Machine cell formation in generalized group technology

Adenso‐Díaz, Belarmino; Lozano, Sebastián; Racero, Jesús; Guerrero, Fernando

doi:10.1016/s0360-8352(01)00056-0

Cited by 49 publications

(21 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, in the proposed case study, intracellular movement cost between two cells only depends on number of movements. This assumption is not far from the truth, since the cost difference between various scenarios due to distance is negligible (Adenso-Diaz et al 2001). …”

Section: Specific Assumptionsmentioning

confidence: 99%

Improved design of CMS by considering operators decision-making styles

Azadeh

Rezaei-Malek

Evazabadian

et al. 2014

International Journal of Production Research

View full text Add to dashboard Cite

Cell formation is one of the oldest problems in cellular manufacturing systems (CMS) including assigning parts, machines and operators to cells. Cell manufacturing contains a number of cells where each cell is responsible for processing the family of similar parts. Another important aspect of cell formation is worker assignment to cells. Since operators work together in long periods, it is suggested to consider operators' personal characteristics to increase their satisfaction and the productivity of system. This paper considers decision-making styles of operators (as an index of operator's personal characteristics) and presents a new mathematical programming model for clustering parts, machines and workers simultaneously. The model includes two objectives; (1) minimization of intracellular movements and cell establishment costs, (2) minimization of decision-making style inconsistency among operators in each cell. The paper applies ε-constraint method for solving the problem and gathering non-dominated solutions such as Pareto optimal solutions. Furthermore, this paper uses common weighted multicriteria decision analysis (MCDA)-data envelopment analysis method to choose the best solution from the candidate Pareto optimal solutions that have been achieved by solving the mathematical model. A real case study is investigated to show the capability of the proposed model to design CMS in the assembly unit. The proposed design assists decision-makers to develop cellular systems with more operators' satisfaction and productivity.

show abstract

Section: Specific Assumptionsmentioning

confidence: 99%

Improved design of CMS by considering operators decision-making styles

Azadeh

Rezaei-Malek

Evazabadian

et al. 2014

International Journal of Production Research

View full text Add to dashboard Cite

show abstract

“…Simulated Annealing (SA) has been used for solving the CFP (Boctor 1991, Adil, et al 1996, Boctor 1996, Sofianopoulou 1999. Tabu Search (TS) has been applied to the CFP by Logendran, et al (1994), Aljaber, et al (1997), and Adenso-Diaz, et al (2001).…”

Section: The Cell Formation Problemmentioning

confidence: 99%

An Enhanced Grouping Genetic Algorithm for solving the cell formation problem

Tunnukij

Hicks

2009

International Journal of Production Research

View full text Add to dashboard Cite

International audienceCell formation is often the first step in solving facility layout design problems. The objective is to group part families and machines so that they can be assigned to manufacturing cells. The cell formation problem is a non-deterministic polynomial (NP) complete problem which means that the time taken to produce solutions increases exponentially with problem size. This paper presents the Enhanced Grouping Genetic Algorithm (EnGGA) that has been developed for solving the cell formation problem. The EnGGA replaces the replacement heuristic in a standard Grouping Genetic Algorithm with a Greedy Heuristic and employs a rank-based roulette-elitist strategy, which is a new mechanism for creating successive generations. The EnGGA was tested using well-known data sets from the literature. The quality of the solutions was compared with those produced by other methods using the grouping efficacy measure. The results show that the EnGGA is effective and outperforms or matches the other methods

show abstract

“…Others authors (Kusiak, 1987); (Gupta, 1993); (Logendran et al, 1994); (Caux et al, 2000); (Adenso-Diaz, 2001) use several processes defined like a sequence of machines. (Pr 1 = {m 1 , m 2 , m 3 }, Pr 2 = {m 3 , m 2 , m 6 } where the product is defined by two process).…”

Section: Alternative Routes/processesmentioning

confidence: 99%

An adapted Genetic Algorithm to solve Generalized Cell Formation

Vin

Francq

Delchambre

2009

IFAC Proceedings Volumes

View full text Add to dashboard Cite

This paper addresses the cell formation problem with alternative process plans and machine capacity constraints. Given alternative process plans, machine capacities and quantities of parts to produce, the problem consists in defining preferential process and routing for each part (grouping of operations into machines) optimizing machines grouping into manufacturing cells. The problem can be decomposed in two distinct sub-problems: operations grouping on resources, yielding flows between the machines, and grouping of these latter into independent cells. The objective of the proposed method is to optimize both groupings (operations on machines and machines into cell) minimizing of the inter-cellular moves. To solve simultaneously both grouping interdependent problems, we propose a modified grouping genetic algorithm (SIGGA). In our adapted grouping genetic algorithm, each chromosome is composed of two parts, one part for each problem. According to different application rates, the genetic operators are applied on the first, on the second problem, or on both problems. Finally, the population chromosomes simultaneously evolve in both problems.

show abstract

Machine cell formation in generalized group technology

Cited by 49 publications

References 38 publications

Improved design of CMS by considering operators decision-making styles

Improved design of CMS by considering operators decision-making styles

An Enhanced Grouping Genetic Algorithm for solving the cell formation problem

An adapted Genetic Algorithm to solve Generalized Cell Formation

Contact Info

Product

Resources

About