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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
|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|
|Publisher Link: ||http://dx.doi.org/10.1007/s10479-008-0423-1|
|Appears in Collections:||Published Articles (Business School)|
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