Ghasemi A.Heavey C.Kabak K.E.2023-06-162023-06-1620199.78E+120891-7736https://doi.org/10.1109/WSC.2018.8632204https://hdl.handle.net/20.500.14365/3659Arena;Bayer;Chalmers;et al.;Simio;The AnyLogic Company2018 Winter Simulation Conference, WSC 2018 -- 9 December 2018 through 12 December 2018 -- 144832Photolithography plays a key role in semiconductor manufacturing systems. In this paper, we address the capacity allocation problem in the photolithography area (CAPPA) subject to machine dedication and tool capability constraints. After proposing the mathematical model of the considered problem, we present a new genetic algorithm named RGA which was derived from a psychological concept called Reference Group in society. Finally, to evaluate the efficiency of the algorithm, we solve a real case study problem from a semiconductor manufacturing company in Ireland and compare the results with one of the genetic algorithms proposed in the literature. Results show the effectiveness and efficiency of RGA to solve CAPPA in a reasonable time. © 2018 IEEEeninfo:eu-repo/semantics/closedAccessEfficiencyGenetic algorithmsPhotolithographyCapacity allocationEffectiveness and efficienciesMachine dedicationNew genetic algorithmsReal caseReference groupSemiconductor manufacturingSemiconductor manufacturing systemsSemiconductor device manufactureConference Object10.1109/WSC.2018.86322042-s2.0-85062598473