Abstract:The purpose of the work is the development of a technique for calculating spare parts in an auto service company based on an analysis of statistical information on the failure of details of each standard accumulated on daily information on the replacement of spare parts for serviced vehicles in previous planning periods. In the theory of system reliability, the calculation of the failure distribution function usually uses information on the main characteristic of failures -the time between failures, and in the absence of such information, it is necessary to use information on the number of failures at the time of receipt for maintenance of cars in this auto service plant. In this connection, the relationship between the distribution functions of these random variables is established using the reciprocity of the distribution processes to failure and the number of failures. For each of the competing theoretical functions of the distribution of the operating time to failure, an estimate is made of the average operating time of the details of each standard in the auto service plant on the basis of actual demand. The estimates obtained make it possible to calculate the number of spare parts for the subsequent replenishment period of the SPIE.
Abstract:The task of determining the optimal sizes of spare parts for an auto-service enterprise based on the maximum profit criterion for a discrete distribution of demand is formulated as a problem of quadratic programming with linear constraints. To calculate the probabilistic measure of the distribution of the values of the demand vector components, an approximation is used of the empirical distribution function of the demand components by hyper-Erlanger distribution functions, and the subsequent calculation of the corresponding distribution densities.
Abstract:On the example of the two-criterion problem with the objective functions of the maximum, the confidence probabilities of the demand and the minimum of the total costs show the applicability of the method of Vector Optimization of Particle Swarm Optimization (VEPSO). Compared with genetic algorithms and other methods of evolutionary modeling, this method is easy to implement and has high efficiency, as well as the accelerated cost of an approximate solution of the problem from the external archive of the no dominant best solutions to the Pareto front, which is the boundary of the Pareto-optimal Compromise) solutions.
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