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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Vydáno v:	Computers & industrial engineering Ročník 98; s. 413 - 426
Hlavní autoři:	Bootaki, Behrang, Mahdavi, Iraj, Paydar, Mohammad Mahdi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Elsevier Ltd 01.08.2016 Pergamon Press Inc
Témata:	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
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Abstract	•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.
AbstractList	•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. 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 epsilon -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. 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.
Author	Bootaki, Behrang Mahdavi, Iraj Paydar, Mohammad Mahdi
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Snippet	•Mathematical model for clustering workers and machines in product mix variation case.•The mutual interest between workers is introduced for the first... This paper deals with configuring manufacturing cells when product mix variation occurs. Most of researches have addressed the cell formation problem when...
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SubjectTerms	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
Title	New criteria for configuration of cellular manufacturing considering product mix variation
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