Comparação entre diferentes formas de obtenção da solução inicial para a aplicação do método Branch-and-Bound para solucionar problemas de sequenciamento em ambientes Flow-Shop com bloqueio

The objective of this paper is to compare the use of different methods to obtain an initial solution for the Branch-and-Bound algorithm with the objective of minimizing the makespan in a flow shop with zero buffer environment. As the problem is known to be NP-Hard, the Branch-and-Bound algorithm may...

ver descrição completa

Autor principal: Sanches, Felipe Borreiro
Formato: Trabalho de Conclusão de Curso (Graduação)
Idioma: Português
Publicado em: Universidade Tecnológica Federal do Paraná 2022
Assuntos:
Acesso em linha: http://repositorio.utfpr.edu.br/jspui/handle/1/28212
Tags: Adicionar Tag
Sem tags, seja o primeiro a adicionar uma tag!
Resumo: The objective of this paper is to compare the use of different methods to obtain an initial solution for the Branch-and-Bound algorithm with the objective of minimizing the makespan in a flow shop with zero buffer environment. As the problem is known to be NP-Hard, the Branch-and-Bound algorithm may take great computational time to find the best solution. The use of an initial solution may reduce the computational time, by providing an initial upper bound. The efficiency of the use of an initial solution for the Branch-and-Bound algorithm is measured by comparing the traditional algorithm (without an initial solution) and four other algorithms which use different initial solutions provided by four different I phase constructive heuristics (according to Framinan et al., 2004 classification) each. The heuristic methods used to provide the initial solutions are: MM (Ronconi,2004); PF (McCormick et al., 1989); PW and wPF (Pan and Wang, 2012). The Branch-and-Bound algorithm used as well as the lower bound were proposed by Ronconi (2005). The five methods were tested using a 180 problems database. Results show that the use of an initial solution does reduce considerable computational time, mostly in large problems.