Building a model for optimal quantity determination of manufacturing facilities

Аuthors

Galkin V. I.^*, Kuzina S. M.^**

Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia

*e-mail: galkin@mati.ru
**e-mail: bart_spa@mail.ru

Abstract

The paper presents a technique for a number of work places optimization at the enterprise with variable product release program. The developed technique is based on simulation and experimental design. The paper considers the operation of the enterprise manufacturing several kinds of products by assembling either purchased components or produced at this enterprise. The simulation model developed in the course of this study allows build and optimize manufacturing resources under various variants of enterprise's target figures.

The model was built with AnyLogic program, which allows specify time intervals at every stage of manufacturing either major product, or associated items. There is a possibility to model the situation with various number of assembling departments.

Based on the built model the authors carried out the optimization experiment, which allows compute an optimal number of equipment for the specified work-order quantity for all types of products. The paper suggests goal functions with productivity optimization. Using this instrument the results for each experiment were obtained by varying values of run-out production plan. It is found on what production volumes minimum quantity of equipment is optimal, and at what moment the number of working places should be increased. It is also determined that maximum possible quantity of equipment under specified production volume boundaries is not necessary.

The obtained results were processed according to the experimental design technique. The equation for corrected production effect computation as function of a number of assembling departments and products production volume. The proposed method is universal and can be applied for various types of production. The developed technique can be used as one of the instruments while developing the system of managerial decision making.

Keywords:

optimization, adoption of engineering solutions, simulation modeling, experimental design, regression analysis

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