A multiple-objective grouping genetic algorithm for the cell formation problem with alternative routings

Vin, Emmanuelle; Lit, Pierre De; Delchambre, Alain

doi:10.1007/s10845-004-5888-4

Cited by 37 publications

(12 citation statements)

References 29 publications

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“…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. For minimizing inter-cellular traffic, the authors proposed an integrated approach based on a multiple-objective grouping genetic algorithm for the preferential routing selection of each part and an integrated heuristic for the cell formation problem.…”

Section: Literature Reviewmentioning

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%

Section: Introductionmentioning

confidence: 99%

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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“…Such route planning problems were mostly addressed in the context of group technology (GT). Example literature includes Dimopoulos (2006), Kim, Beak, and Jun (2005), Mahdavi, Rezaeian, Shanker, and Amiri (2006), Nsakanda, Diaby, and Price (2006), Spiliopoulos and Sofianopoulou (2007), Vin, Lit, and Delchambre (2005).…”

Section: Relevant Literaturementioning

confidence: 99%

An efficient approach to cross-fab route planning for wafer manufacturing

Shih

Chen

2009

Expert Systems with Applications

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“…Each type is composed of several machines able to achieve a type of operation. One process plan corresponds to alternative routings (=sequence of machines) (Askin et al, 1997); (Suresh and Slomp, 2001), (Yin and Yasuda, 2002), (Vin and al., 2005).…”

Section: Alternative Routes/processesmentioning

confidence: 99%

“…We (Vin and al., 2005) presented an genetic algorithm in two steps. The used algorithm is based on a semi-simultaneous method.…”

Section: Originsmentioning

confidence: 99%

An Adapted Genetic Algorithm to Solve Generalized Cell Formation

Vin¹

2009

13th IFAC Symposium on Information Control Problems in Manufacturing

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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.

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A multiple-objective grouping genetic algorithm for the cell formation problem with alternative routings

Cited by 37 publications

References 29 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 efficient approach to cross-fab route planning for wafer manufacturing

An Adapted Genetic Algorithm to Solve Generalized Cell Formation

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