Performances Of Metaheuristic Algorithms In Optimizing Tool Capacity Allocations - Goheannee (2014)
Semiconductor manufacturing industry in general has moved into high mix productions resulting from the drastic pace of product innovation. Capacity planning In semiconductor manufacturing facility, such as allocating right mix of products to maximize the capacity output, needs to consider multiple mutually influenced constraints in resource, product demand, as well as product and process characteristics. To achieve the best allocation, optimization methods, such as metaheuristic algorithms are commonly used. This research compares the performances of various metaheuristic algorithms to optimize tool capacity allocation in two case studies. In this research, the algorithms studied includes Genetic Algorithm, Particle Swarm Optimization Algorithm, Differential Evolution Algorithm, Harmony Search Algorithm, Teaching-LearningBased Optimization Algorithm and Black Hole Algorithm. These algorithms are inspired by different nature of phenomenon. The former three are common in literature for tool capacity allocation problems. The latter three are the next generation of metaheuristic algorithms and albeit popular elsewhere, have no known attempt in tool capacity allocation problems. The case studies were obtained from two real industries and five demand scenarios were derived. The demand scenarios were with different demand intensities and levels. For each case study, a capacity model was constructed in Microsoft Excel spreadsheet, as an input to the above mentioned metaheuristic algorithms which programmed in Matlab coding. The performances considered are tool utilization and aggregate capacity outputs.