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This study shows an example of software implementation of previously developed mathematical models [1] for calculating the characteristics of a new type of ice cream. The use of such computer modeling makes it possible to eliminate full-scale experiments, reduce the time for developing a new product, increase the competitiveness of the resulting product, take into account the advantages and needs of consumers, and promote innovative development in the food industry. The “Blending” application has been created, developed in the Visual C++ environment for personal computers running on the Windows operating system, giving the user an effective tool that he can use to calculate the recipes for new varieties of ice cream. The application provides two calculation modes - direct and reverse. In direct calculation, the user can select the recipe for the input ingredients of a new ice cream and analyze the resulting product characteristics. In reverse calculation, the user sets the required product characteristics and their weighting coefficients, and the computer calculates the optimal ratios of input ingredients. As an optimization method in this approach, a modification of the Monte Carlo random search method is proposed. The program uses the number of unsuccessful attempts to generate numbers as a control parameter, so in the process of several experimental runs of the program, the optimal value of this number was found. The practical results of the study are that the developed “Blending” program puts in the hands of the user-technologist a tool that he can use to calculate the recipe for new varieties of ice cream with the addition of blue tea and rose hips, examining the properties of the product on a computer based on computer modeling. [ABSTRACT FROM AUTHOR]

This paper computes a quality adjusted price index for the personal computer CPU from 1996 to 2000. The index is based on the pure characteristics demand model. I first compute the quality adjusted price index for the whole market, and show that it is very comparable with the hedonic price index but more sensitive to changes in product quality. Two types of the hedonic index are considered. One is the dummy variable index and the other is the formulation in Pakes (2003). When I group consumers by their willingness to pay for attribute improvement, the index shows consumer groups are differently affected by their product choices. [ABSTRACT FROM AUTHOR]