Discrete-Time Network
Scheduling and Dynamic Optimization
of Batch Processes with Variable Processing Times through Discrete-Steepest
Descent Optimization
posted on 2024-02-27, 13:05authored byDavid
A. Liñán, Luis A. Ricardez-Sandoval
This work proposes a general discrete-time simultaneous
scheduling
and dynamic optimization (SSDO) formulation based on the state-task
network (STN) representation. This formulation explicitly considers
variable processing times, which is a key aspect in the integration
of scheduling and control decisions. The resulting Mixed-Integer Nonlinear
Programming (MINLP) problem is solved using a custom Discrete-Steepest
Descent Algorithm (D-SDA), which is designed to efficiently explore
the ordered discrete decisions in the formulation, i.e., processing
times and batching variables. The performance of the proposed solution
framework is illustrated using two case studies adapted from the literature.
The results show that the D-SDA explores the feasible region of ordered
discrete decisions more efficiently than a general-purpose MINLP solver,
leading to more profitable solutions in shorter computational times.