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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2015

Autores

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

Revista

Título: Computers and Chemical Engineering

Vol. 77

Páginas: 85-104

ISSN: 0098-1354

Editora: Elsevier

Indexação

ISI Web of Knowledge - 23 Citações

Scopus - 35 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias

CORDIS: Ciências Tecnológicas

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 20