文章基本信息

标题：New Method to Optimize the Production Functions in the System of Safety in Operation Management
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作者：Adrian Vilcu ; Adrian Vilcu ; Ion Verzea 等
期刊名称：Procedia - Social and Behavioral Sciences
印刷版ISSN：1877-0428
出版年度：2018
卷号：238
页码：424-431
DOI：10.1016/j.sbspro.2018.04.020
语种：English
出版社：Elsevier
摘要：AbstractThe Safety in Operation system represents the framework within which every technological process operates. Any technological parameter - production, quality, must be analyzed in terms of this system. Mathematical and/or empirical modeling, as the first stage in the determination of the technological processes optimum operation regimes, represents a “must” both in the phase of conception, but mainly in the operation analysis phase. From this standpoint, the introduction of the active optimization method based on scheduled experiment represents an efficient tool to relieve the extreme conditions and to get information for technological processes optimum management.The work is focused on the mathematical modeling of the production function for an assembly of 13 drawing frames from two Romanian units, in terms of maintainability and reliability parameters. After determining the polynomial that characterizes the model, the work investigates the optimization of non-linear multi-variable polynomial function, and explains the context of getting the extreme values within the multi-factorial space.The work defines in its structure the notions of the safety in operation system used in research, applies quantitative study on a system from textile spinning mill field (one notices reliable operation times, break-down times, number of failures and the production of the technical systems on a pre-established time horizon); it statistically validates and mathematically optimizes the results.The theoretical approach consists in an algorithm in a unitary software application that can be used as a tool in the decision problems appeared during the technological process.
关键词：textile technological process;statistic modeling;reliability;production;industrial processes optimization