New criteria for configuration of cellular manufacturing considering product mix variation

•Mathematical model for clustering workers and machines in product mix variation case.•The mutual interest between workers is introduced for the first time.•Comparing two different MOP solution techniques to the proposed problem. This paper deals with configuring manufacturing cells when product mix...

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Bibliographic Details
Published in:Computers & industrial engineering Vol. 98; pp. 413 - 426
Main Authors: Bootaki, Behrang, Mahdavi, Iraj, Paydar, Mohammad Mahdi
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.08.2016
Pergamon Press Inc
Subjects:
Algorithms
Cellular manufacturing systems
Constants
Criteria
Incidence
Manufacturing cells
Mathematical analysis
Mathematical models
Mathematical programming
Matrices (mathematics)
NSGAII
Optimization
Optimization algorithms
Pareto optimum
Product mix variation
Product mixes
Stochastic models
Studies
Worker interest
ε-constraint method
Cellular manufacturing systems
NSGAII
Worker interest
ε-constraint method
Product mix variation
ISSN:0360-8352, 1879-0550
Online Access:Get full text
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Summary:•Mathematical model for clustering workers and machines in product mix variation case.•The mutual interest between workers is introduced for the first time.•Comparing two different MOP solution techniques to the proposed problem. This paper deals with configuring manufacturing cells when product mix variation occurs. Most of researches have addressed the cell formation problem when part-machine incidence matrix is constant even for dynamic/stochastic case. But to the nature of CMS in manufacturing products in mid-variety and mid-volume, the product mix variation is not too far-fetched. Product mix variation causes the part-machine incidence matrix to change. To formulate the proposed problem two different criteria are considered which one relates to worker experts and another to worker relations. The first object considers the maximizing the expert levels in manufacturing cells. While the second object tries to maximize the interest levels in manufacturing cells. To make these concepts practical, a mathematical formulation which minimizes the voids of both worker-machine and worker-worker incidence matrices is developed. Due to the non-homogenous nature of the objective functions and possible conflicts, a bi-objective programming approach is applied. To find the Pareto-optimal front, the augmented ε-constraint method (AUGMECON) is applied. Since AUGMECON may not provide non-dominated set in a reasonable time, especially for large-size instances, NSGAII algorithm is customized and applied to produce optimal/near optimal Pareto solutions. To assess the performance of the proposed NSGAII algorithm, several randomly generated test problems were solved for a set of well-known multi-objective performance metrics.
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ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2016.06.021