A differential evolution algorithm for the manufacturing cell formation problem using group based operators
Cellular manufacturing (CM) is an important application of group technology (GT), a manufacturing philosophy in which parts are grouped into part families, and machines are allocated into machine cells to take advantage of the similarities among parts in manufacturing. The target is to minimize inter-cellular movements. Inspired by the rational behind the so called grouping genetic algorithm (GGA), this paper proposes a grouping version of differential evolution (GDE) algorithm and its hybridized version with a local search algorithm (HGDE) to solve benchmarked instances of cell formation problem posing as a grouping problem. To evaluate the effectiveness of our approach, we borrow a set of 40 problem instances from literature and compare the performance of GGA and GDE. We also compare the performance of both algorithms when they are tailored with a local search algorithm. Our computations reveal that the proposed algorithm performs well on all test problems, exceeding or matching the best solution quality of the results presented in previous literature.
differential evolution algorithm cell formation problem grouping genetic algorithm