A Novel Multi-Objective Genetic Algorithm for Cell Formation Problems

contemplating a real cell formation problem (CFP), we can see there is not just a single objective to optimize; ideally, an eligible solution should be optimized in more than one objective simultaneously. The design of manufacturing cells with respect to multiple criteria has been attractive research for more than two decades. Due to contradictory and incommensurable objectives, most of the exact and heuristic algorithms fail to solve multi-objective cell formation problems. In this paper, we propose a novel methodology based on a genetic algorithm (GA) dealing with multi objective-based CFPs. We present a new and unique chromosome inspired by the essence of the CFP. This chromosome is defined somehow by two spirits (i.e., rows), and this fact empower us to propose unique and equally adaptable crossover and mutation operators. Furthermore, the proposed methodology of dealing with the multi-objective optimization problem (MOOP) shows a highly powerful tool in finding a Pareto optimal set. We engage a dummy evolutional objective (DEO) to better inspire the essence of the MOOP. Both main contribution of this paper, the unique chromosome for the CFP and the new methodology for solving the MOOP are adaptive enough to be applied to the gamut majority of problems related to the CFP and the MOOP