Machine-component grouping using genetic algorithms

Lau

Proceedings of the Institution of Mechanical Engineers, Part B:

2004

One of the most practical approaches of improving productivity in a factory is to adopt the superior concept and technique of cellular manufacturing (CM) based on group technology (GT). Particularly, cell formation is an important, critical and difficult step in CM. In general, there have been a number of methodologies proposed for solving a machine-part grouping problem (MPGP). Besides considering the simple cell formation problem, some researchers have focused on machine flexibility, in which parts are having alternative routeings/process plans. However, it is very rare to consider the area of aggregation and disaggregation of machines in cell formation under uncertain constraints and uncertainty. In the light of this, the main aim of this present work is to address the MPGP holistically with the considerations of machine flexibility as well as machine aggregation and disaggregation simultaneously. In addition, based on the availability of alternative routeings, a method is proposed to generate an alternative solution for machine breakdown situations. Thus, the problem nature of this work will be more realistic and practical for today's global manufacturing era. The problem scope has been identified and the model is formulated in mathematical programming form. The objective function of this model is to minimize the total intercellular and intracellular part movement. Since MPGP has been proved to be non-polynomial (NP) complete, a genetic algorithm (GA), which is an excellent optimization technique, is employed to solve this problem.

Section: Illustration and Discussionmentioning

confidence: 99%

“…They have considered the condition of routeing flexibility in cell formation [10,11]. Consideration of multi-criteria objectives in cell formation was discussed by Pierreval and Plaquin [12] and Chan et al [13]. Routeing flexibility and multiple objectives were discussed by Zhao and Wu [14].…”

Section: Introductionmentioning

confidence: 99%

A holistic approach to manufacturing cell formation: Incorporation of machine flexibility and machine aggregation

Lau

Proceedings of the Institution of Mechanical Engineers, Part B:

2004

“…As shown in Table 5, the additional numerical Author (year) Problem size Machine Part HPH (Askin et al, 1991) 5×6 5 6 SCA (Kusiak and Cho, 1992) 6×8 6 8 AVV (Yin and Yasuda, 2002) 7×9 7 9 SA (Boctor, 1991) 7×11 7 11 ZODIAC (Cheng et al, 1998) 8×20 8 20 GA (Onwubolu and Mutingi, 2001) 10×15 10 15 SC-TSP (Balakrishnan and Jog, 1995) 12×19 12 19 GA (Chan et al, 1998) 20×35 20 35 ZODIAC (Chandrasekharan and Rajagopalan, 1989) 24×40 24 40 ZODIAC (Chandrasekharan and Rajagopalan, 1987) 40×100 40 100 experiments included 10 test samples, ranging from 5×6 (five machines with six parts) to 40×100 (40 machines with 100 parts), which are adapted from previous literature. The computational results are compared with the cell formation, and Microsoft office Excel 2007 and C are used as the calculation tools.…”

Section: Illustration and Computational Experimentsmentioning

confidence: 99%

“…Second, the machine-part matrix does not show the sequence of operations for each part; hence, it may face a problem when counting the number of intercell trips. Due to the NP-Complete property, the cell formation problem is difficult to solve; thus, numerous studies have applied heuristic methods to solve cell formation problems, such as simulated annealing algorithms (Boctor, 1991;Chen and Srivastava, 1994;Baykasoglu, 2004;Wu et al, 2009), tabu search algorithms (Sun et al, 1995;Onwubolu and Songore, 2000;Belarmino et al, 2001) and genetic algorithms (Gupta et al, 1996;Hwang and Sun, 1996;Lee et al, 1997;Chan et al, 1998;Mak et al, 2000;Onwubolu and Mutingi, 2001;Vin et al, 2005;Wu et al, 2007;Defersha and Chen, 2008;Deljoo et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

An electromagnetism-like mechanism for solving cell formation problems

Wei¹

2012

Sci. Res. Essays

This paper presents an electromagnetism-like mechanism (EM) algorithm in order to solve cell formation problems. Based on the attraction-repulsion principle in electromagnetic theory, each combination of the part and machine groups was regarded as a charged particle. The total force calculation and the move along the total force calculation were then determined out to either maximize the cell group effectiveness or minimize the exceptional elements (EE) and the number of vacancies (NV). A comparison with other algorithms in the related literature found that the final result of the EM algorithm met the expected quality in terms of the exceptional elements and number of vacancies, regardless of whether or not new cells were added.

Proceedings of the Institution of Mechanical Engineers, Part B:

“…Chan et al [6] proposed an approach for cell formation problem by considering machine flexibility and aggregation. Several nonconventional optimization techniques have been used in cell formation problems and in analysing the clusters for total cell movements [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

An optimal design approach for a cellular manufacturing system

Giri

Srinivas

Mouli

2007

The current paper proposes an optimum clustering approach for automatic generation of machine cells and part families. The design of a cellular manufacturing system begins with a specified ‘part-machine incidence’ matrix, showing the machine sequence and volume of production. The arrangement of machines in the cells is addressed in the present paper by finding an optimal machine sequence, which maximizes the overall flow of components between the machines. An optimal number of cells is arrived heuristically by accounting the total intercell movements using the obtained machine sequence. The methodology is illustrated with examples.