An Adjustable Grouping Genetic Algorithm for integrated design of cellular manufacturing system

Abstract

This paper focus on the design of cellular manufacturing system (CMS), which integrates

the structural parameters of a number of cells, and parts - machines assignment to cells, and the

operational parameter of scheduling under machine duplications and alternate routings / cross-

flow environment. The mathematical model developed here is aiming towards the minimization

of total cost of operation, which includes machine utility cost, inter-cell cost and cross-flow

costs. A detailed study on the model validity implies that integrated approach enables to evolve

better CMS design decisions in terms of operational cost when compared to the literature part-

machine grouping decisions. This integrated design approach can be applied to a variety of

manufacturing system designs by relaxing the model constraints. Considering the complexities

associated with non-linearity in the model, an adjustable grouping genetic algorithm (AGGA) is

proposed. The mathematical model is relaxed to fixed number of cells and solved by ILOG

CPLEX solver and compared with the solutions obtained by AGGA. The obtained results

demonstrate that the proposed AGGA requires less number of evaluations and is capable of

evolving optimal or near optimal solutions in a computationally efficient manner. Further, the

performance of AGGA tested by changing the features of grouping based selection operator and

adaptive parameter and arrived two of its variants denoted as AGGA_1 and AGGA_2. The

comparative analysis reveals that AGGA has improved convergence as an effect of the proposed

selection operator and better computational capabilities due to adaptive evaluations.