A decision support system for the operational production planning and scheduling of an integrated pulp and paper mill

• Type: Article in International Scientific Journal
• Date: 2015

Authors

Gonçalo Figueira (Author) FEUP; Pedro Amorim (Author) FEUP; Luís Guimarães (Author) FEUP; Mário Lopes (Author) FEUP; Fábio Moreira (Author) FEP; Bernardo Almada Lobo (Author) FEUP

Journal

Title: Computers and Chemical Engineering

Vol. 77

Pages: 85-104

ISSN: 0098-1354

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 23 Citations

Scopus - 35 Citations

Scientific classification

FOS: Engineering and technology

CORDIS: Technological sciences

Other information

Authenticus ID: P-00A-BSN

DOI: 10.1016/j.compchemeng.2015.03.017

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