scholarly journals FEATURES OF FINDING OPTIMAL SOLUTIONS IN NETWORK PLANNING

2020 ◽  
pp. 82-96
Author(s):  
Olena Domina
Keyword(s):  
Objective Function ◽  
Response Surface ◽  
Canonical Transformation ◽  
Network Planning ◽  
Experiment Planning ◽  
Optimal Solutions ◽  
Network Diagram ◽  
The Difference ◽  
Input Variables ◽  
Network Diagrams

The object of research is a test network diagram, in relation to which the task of minimizing the objective function qmax/qmin→min is posed, which requires maximizing the uniformity of the workload of personnel when implementing an arbitrary project using network planning. The formulation of the optimization problem, therefore, assumed finding such times of the beginning of the execution of operations, taken as input variables, in order to ensure the minimum value of the ratio of the peak workload of personnel to the minimum workload. The procedure for studying the response surface proposed in the framework of RSM is described in relation to the problem of optimizing network diagrams. A feature of this procedure is the study of the response surface by a combination of two methods – canonical transformation and ridge analysis. This combination of methods for studying the response surface allows to see the difference between optimal solutions in the sense of "extreme" and in the sense of "best". For the considered test network diagram, the results of the canonical transformation showed the position on the response surface of the extrema in the form of maxima, which is unacceptable for the chosen criterion for minimizing the objective function qmax/qmin→min. It is shown that the direction of movement towards the best solutions with respect to minimizing the value of the objective function is determined on the basis of a parametric description of the objective function and the restrictions imposed by the experiment planning area. A procedure for constructing nomograms of optimal solutions is proposed, which allows, after its implementation, to purposefully choose the best solutions based on the real network diagrams of your project

scholarly journals FEATURES OF FINDING OPTIMAL SOLUTIONS IN NETWORK PLANNING

2020 ◽  
pp. 82-96
Author(s):  
Olena Domina
Keyword(s):  
Objective Function ◽  
Response Surface ◽  
Canonical Transformation ◽  
Network Planning ◽  
Experiment Planning ◽  
Optimal Solutions ◽  
Network Diagram ◽  
The Difference ◽  
Input Variables ◽  
Network Diagrams

The object of research is a test network diagram, in relation to which the task of minimizing the objective function qmax/qmin→min is posed, which requires maximizing the uniformity of the workload of personnel when implementing an arbitrary project using network planning. The formulation of the optimization problem, therefore, assumed finding such times of the beginning of the execution of operations, taken as input variables, in order to ensure the minimum value of the ratio of the peak workload of personnel to the minimum workload. The procedure for studying the response surface proposed in the framework of RSM is described in relation to the problem of optimizing network diagrams. A feature of this procedure is the study of the response surface by a combination of two methods – canonical transformation and ridge analysis. This combination of methods for studying the response surface allows to see the difference between optimal solutions in the sense of "extreme" and in the sense of "best". For the considered test network diagram, the results of the canonical transformation showed the position on the response surface of the extrema in the form of maxima, which is unacceptable for the chosen criterion for minimizing the objective function qmax/qmin→min. It is shown that the direction of movement towards the best solutions with respect to minimizing the value of the objective function is determined on the basis of a parametric description of the objective function and the restrictions imposed by the experiment planning area. A procedure for constructing nomograms of optimal solutions is proposed, which allows, after its implementation, to purposefully choose the best solutions based on the real network diagrams of your project

Optimal Design of Rack-and-Pinion Steering Linkages

1983 ◽  
Vol 105 (2) ◽  
pp. 220-226 ◽  
Author(s):  
C. E. Zarak ◽  
M. A. Townsend
Keyword(s):  
Mathematical Model ◽  
Optimal Design ◽  
Objective Function ◽  
Full Range ◽  
Mathematical Representation ◽  
Optimal Solutions ◽  
Steering Angle ◽  
Design Synthesis ◽  
Constraint Set ◽  
The Difference

A mathematical model is developed for the design synthesis of rack-and-pinion steering linkages. The general objective is to minimize the difference between the steering centers over the full range of steering angle inputs while fitting into a reasonable space. Because there is a substantial amount of design art in these systems and the mathematical representation is not clear, the model, constraints, and objective actually “evolve” to the eventual desired form. The problem has multiple optima, and practical and heuristic considerations are used to choose suboptimal but more realistic solutions, once satisfactory optimal solutions are identified. These involve manipulation of the objective function, constraint set, and intitial guesses. Both leading and trailing link designs are considered, the former being slightly better. Limitations of the model are also presented.

scholarly journals Algorithm for create network diagrams for planning maintenance of agricultural machines

2021 ◽  
Vol 285 ◽  
pp. 07020
Author(s):  
Anton Popov ◽  
Vladimir Lukhanin
Keyword(s):  
Critical Path ◽  
Network Planning ◽  
Network Models ◽  
Working Hours ◽  
Network Diagram ◽  
Time To Build ◽  
Long Time ◽  
Matlab Programming ◽  
Agricultural Machines ◽  
Network Diagrams

