Resumo (PT):
Production planning and scheduling in the process industry in general and in the pulp and paper (P&P) sector in particular can be very challenging. Most practitioners, however, address those
activities relying only on spreadsheets, which is time-consuming and sub-optimal. The literature has reported some decision support systems (DSSs) that are far from the state-of-the-art with respect to optimization models and methods, and several research works that do not address industrial issues.
We contribute to reduce that gap by developing and describing a DSS that resulted from several iterations with a P&P company and a thorough review of the process systems engineering literature. The DSS incorporates relevant industrial features (which motivated the development of a specific
model), exhibits important technical details (such as the connection to existing systems and user-friendly interfaces) and shows how optimization can be integrated in real world applications, enhanced by key pre- and post-optimization procedures.
Abstract (EN):
Production planning and scheduling in the process industry in general and in the pulp and paper (P&P) sector in particular can be very challenging. Most practitioners, however, address those activities relying only on spreadsheets, which is time-consuming and sub-optimal. The literature has reported some decision support systems (DSSs) that are far from the state-of-the-art with regard to optimization models and methods, and several research works that do not address industrial issues. We contribute to reduce that gap by developing and describing a DSS that resulted from several iterations with a P&P company and from a thorough review of the literature on process systems engineering. The DSS incorporates relevant industrial features (which motivated the development of a specific model), exhibits important technical details (such as the connection to existing systems and user-friendly interfaces) and shows how optimization can be integrated in real world applications, enhanced by key pre- and post-optimization procedures.
Language:
English
Type (Professor's evaluation):
Scientific
No. of pages:
20