Computer experiments have received a great deal of attention in many fields of science and technology.Most literature assumes that all the input variables are quantitative.However,researchers often encounter computer experiments involving mixed input variables: both quantitative and qualitative variables.In this talk,a new design,called optimal clustered-sliced Latin hypercube design,is proposed.The proposed design is one kind of sliced Latin hypercube design with points clustered in the design region,and possesses good uniformity within each slice.For computer experiments,such designs help to measure the similarities between responses of different level-combinations in the qualitative variables.Furthermore,an adaptive analysis strategy intended for the proposed designs is developed.The proposed strategy allows us to automatically extract useful information from all auxiliary responses to increase the precision of prediction for the target response.The proposed designs,with the help of the proposed analysis strategy,are demonstrated to be effective via simulation examples.A real-life example from the food engineering literature is also studied to anticipate the improvements gained from using the proposed design and analysis strategy.