Publicación - Modeling realistic hybrid flexible flowshop scheduling problems

Modeling realistic hybrid flexible flowshop scheduling problems

Área de Investigación: Articulos Año: 2008
Tipo de publicación: Artículo
Autores: Ruiz, Rubén; Serifoglu, Funda Sivrikaya; Urlings, Thijs
Revista: Computers & Operations Research Volumen: 35
Número: 4 Páginas: 1151-1175
Nota:
Times Cited: 13 Article English Ruiz, R Univ Politecn Valencia, Dept Appl Stat {&} Operat Res, E-46071 Valencia, Spain Cited References Count: 42 232GNPERGAMON-ELSEVIER SCIENCE LTDTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND OXFORD
Abstract:
This paper aims to contribute to the recent research efforts to bridge the gap between the theory and the practice of scheduling by modelizing a realistic manufacturing environment and analyzing the effect of the inclusion of several characteristics in the problem formulation. There are several constraints and characteristics that affect the scheduling operations at companies. While these constraints are many times tackled in the literature, they are seldom considered together inside the same problem formulation. We propose a formulation along with a mixed integer modelization and some heuristics for the problem of scheduling it jobs on in stages where at each stage we have a known number of unrelated machines. The jobs might skip stages and, therefore, we have what we call a hybrid flexible flowshop problem. We also consider per machine sequence-dependent setup times which can be anticipatory and non- anticipatory along with machine lags, release dates for machines, machine eligibility and precedence relationships among jobs. Manufacturing environments like this appear in sectors like food processing, ceramic tile manufacturing and several others. The optimization criterion considered is the minimization of the makespan. The MIP model and the heuristics proposed are tested against a comprehensive benchmark and the results evaluated by advanced statistical tools that make use of decision trees and experimental designs. The results allow us to identify the constraints that increase the difficulty. (C) 2006 Elsevier Ltd. All rights reserved
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