| dc.description.abstract | Cellular manufacturing (CM) is a crucial method to adapt lean manufacturing with
an enormous archive of research and studies. One pivotal task of CM is the cell formation
problem (CFP) that can determine how successful the formation of cells is. This study's
goal is to effectively implement particle swarm optimization (PSO) as a linear integer
program to solve the cell formation problem (CFP). The proposed model adopts the
expression of particle velocities as proportional likelihood, uses Sigmoid function as a
probability of change, and introduces a weight matrix for a repair heuristic that can
effectively boost the overall performance. Different well-known benchmarks are used as a
reference to compare the grouping efficacy of the proposed model with Intelligent Particle
Swarm Optimization (IPSO) - a 2-phase network to evaluate the proposed model's
effectiveness. Numerical results have shown that the proposed model can perform well in
most cases when the best grouping efficacy values are equal or better than that of IPSO.
Furthermore, the results also show significant improvements of both the convergence
speed and the consistent of PSO algorithm by implementing weight matrix into the
instance of assigning unassigned-parts to formed cells. Although there are still visible
weaknesses in the current work, this study can be a ground work for improvements and
innovations for future research. | en_US |
