A hybrid genetic-goal programming approach for improving group performance in cell formation problems

This paper proposes a hybrid genetic-goal programming approach to improve group performance in cell formation problems in manufacturing systems. The problem is formulated mathematically as a multi-objective programming problem. A proposed genetic algorithm (GA) is used to solve the problem. The chromosomes of the GA represent a combination of machines and parts. The proposed approach improves group performance by considering group efficacy as the performance measure. A software package corresponding to the proposed approach is developed in C# has a user-friendly GUI. Thirty problem instances of varying sizes prove the superiority of the approach in terms of group efficacy by avoiding duplicity in the allocation of parts into machines.