Grouping genetic algorithms: an efficient method to solve the cell formation problem

Lit, Pierre De; Falkenauer, Emanuel; Delchambre, Alain

doi:10.1016/s0378-4754(99)00122-6

Cited by 60 publications

(24 citation statements)

References 13 publications

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“…Their computational experiments signify that the hybrid approach outperforms the standard grouping genetic algorithm in terms of grouping efficacy. DeLit et al [18] proposed a grouping genetic algorithm for the cell formation problem to minimize traffic of items between the cells. Vin et al [55] addressed the cell formation problem with alternative part routing, considering capacity constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Products needing similar operations and a common set of resources are grouped into families, the resources being regrouped into production subsystems [18].…”

Section: Introductionmentioning

confidence: 99%

“…The computational intractability of the problem makes the use of heuristic approaches appropriate. Given that MPCF is a grouping problem (in a grouping problem, one seeks for grouping of objects into disjoint groups with the aim of optimizing a given objective function of groups and a set of hard constraints), successful applications of grouping genetic algorithm (GGA) on MPCF have been reported by researchers [18,11,55,10]. Up to now, there are a few algorithms that have been mainly modified to consider the structure of grouping problems, such as cell formation problem, like genetic algorithm, differential evolution algorithm, and so on.…”

Section: Introductionmentioning

confidence: 99%

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A Metaheuristic algorithm for the manufacturing cell formation problem based on grouping efficacy

Noktehdan

Seyedhosseini

Saidi‐Mehrabad

2015

Int J Adv Manuf Technol

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The cell formation problem determines decomposition of the manufacturing cells of a production system. Machines are assigned to the cells to process one or more part families so that each cell is operated independently and the inter-cellular movements are minimized. This paper proposes a new algorithm for grouping problems (bin packing, graph coloring, scheduling, etc.) which is a grouping version of an almost new algorithm (league championship algorithm (LCA)), and we used it to solve benchmarked instances of cell formation problem posing as a grouping problem. To evaluate the effectiveness of our approach, we borrow a set of 35 most widely used benchmark problem instances from literature and compare the performance of grouping LCA (GLCA) and several well-known algorithms published. The proposed algorithm can reach the best solution for 29 of the 35 benchmark problems and differs with the best-known solution of three benchmark problems only with 0.7 % average gap. We also used a new method to find the number of initial cells. The results show that GLCA may hopefully be a new approach for such kinds of difficult-to-solve problems. Moreover, a real-world industrial case is provided to show how the proposed algorithm works. Considering the performance of the GLCA algorithm on all test problems, the proposed algorithm should thus be useful to both practitioners and researchers.

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Section: Literature Reviewmentioning

confidence: 99%

“…Products needing similar operations and a common set of resources are grouped into families, the resources being regrouped into production subsystems [18].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Metaheuristic algorithm for the manufacturing cell formation problem based on grouping efficacy

Noktehdan

Seyedhosseini

Saidi‐Mehrabad

2015

Int J Adv Manuf Technol

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“…Heuristic methods can be used for large problems, but they often become trapped in local optima (De Lit, et al 2000). More recently, stochastic optimisation algorithms (meta-heuristic methods) have been used for solving the CFP.…”

Section: The Cell Formation Problemmentioning

confidence: 99%

“…The gene encoding scheme and the modified genetic operators enable the GGA to De Lit et al (2000) used the GGA to solve the CFP with a fixed maximum cell size. Brown and Sumichrast (2001) tested the GGA using data sets from the literature.…”

Section: Genetic Algorithms and Grouping Genetic Algorithmsmentioning

confidence: 99%

An Enhanced Grouping Genetic Algorithm for solving the cell formation problem

Tunnukij

Hicks

2009

International Journal of Production Research

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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

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A Novel Grouping Genetic Algorithm for Assigning Resources to Users in WCDMA Networks

Cuadra

Salcedo‐Sanz

Carnicer

et al. 2015

Applications of Evolutionary Computation

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Grouping genetic algorithms: an efficient method to solve the cell formation problem

Cited by 60 publications

References 13 publications

A Metaheuristic algorithm for the manufacturing cell formation problem based on grouping efficacy

A Metaheuristic algorithm for the manufacturing cell formation problem based on grouping efficacy

An Enhanced Grouping Genetic Algorithm for solving the cell formation problem

A Novel Grouping Genetic Algorithm for Assigning Resources to Users in WCDMA Networks

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