In process optimization, a process is adjusted so as to optimize a set of parameters while meeting constraints, with the objective to either minimize the total processing time or maximize the throughput. This article focused on the process optimization of a fully automated yogurt and flavor-filling machine developed based on the industrial revolution 4.0 concept. Mathematical models were developed for minimizing the total processing time or maximizing the throughput of an Industry 4.0-based yogurt filling system with two different machine settings called Case-I and Case-II. In Case-I, the yogurt and flavors are filled at two distinct points while Case-II considers the filling of yogurt and flavors at a single point. The models were tested with real data and the results revealed that Case-II is faster than Case-I in processing a set of customer orders. The results were used as inputs for the single-dimension rules to check which one results in more intended outputs. Additionally, different performance measures were considered and the one with most importance to the management was selected.

In this paper, a mathematical model for large deformation of a cantilever beam subjected to tip‐concentrated load is presented. The model is governed by nonlinear differential equations. Large deformation of a cantilever beam has number of applications is structural engineering. Since finding an exact solution to such nonlinear models is difficult task, this paper focuses on developing soft computing technique based on artificial neural networks (ANNs), generalized normal distribution optimization (GNDO) algorithm, and sequential quadratic programming (SQP). The strength of ANN modeling for governing the equation of cantilever beam is exploited by the global search ability of GNDO and further explored by the local search mechanism of SQP. Design scheme is evaluated for different cases depending on variations in dimensionless end‐point load (ρ). Furthermore, to validate the effectiveness and convergence of algorithm proposed technique, the results of the differential transformation method (DTM) and exact solutions are compared. The statistical analysis of performance indicators in terms of mean, median, and standard deviations further establishes the worth of ANN‐GNDO‐SQP algorithm.

Nowadays, R&D Expenditure plays an important role in more and more creative activities of enterprises and other entities, especially in research activities and programs of society. However there still have a big problem that is how to collect and classify R&D Expenditure accurately. In this paper, after analyzing the restrictive collection factors on R&D Expenditure statistically, a practical scheme was provided that including R&D Expenditure Feature Vector and “Object Wood” concept were defined firstly, intelligent receipt recognizing model (IRPM), intelligent receipt persona model (IRRM) based on spatial-temporal representation of multi-factors and R&D expenditure data Twin(REDT) based on data multi relationship were developed creatively. Besides, intelligence carrier-class R&D expenditure management system (REMS) was developed based on above novel technologies and deployed on cloud with SaaS mode. For calling advantageously and updating conveniently, API standard interface and Full Stack Security Mechanism were also improved and used in REMS. Meanwhile, it was proved that REMS had better performance on assisting enterprise in collecting and using their R&D Expenditure after REMS employed by 50 industrial enterprises at first batch in practical over a period of time. There also have better economic benefits and social benefits after REMS was used by 211 enterprises in practically. Next, REMS would be utilized and tested in more scope of important entities so that the correlation technologies could be tested, iterated and optimized forward in the future. Actually, REMS is becoming R&D Expenditure industrial promoted by investor and market. Eventually, REMS would become one of the best R&D Expenditure collecting and using tools, it would not only promote R&D Expenditure increase but also become a industrial correlating with R&D Expenditure.