Using network modelling methods to improve the foam glass production technology

Introduction. The authors applies the critical path method to the simulation of successive technological operations and individual foam glass technology stages. The strengths of the method are user friendliness, availability and consistency. The author proved the expediency of its introduction into business processes and branches of the national economy. Materials and methods. The foam glass production process is presented as a project network schedule, which represents a weighted graph. The source material for this schedule is a list of technological operations, their sequence in the production process, which is conditioned by the feasibility of some operations upon the completion of others, and also by the time of their implementation. In the course of transformation of operational stages, the authors applied the Gauss distribution which was set by the probability density, representing an indicator function for network scheduling methods. Harrington’s D-function was calculated to estimate the effectiveness of the model implementation. Results. The effectiveness of the proposed network scheduling methods and foam glass production management is demonstrated. The author experimentally proved a 10 % rise in the efficiency of technological operations in the process of modelling the indicators of the thermal impact on foam glass. Conclusions. The technological process of foam glass production includes a set of operations, characterized by their performance time and sequence in the production cycle, which allows using network scheduling methods to evaluate each individual stage taking into account the time spent and the amount of work performed at each stage. Any adjustment of estimated values of performance of separate operations in the course of modernization of a network schedule contributes to a reduction in the total time of foam glass production, which affects the cost of material. The quantitative evaluation was the time of all stages with pre-set values of probabilities. The Harrington’s desirability function was used as an indicator of efficiency (qualitative evaluation) of network scheduling methods and their application.