scholarly journals Solution of the compromise optimization problem of network graphics on the criteria of uniform personnel loading and distribution of funds

2021 ◽  
Vol 1 (4(57)) ◽  
pp. 14-21
Author(s):  
Olena Domina
Keyword(s):  
Optimization Problem ◽  
Mathematical Planning ◽  
One Dimensional ◽  
Model Network ◽  
All Solutions ◽  
Network Schedule ◽  
Optimal Values ◽  
Input Variables ◽  
Point Of Intersection ◽  
Compromise Optimization

The object of research is a model network schedule for performing a complex of operations. One of the most problematic areas is the lack of a unified procedure that allows finding a solution to the problem of compromise optimization, for which the optimization criteria can have a different nature of the influence of input variables on them. In this study, such criteria are the criteria for the uniformity of the workload of personnel and the distribution of funds. Two alternative cases are considered: with monthly planning and with quarterly planning of allocation of funds and staff load. The methods of mathematical planning of the experiment and the ridge analysis of the response surface are used. The peculiarities of the proposed procedure for solving the problem of compromise optimization are its versatility and the possibility of visualization in one-dimensional form – the dependence of each of the alternative criteria on one parameter describing the constraints. The solution itself is found as the point of intersection of equally labeled ridge lines, which are curves that describe the locally optimal values of the output variables. The proposed procedure, despite the fact that it is performed only on a model network diagram, can be used to solve the trade-off optimization problem on arbitrary network graphs. This is due to the fact that the combination of locally optimal solutions in a parametric form on one graph allows visualizing all solutions to the problem. The results obtained at the same time make it possible to select early dates for the start of operations in such a way that, as much as possible, take into account possible difficulties due to the formation of bottlenecks at certain stages of the project. The latter may be due to the fact that for the timely execution of some operation, it may be necessary to combine two criteria, despite the fact that the possible costs may turn out to be more calculated and estimated as optimal.

scholarly journals UNAMBIGUOUSNESS-AMBIGUITY OF PARAMETRIC OPTIMIZATION OF MOTOR-CAR SYSTEMS IN THE CONDITIONS OF CRITERIAL UNCERTAINTY

2020 ◽  
Vol 21 ◽  
pp. 5-23
Author(s):  
P. Hashchuk
Keyword(s):  
Fair Trade ◽  
Optimization Problem ◽  
Parametric Optimization ◽  
Arithmetic Mean ◽  
Trade Off ◽  
Step Process ◽  
One Dimensional ◽  
Real Process ◽  
All Solutions ◽  
The Ideal

Annotation. The general methodology of parametric optimization of systems is considered for two arbitrary cri-teria simultaneously. The so-called principle of expanding an optimization problem is proposed, which creates the basis for finding guaranteed unambiguous solutions, without resorting to artificial formal means of «collapse» of the two cri-teria into one. It turns out that a very common multiplicative criterion for so-called fair trade-off actually expresses the average geometric basic criteria. It is easy to reduce (lead down) it to additive. Therefore, it is certainly not known, why he should give preference to the arithmetic mean (after the appropriate coordinate) of the dimensions of the primary criteria. There are more subjective and far-fetched than objective and truthful in the criterion of a fair compromise.Perfection is a permanent process — it has a beginning but has no end. In that the new" perfections arise from time to time and each of them definitely use a certain time, then, of course, the process of perfection is a step-by-step process, an endless step to an unattainable ideal. This particular circumstance should be taken into account.Described algorithms for optimal search formally reproduce on a primitive model plane the real process of step-by-step improvement of all man-made - from acceptable to better... There are no examples when something was created immediately unconditionally optimally (and the ideal — at all not recognizable and therefore not embodied). At each step, one of the algorithms regulates minimizing the value of a single criterion, without affecting it, without changing the other. That is why there are no conflicts outside the attractor. Only within the attractor, for which the line (which is a one-dimensional attractor) rules on the model plane, the consistency disappears. Another algorithm combines a series of steps in each of which only one parameter varies, and the gain at the same time has both supporters of one perfection, and supporters of some other perfection. Consequently, there are no conflicts, until the algorithm does not attract the attractor, which this time is an area on a model plane, that is a two-dimensional attractor.Within the attractor, all solutions to the optimization problem is appropriate without a doubt, even advisable to consider completely equivalent. However, in fact, insurmountable subjectivism does not allow us to adhere to this idea (let's say, without the participation of any dictator).

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.

