OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 5 No. 1 2023 Due to this, the preliminary stage of any industrial system is critical for any enterprise. The competitive abilities directly depend on approaches to the industrial operation process as a generalized production system having numerous target functions depending on various factors [11, 12]. The primary estimation of the production system performance should be made at the preparation stage for further long-term solutions, which in turn directly impact the amount of capital investments in whole. The basic challenges of the selection of the best design option for production systems depend on complex process tasks, meaning a large number of contributing factors and absence of patterns [13, 14]. Knowledge of design baseline allows selecting the most rational options to arrange the production system and develop management algorithms for further automation of preparation and design process of production systems using mathematical methods. When designing a production system it is necessary to have a database with information, comprising necessary data on the subject and representing the existing connections and/or patterns between the elements and properties of the compared facilities [15–21]. The availability of information on the analyzed facilities allows making informed decisions, which may be the basis for modeling, predicting and optimizing the system. This is of particular relevance at the stage of organizational or process, when it is necessary to make an informed choice from a large number of options in a short time. With this, one is targeted to output economical and processing production performance. The selection of effective facilities and systems is often a challenging and multi-criteria process requiring significant time expenditures, which results in the decrease of efficiency in process preparation [22–26]. In real settings, the signs are individually determined, according to which the assessment is made and the optimal solution is selected. Considering the fact that the parameters are targeted to achieve the extreme points (increase or decrease) and, while providing manufacturing flexibility, when ranking parameters by priority can be variable, accounting production specifics, the process of the criteria analysis becomes more complicated. The purpose of the work is to create a generalized methodology for the criteria analysis of multivariant systems, the meaning of which is to detect parameters that are most important in real conditions at the moment of making an informed decision, with further analysis under prioritized parameters. The result of the system analysis should target the provision of efficiency of the analyzed system in the conditions of accepted limitations and priorities. The sequence of the selection of the optimal variant of the production system is determined by the economical, technical and organizational tasks. When designing, it is necessary to understand that any processing solutions can be and should be changed or adjusted during the implementation at the executive stage of production. The difficulty and labour intensity of the whole process of multivariant system design is the comparison of efficiency and profitability of various options. With this, the comparison of equivalent options is necessary at every stage of design. The degree of depth and structure of production system depends on the type of production. Research methodology To formalize the problem, let’s use the basics of matrix analysis. Let Оi be the facilities or systems for comparison, where i varies from 1 to m, and m is the number of facilities/systems for comparison. The parameters, characterizing the comparison systems, are marked as Pj, where j varies from 1 to n, and n is the number of parameters, selected for comparative analysis. In this way, Оi = О1, О2, … Оm; Pj = P1, P2, … Pn, P ϵ О. As each criterion usually has its own dimension, to make matrix computation more convenient, considering the priority of the minimal or maximal value of a criterion, let’s represent the entries of the matrix as the non-dimensional value aij. For encoding, it is necessary to rank the indicators of Pj into those, preferable in the maximal value (increasing is required), and those preferable in the minimal value (decreasing is required). If the maximal value of the criterion in the specified comparison conditions, is more preferable, the matrix entry aij in the encoded view will have a non-dimensional numeral value equaling the module of the
RkJQdWJsaXNoZXIy MTk0ODM1