Tu, YiliuAnderson, David C2017-01-262017-01-2620172017http://hdl.handle.net/11023/3591Sheet metal parts processed by CNC turret punches are often grouped together onto single sheets of material, known as nests, in random combinations based on current demand. The content and configuration of each nest is highly variable, resulting in unique hole locations and quantities. A hybrid genetic algorithm (HGA) is presented for the development of a robust turret layout given a set of parts with known tool requirements and flexible operation sequences. HGA population members are improved through an iterative local search heuristic that alternately considers part operation sequences and turret layout. Improved members replace their unimproved predecessors in the population. HGA solutions are tested for robustness using a modified form of the Layout Configuration Robustness Index (LCRI). The HGA solutions are shown to offer a statistically significant decrease in total turret rotation distance when compared to a population of randomly generated layouts.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Engineering--Operations ResearchoptimizationHybrid Genetic AlgorithmPunch PressQuadratic Assignment ProblemNestingSheet MetalFlexible Sequencemaster thesis10.11575/PRISM/26483