a manufacturing facility involving a sequence of three manufacturing stages and producing seven intermediates and fifteen final products. Compared to previous continuous-time scheduling methodologies, the approach shows a drastic reduction in variables/constraints, a much higher computational efficiency and it additionally provides a better production schedule.
Show more
Show less
Key Words
SchedulingMILP model