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"A mechanical assembly consists of several components to perform an intended function. At the design stage, the intended function must be converted into critical product dimensions. After determining the dimensions, a designer must determine the assembly tolerance and allocate this tolerance to the tolerances of the corresponding components. After determining the optimal tolerances, process selection must be conducted along with production allocation to the selected process. There are three aspects in commercial competition that must be considered by a manufacturing company: cost, quality, and delivery. The aim of this research is to develop an optimization model for process selection for a make to order company to minimize manufacturing cost, quality loss, and lateness cost. The model attempts to determine optimal tolerance and production allocation, which takes into consideration the production capacity and process sequence. Hence, the model attempts to include not only the product design decision, but also to solve the process selection and allocation problems. A numerical example is provided to show the implementation of the model."

"A mechanical assembly consists of several components to perform an intended function. At the design stage, the intended function must be converted into critical product dimensions. After determining the dimensions, a designer must determine the assembly tolerance and allocate this tolerance to the tolerances of the corresponding components. After determining the optimal tolerances, process selection must be conducted along with production allocation to the selected process. There are three aspects in commercial competition that must be considered by a manufacturing company: cost, quality, and delivery. The aim of this research is to develop an optimization model for process selection for a make to order company to minimize manufacturing cost, quality loss, and lateness cost. The model attempts to determine optimal tolerance and production allocation, which takes into consideration the production capacity and process sequence. Hence, the model attempts to include not only the product design decision, but also to solve the process selection and allocation problems. A numerical example is provided to show the implementation of the model."

"In this research, an optimization and improvement of gas spring design is discussed. The gas spring is used as a suspension component of an energy storing prosthetic knee. The gas spring replaces the quadricep muscles of transfemoral amputee. A deterministic and a stochastic optimization is proposed in this research. Both models are used to determine the optimal design variables of the gas spring: cylinder diameter, cylinder length, extension stroke, and compression stroke. The optimal design variables resulted from the deterministic optimization model must be further analyzed to determine the effect of its variation to the objective function. Monte Carlo simulation is used to determine the effect of such variation and making improvement when necessary. Process capability index (Cp) is used as a criteria to make such improvement considering the contribution to variation of design variables to the objective function. Stochastic optimization is proposed to find the optimal design variables by taking into consideration the randomness of its parameters. The objective function of the stochastic optimization is to maximize the capability process. Both Monte Carlo simulation and stochastic optimization was solved using Oracle Crystal Ball Software. From the simulation, the reduction of compression stroke and extension stroke standard deviations resulted in 30% improvement of energy storage standard deviation. The Cp is also improved about 70% from 0.99 to 1.44. The stochastic optimization resulted in extension stroke and compression stroke which are shorter than deterministic optimization with 1.25 process capabilty."