Machine cell formation for cellular manufacturing systems using an ant colony system approach

Abstract

The aim of a cellular manufacturing system is to group parts that have similar processing needs into part families and machines that meet these needs into machine cells. This paper addresses the problem of grouping machines with the objective of minimizing the total cell load variation and the total intercellular moves. The parameters considered include demands for number of parts, routing sequences, processing time, machine capacities, and machine workload status. For grouping the machines, an ant colony system (ACS) approach is proposed. The computational procedure of the approach is explained with a numerical illustration. Large problems with up to 40 machines and 100 part types are tested and analyzed. The results of ACS are compared with the results obtained from a genetic algorithm (GA), and it is observed that its performance is better than that of GA.

Sustainability Formation of Machine Cells in Group Technology Systems Using Modified Artificial Bee Colony Algorithm

“…Different parameters, such as machine workload, operation sequences, unit processing times on every equipment, and manufacturing quantity, among others, are considered in this study. From the literature [16], 20 data sets have been taken, and the experimental works have been carried out using the proposed MABC algorithm, and the outcomes were compared with those of the other two approaches, GA [16] and ACO [21]. The parameter settings selected for the problem, its implementation, and the results obtained are discussed below:…”

“…That is, variations obtained for cycles 400-500 are very minimal. For the cell number 4, drastic changes are observed in intercellular moves from the cycles 100 to 200, and minimum variation from the cycle of 200 to 500. the literature [16], 20 data sets have been taken, and the experimental works have been carried out using the proposed MABC algorithm, and the outcomes were compared with those of the other two approaches, GA [16] and ACO [21]. The parameter settings selected for the problem, its implementation, and the results obtained are discussed below:…”

“…An e-constraint method is then applied to solve the model and to generate the efficient solutions. Other metaheuristics, like tabu search [19,20], ant colony optimization (ACO) [21,22], particle swarm optimization [23,24], and scatter search [25] have also been proposed for designing CMS. Chattopadhyayet et al [26] discussed machine-part cell formation through visual decipherable clustering of self-organizing map.…”

The evolution of cell formation problem methodologies based on recent studies (1997–2008): Review and directions for future research

“…The most remarkable outcome was that ant systems performed better than the other techniques as far as an equal number of solution alternatives was concerned. Prabhaharan et al (2005) also proposed an ACO approach for grouping the machines, with the objective of minimising total cell load variation and total intercellular moves. A number of parameters were also considered in this study, such as demands for numbers of parts, routing sequences, processing time, machine capacities and machine workload status.…”

Application of principal component analysis in machine-part cell formation

“…George et al, 2003 developed an analytical-iterative clustering algorithm for cell formation in cellular manufacturing systems with ordinal-level and ratio-level data. Prabhaharan et al (2005) and Kao and Fu (2006) proposed antcolony based clustering algorithm for manufacturing cell design. Muruganandam et al (2005), Adil and Ghosh (2005) and Yin et al (2005) successfully introduced various metaheuristic algorithms to solve the machine cell formation problem in group technology.…”