Part family and machine cell formation in multiperiod planning horizons of cellular manufacturing systems

Abstract Machine cell formation in design of cellular manufacturing systems has traditionally ignored the issues of reliability. As such, the machine cell formation methods have been developed without the explicit considerations of system reliability and maintainability and the systems designed by these methods may have poor reliability and maintainability and hence result in low availability and productivity. In this paper, we discussed how the reliability issues should be incorporated into solving machine cell formation problems. A new formulation for machine cell formation with reliability considerations in cellular manufacturing systems was developed. The formulation is based on multi-attribute utility theory and represents the tradeoff the designers are willing to make between reliability and other design attributes in design of cellular manufacturing systems. An example was used to illustrate the application of the formulation for machine cell formation with reliability considerations. An algorithm for determining the optimal machine cell size in design of cellular manufacturing systems is also proposed.

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Machine cell formation for production management in cellular manufacturing systems

International Journal of Production Research ◽

10.1080/00207540600664144 ◽

2007 ◽

Vol 45 (4) ◽

pp. 913-934 ◽

Cited By ~ 22

Author(s):

Farouq Alhourani ◽

Hamid Seifoddini

Keyword(s):

Manufacturing Systems ◽

Production Management ◽

Cellular Manufacturing ◽

Cell Formation ◽

Cellular Manufacturing Systems ◽

Machine Cell

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Cell Formation and Part Family Identification by Using Traditional Methods

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.854.121 ◽

2016 ◽

Vol 854 ◽

pp. 121-126 ◽

Cited By ~ 1

Author(s):

M. Shunmuga Sundaram ◽

V. Anbumalar ◽

P. Anand ◽

B. Aswinkumar

Keyword(s):

Manufacturing Systems ◽

Cellular Manufacturing ◽

Rank Order ◽

Cell Formation ◽

Second Phase ◽

Single Linkage ◽

Part Family ◽

Part Families ◽

Machine Cell ◽

Combined Algorithm

A Combined Algorithm is proposed to form the machine cell and part family identification in the cellular manufacturing system. In the first phase, part families identification, by using Rank Order Clustering (ROC) and Modified Single Linkage Clustering (MOD-SLC). In second phase, Machine cell formation, by using Rank Order Clustering (ROC) and Modified Single Linkage Clustering (MOD-SLC), which is to assign machines into machine cells to produce part families. The above Proposed method is tested by using standard problems and compared with other method results for the same standard problems. Grouping efficiency is one of the most widely used measures of quality for Cellular Manufacturing Systems.

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Modeling the Scheduling Problem in Cellular Manufacturing Systems Using Genetic Algorithm as an Efficient Meta-Heuristic Approach

Journal of Artificial Intelligence and Technology ◽

10.37965/jait.2021.0018 ◽

2021 ◽

Author(s):

Amin Rezaeipanah ◽

Musa Mojarad

Keyword(s):

Genetic Algorithm ◽

Manufacturing Systems ◽

Cellular Manufacturing ◽

Programming Model ◽

Heuristic Method ◽

Cell Formation ◽

Mixed Integer ◽

Test Problems ◽

Cellular Manufacturing Systems ◽

Machine Grouping

This paper presents a new, bi-criteria mixed-integer programming model for scheduling cells and pieces within each cell in a manufacturing cellular system. The objective of this model is to minimize the makespan and inter-cell movements simultaneously, while considering sequence-dependent cell setup times. In the CMS design and planning, three main steps must be considered, namely cell formation (i.e., piece families and machine grouping), inter and intra-cell layouts, and scheduling issue. Due to the fact that the Cellular Manufacturing Systems (CMS) problem is NP-Hard, a Genetic Algorithm (GA) as an efficient meta-heuristic method is proposed to solve such a hard problem. Finally, a number of test problems are solved to show the efficiency of the proposed GA and the related computational results are compared with the results obtained by the use of an optimization tool.

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