Reducing the maintenance man-hour is one of the ways to improve of operation efficiency of agricultural machines. The total maintenance time of the agricultural machine is determined by the critical path and other network planning parameters. The network model in the form of network diagrams is used for visualization and analysis of maintenance processes. Building network diagrams be not a trivial task and it takes a long time to build. Therefore, automating the creation of network diagrams of technological maintenance of agricultural machines is an urgent task. This work aims to develop an algorithm for creating and analyzing network diagrams for planning the processes of technological maintenance and repair of various agricultural machines. When building an algorithm for creating network diagrams, the methodological apparatus of network planning and basic algorithmic structures were used. The algorithm is realized in the Matlab programming language. The article presents the flowchart and a description of the formation of an array of all and full paths. The description of identifying fictitious operations in the network diagram is given. The present algorithm calculates the maintenance man-hour of paths, determines the critical path of the network diagram and visualizes it on the graph, and also calculates the time reserves of the specified work. The logic of the algorithm is developed in accordance with the rules for building network models and excludes dead-end and tail events, closed loops. Thus, the algorithm will increase the efficiency of solving practical tasks of network planning of maintenance of agricultural machinery, shorten working hours and time for the building of the network diagram and reduce the associated possible errors.

Demulsification of a Water-in-crude Oil Emulsion with a Corn Oil BasedDemulsifier using the Response Surface Methodology: Modelling and Optimization

2021 ◽  
Vol 14 ◽  
Author(s):  
Abed Saad ◽  
Nour Abdurahman ◽  
Rosli Mohd Yunus
Keyword(s):  
Response Surface Methodology ◽  
Crude Oil ◽  
Water Content ◽  
Response Surface ◽  
Separation Efficiency ◽  
Corn Oil ◽  
Oil Emulsion ◽  
Optimal Values ◽  
Input Variables ◽  
Oil Emulsions

: In this study, the Sany-glass test was used to evaluate the performance of a new surfactant prepared from corn oil as a demulsifier for crude oil emulsions. Central composite design (CCD), based on the response surface methodology (RSM), was used to investigate the effect of four variables, including demulsifier dosage, water content, temperature, and pH, on the efficiency of water removal from the emulsion. As well, analysis of variance was applied to examine the precision of the CCD mathematical model. The results indicate that demulsifier dose and emulsion pH are two significant parameters determining demulsification. The maximum separation efficiency of 96% was attained at an alkaline pH and with 3500 ppm demulsifier. According to the RSM analysis, the optimal values for the input variables are 40% water content, 3500 ppm demulsifier, 60 °C, and pH 8.

scholarly journals Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

Membranes ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 70
Author(s):  
Jasir Jawad ◽  
Alaa H. Hawari ◽  
Syed Javaid Zaidi
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Forward Osmosis ◽  
Feed Solution ◽  
Operating Conditions ◽  
Ann Model ◽  
Membrane Flux ◽  
Input Variables ◽  
Rsm Model

The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

scholarly journals Machine Learning Model of Dimensionless Numbers to Predict Flow Patterns and Droplet Characteristics for Two-Phase Digital Flows

2021 ◽  
Vol 11 (9) ◽  
pp. 4251
Author(s):  
Jinsong Zhang ◽  
Shuai Zhang ◽  
Jianhua Zhang ◽  
Zhiliang Wang
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Digital Microfluidics ◽  
Flow Patterns ◽  
Machine Learning Algorithms ◽  
Dimensionless Numbers ◽  
Two Phase ◽  
The Difference ◽  
Input Variables ◽  
Digital Microfluidic

In the digital microfluidic experiments, the droplet characteristics and flow patterns are generally identified and predicted by the empirical methods, which are difficult to process a large amount of data mining. In addition, due to the existence of inevitable human invention, the inconsistent judgment standards make the comparison between different experiments cumbersome and almost impossible. In this paper, we tried to use machine learning to build algorithms that could automatically identify, judge, and predict flow patterns and droplet characteristics, so that the empirical judgment was transferred to be an intelligent process. The difference on the usual machine learning algorithms, a generalized variable system was introduced to describe the different geometry configurations of the digital microfluidics. Specifically, Buckingham’s theorem had been adopted to obtain multiple groups of dimensionless numbers as the input variables of machine learning algorithms. Through the verification of the algorithms, the SVM and BPNN algorithms had classified and predicted the different flow patterns and droplet characteristics (the length and frequency) successfully. By comparing with the primitive parameters system, the dimensionless numbers system was superior in the predictive capability. The traditional dimensionless numbers selected for the machine learning algorithms should have physical meanings strongly rather than mathematical meanings. The machine learning algorithms applying the dimensionless numbers had declined the dimensionality of the system and the amount of computation and not lose the information of primitive parameters.

ANN-Based Structure Optimization with Fatigue Reliability Constrains

2012 ◽  
Vol 204-208 ◽  
pp. 3128-3131
Author(s):  
Li Rong Sha ◽  
Yue Yang
Keyword(s):  
Structural Optimization ◽  
Objective Function ◽  
Reliability Analysis ◽  
Response Surface ◽  
Minimum Weight ◽  
Structure Optimization ◽  
Fatigue Reliability ◽  
Ann Model ◽  
Surface Method ◽  
The Relationship

The ANN-based optimization for considering fatigue reliability requirements in structural optimization was proposed. The ANN-based response surface method was employed for performing fatigue reliability analysis. The fatigue reliability requirements were considered as constraints while the weight as the objective function, the ANN model was adopted to establish the relationship between the fatigue reliability and geometry dimension of the structure, the optimal results of the structure with a minimum weight was reached.

