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Title: Fuzzy extensions to Integer Programming models of cell-formation problems in machine scheduling
Authors: Papaioannou, Grammatoula
Wilson, John M.
Keywords: Cellular manufacturing system
Machine operation sequence
Integer programming
Uncertainty
Fuzzy models
Issue Date: 2009
Publisher: © Springer Science+Business Media
Citation: PAPAIOANNOU, G. and WILSON, J.M., 2009. Fuzzy extensions to Integer Programming models of cell-formation problems in machine scheduling. Annals of Operations Research, 166 (1), pp. 163 - 181.
Abstract: Cell formation has received much attention from academicians and practitioners because of its strategic importance to modern manufacturing practices. Existing research on cell formation problems using integer programming (IP) has achieved the target of solving problems that simultaneously optimise: (a) cell formation, (b) machine-cell allocation, and (c) part-machine allocation. This paper will present extensions of the IP model where part-machine assignment and cell formation are addressed simultaneously, and also a significant number of constraints together with an enhanced objective function are considered. The main study examines the integration of inter-cell movements of parts and machine set-up costs within the objective function, and also the combination of machine set-up costs associated with parts revisiting a cell when part machine operation sequence is taken into account. The latter feature incorporates a key set of constraints which identify the number of times a part travels back to a cell for a later machine operation. Due to two main drawbacks of IP modelling for cell formation, i.e. (a) only one objective function can be involved and (b) the decision maker is required to specify precisely goals and constraints, fuzzy elements like fuzzy constraints and fuzzy goals will be considered in the proposed model. Overall the paper will not only include an extended and enhanced integer programming model for assessing the performance of cell formation, but also perform a rigorous study of fuzzy integer programming and demonstrate the feasibility of achieving better and faster clustering results using fuzzy theory.
Description: This article is published in the journal, Annals of Operations Research [© Springer Science+Business Media]. The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-008-0423-1
Version: Accepted for publication
DOI: 10.1007/s10479-008-0423-1
URI: https://dspace.lboro.ac.uk/2134/17063
Publisher Link: http://dx.doi.org/10.1007/s10479-008-0423-1
ISSN: 0254-5330
Appears in Collections:Published Articles (Business School)

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