Please use this identifier to cite or link to this item:
https://hdl.handle.net/10216/77460
Author(s): | Marcos Furlan Bernardo Almada Lobo Maristela Santos Reinaldo Morabito |
Title: | Unequal individual genetic algorithm with intelligent diversification for the lot-scheduling problem in integrated mills using multiple-paper machines |
Issue Date: | 2015 |
Abstract: | This paper addresses the lot-sizing and scheduling problem of pulp and paper mills involving multiple paper machines. The underlying multi-stage integrated production process considers the following critical units: continuous digester, intermediate stocks of pulp and liquor, multiple paper machines and a recovery line to treat by-products. This work presents a mixed integer programming (MIP) model to represent the problem, as well as a solution approach based on a customized genetic algorithm (GA) with an embedded residual linear programming model. Some GA tools are explored, including literature and new operators, a novel diversification process and other features. In particular, the diversification process uses a new allele frequency measure to change between diversification and intensification procedures. Computational results show the effectiveness of the method to solve relatively large instances of the single paper machine problem when compared to other single paper machine solution methods found in the literature. For multiple paper machine settings, in most runs the GA solutions are better than those obtained for the MIP model using an optimization software. |
Description: | This paper addresses the lot-sizing and scheduling problem of pulp and paper mills involving multiple paper machines. The underlying multi-stage integrated production process considers the following critical units: continuous digester, intermediate stocks of pulp and liquor, multiple paper machines and a recovery line to treat by-products. This work presents a mixed integer programming (MIP) model to represent the problem, as well as a solution approach based on a customized genetic algorithm (GA) with an embedded residual linear programming model. Some GA tools are explored, including literature and new operators, a novel diversification process and other features. In particular, the diversification process uses a new allele frequency measure to change between diversification and intensification procedures. Computational results show the effectiveness of the method to solve relatively large instances of the single paper machine problem when compared to other single paper machine solution methods found in the literature. For multiple paper machine settings, in most runs the GA solutions are better than those obtained for the MIP model using an optimization software. |
Subject: | Ciências Tecnológicas, Ciências da engenharia e tecnologias Technological sciences, Engineering and technology |
Scientific areas: | Ciências da engenharia e tecnologias Engineering and technology |
URI: | https://hdl.handle.net/10216/77460 |
Related Information: | info:eu-repo/grantAgreement/Autoridade de Gestão do Programa Operacional Regional do Norte/Programas Integrados de IC&DT/NORTE-07-0124-FEDER-000057/Smart Manufacturing and Logistics/BESTCASE-RL2 |
Document Type: | Artigo em Revista Científica Internacional |
Rights: | openAccess |
License: | https://creativecommons.org/licenses/by-nc/4.0/ |
Appears in Collections: | FEUP - Artigo em Revista Científica Internacional |
Files in This Item:
File | Description | Size | Format | |
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95795.1.pdf | 2.71 MB | Adobe PDF | View/Open | |
95795.pdf | 2.71 MB | Adobe PDF | View/Open |
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