An Efficient Response Surface Method Using Givens Transformation for Structural Reliability Analysis

2012 ◽  
Vol 532-533 ◽  
pp. 408-411
Author(s):  
Wei Tao Zhao ◽  
Yi Yang ◽  
Tian Jun Yu
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Structural Reliability ◽  
Limit State ◽  
State Function ◽  
Limit State Function ◽  
Surface Method ◽  
Mathematical Techniques ◽  
Input Variables ◽  
Improved Accuracy

The response surface method was proposed as a collection of statistical and mathematical techniques that are useful for modeling and analyzing a system which is influenced by several input variables. This method gives an explicit approximation of the implicit limit state function of the structure through a number of deterministic structural analyses. However, the position of the experimental points is very important to improve the accuracy of the evaluation of failure probability. In the paper, the experimental points are obtained by using Givens transformation in such way these experimental points nearly close to limit state function. A Numerical example is presented to demonstrate the improved accuracy and computational efficiency of the proposed method compared to the classical response surface method. As seen from the result of the example, the proposed method leads to a better approximation of the limit state function over a large region of the design space, and the number of experimental points using the proposed method is less than that of classical response surface method.

scholarly journals Effects of Ultrasonic-Microwave-Assisted Technology on Hordein Extraction from Barley and Optimization of Process Parameters Using Response Surface Methodology

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Xiao Guan ◽  
Lv Li ◽  
Jing Liu ◽  
Sen Li
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Microwave Power ◽  
Process Intensification ◽  
Extraction Yield ◽  
Extraction Time ◽  
Quadratic Model ◽  
Ultrasonic Power ◽  
Microwave Assisted ◽  
The Difference

We investigated the process intensification of ultrasonic-microwave-assisted technology for hordein extraction from barley. Response surface methodology was utilized to optimize the extraction conditions and to analyze the interaction between four selected variables: temperature, microwave power, ultrasonic power, and extraction time. The validated extraction yield of hordein reached 8.84% at 78°C, microwave power 298 W, and ultrasonic power 690 W after 20 min as optimum conditions. Compared with traditional water-bath extraction (4.7%), the ultrasonic-microwave-assisted technology effectively increased the hordein extraction yield and shortened the extraction time. According to the obtained quadratic model (R2 = 0.9457), ultrasonic power and extraction time were the first two significant factors. However, temperature limited the effects of other factors during extraction. SDS-PAGE and scanning electron microscopy were used to identify the hordein extract and to clarify the difference between the two hordein fractions extracted with new and traditional methods, respectively. Ultrasonic-microwave-assisted technology provided a new way to improve hordein extraction yield from barley and could be a good candidate for industrial application of process intensification.

scholarly journals Table cartogram generation as an optimization problem

2019 ◽  
Vol 1 ◽  
pp. 1-2 ◽  
Author(s):  
Mao Li ◽  
Ryo Inoue
Keyword(s):  
Objective Function ◽  
Least Squares ◽  
Optimization Problem ◽  
Data Representation ◽  
Linear Least Squares ◽  
Original Table ◽  
Outer Border ◽  
Limited Degree ◽  
The Difference ◽  
Operation Results

<p><strong>Abstract.</strong> A table cartogram, visualization of table-form data, is a rectangle-shaped table in which each cell is transformed to express the magnitude of positive weight by its area while maintaining the adjacency relationship of cells in the original table. Winter (2011) applies an area cartogram generation method of Gastner and Newman (2004) for their generation, and Evans et al. (2018) proposes a new geometric procedure. The rows and columns on a table cartogram should be easily recognized by readers, however, no methods have focused to enhance the readability. This study proposes a method that defines table cartogram generation as an optimization problem and attempts to minimize vertical and horizontal deformation. Since the original tables are comprised of regular quadrangles, this study uses quadrangles to express cells in a table cartogram and fixes the outer border to attempt to retain the shape of a standard table.</p><p>This study proposes a two-step approach for table cartogram generation with cells that begin as squares and with fixed outer table borders. The first step only adjusts the vertical and horizontal borders of cells to express weights to the greatest possible degree. All cells maintain their rectangular shape after this step, although the limited degree of freedom of this operation results in low data representation accuracy. The second step adapts the cells of the low-accuracy table cartogram to accurately fit area to weight by relaxing the constraints on the directions of borders of cells. This study utilizes an area cartogram generation method proposed by Inoue and Shimizu (2006), which defines area cartogram generation as an optimization problem. The formulation with vertex coordinate parameters consists of an objective function that minimizes the difference between the given data and size of each cell, and a regularization term that controls the changes of bearing angles. It is formulated as non-linear least squares, and is solved through the iteration of linear least squares by linearizing the problem at the coordinates of vertices and updating the estimated coordinates until the value of the objective function becomes small enough.</p>

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