The changing global scenario and increased competitiveness has enforced manufacturers to optimize their processes to sustain their position in the market. Manufacturing firms rely on an effective process plan to efficiently utilize their manufacturing resources. Therefore, process plan selection is a crucial and often very complex task. The complexity further increases when there are alternative machines, setups, and process plan selection problems using Genetic Algorithms with Chromosome Differentiation (GACD) optimization technique. Comparative results on a case study as well as on randomly generated datasets of increasing complexity confirm that the proposed algorithm achieves an improved balance between exploration and exploitation, and has a better ability to escape from the local minima than other efficient meta-heuristic approaches.
|Title of host publication||Evolutionary Computing in Advanced Manufacturing|
|Editors||Manoj Tiwari, Jenny A. Harding|
|Publication status||Published - 20 Jun 2011|