Network Centric Warfare as Complex Optimization: An Evolutionary Approach

2016 ◽  
Vol 15 (2) ◽  
pp. 93-105
Author(s):  
Zoltán Jobbágy
Keyword(s):  
Optimization Problem ◽  
Fitness Landscape ◽  
Balance Theory ◽  
Military Operations ◽  
Shifting Balance Theory ◽  
Number Of Genes ◽  
Shifting Balance ◽  
Complex Optimization ◽  
Optimal Values ◽  
Different Characteristics

Military operations are very complex undertakings. However, complexity is not a feature unique to military operations. When biologists wanted to understand the properties of gene mutation they also faced complexity. Confronted by a large number of genes featuring different characteristics, a difficult-to-decode interac- tion among those genes, and an environment that could not be excluded as a factor, Sewell Wright introduced the shifting balance theory, also known as the theory of the fitness landscape. The theory allows complexity to be seen as a process that rests on adaptation and mutation. These two processes are also central to military operations as it is imperative to offset the changing conditions coming both from the environment and the interaction with the enemy. In the article the author uses Wright’s theory to help see military operations as a complex optimization problem that includes approximations and estimations regarding optimal values.

One-Dimensional Optimization Problem

2020 ◽  
pp. 1-14
Author(s):  
Nita H. Shah ◽  
Poonam Prakash Mishra
Keyword(s):  
Optimization Problem ◽  
One Dimensional ◽  
Dimensional Optimization

Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models

2015 ◽  
pp. 406-434 ◽  
Author(s):  
Goran Klepac
Keyword(s):  
Particle Swarm Optimization ◽  
Portfolio Management ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Optimal Values ◽  
Input Variables ◽  
Predictive Data Mining ◽  
Customer Profiles

This chapter introduces the methodology of particle swarm optimization algorithm usage as a tool for finding customer profiles based on a previously developed predictive model that predicts events like selection of some products or services with some probabilities. Particle swarm optimization algorithm is used as a tool that finds optimal values of input variables within developed predictive models as referent values for maximization value of probability that customers select/buy a product or service. Recognized results are used as a base for finding similar profiles between customers. The presented methodology has practical value for decision support in business, where information about customer profiles are valuable information for campaign planning and customer portfolio management.

scholarly journals Heat transfer from convecting-radiating fin through optimized Chebyshev polynomials with interior point algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 102-110
Author(s):  
Elyas Shivanian ◽  
Mahdi Keshtkar ◽  
Hamidreza Navidi
Keyword(s):  
Heat Transfer ◽  
Chebyshev Polynomials ◽  
Interior Point ◽  
Interior Point Method ◽  
Optimization Problem ◽  
Mathematical Formulation ◽  
Interior Point Algorithm ◽  
Equivalent Problem ◽  
One Dimensional ◽  
Fin Efficiency

AbstractIn this paper, the problem of determining heat transfer from convecting-radiating fin of triangular and concave parabolic shapes is investigated.We consider one-dimensional, steady conduction in the fin and neglect radiative exchange between adjacent fins and between the fin and its primary surface. A novel intelligent computational approach is developed for searching the solution. In order to achieve this aim, the governing equation is transformed into an equivalent problem whose boundary conditions are such that they are convenient to apply reformed version of Chebyshev polynomials of the first kind. These Chebyshev polynomials based functions construct approximate series solution with unknown weights. The mathematical formulation of optimization problem consists of an unsupervised error which is minimized by tuning weights via interior point method. The trial approximate solution is validated by imposing tolerance constrained into optimization problem. Additionally, heat transfer rate and the fin efficiency are reported.

Automating statistical diagrammatic representations with data characterization

2017 ◽  
Vol 17 (4) ◽  
pp. 316-334
Author(s):  
Pere Millán-Martínez ◽  
Pedro Valero-Mora
Keyword(s):  
Graphical Representation ◽  
Open Data ◽  
Graphical Representations ◽  
One Dimensional ◽  
The Public ◽  
Cognitive Studies ◽  
Multidimensional Databases ◽  
Diagrammatic Representations ◽  
Input Variables

The search for an efficient method to enhance data cognition is especially important when managing data from multidimensional databases. Open data policies have dramatically increased not only the volume of data available to the public, but also the need to automate the translation of data into efficient graphical representations. Graphic automation involves producing an algorithm that necessarily contains inputs derived from the type of data. A set of rules are then applied to combine the input variables and produce a graphical representation. Automated systems, however, fail to provide an efficient graphical representation because they only consider either a one-dimensional characterization of variables, which leads to an overwhelmingly large number of available solutions, a compositional algebra that leads to a single solution, or requires the user to predetermine the graphical representation. Therefore, we propose a multidimensional characterization of statistical variables that when complemented with a catalog of graphical representations that match any single combination, presents the user with a more specific set of suitable graphical representations to choose from. Cognitive studies can then determine the most efficient perceptual procedures to further shorten the path to the most efficient graphical representations. The examples used herein are limited to graphical representations with three variables given that the number of combinations increases drastically as the number of selected variables increases.

Optimal Decentralized Supplementary Inverter Control Loop to Mitigate Instability in an Islanded Microgrid with Active and Passive Loads

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
P. E. S. N. Raju ◽  
Trapti Jain
Keyword(s):  
Distributed Generation ◽  
Optimization Problem ◽  
Linear Quadratic Regulator ◽  
Control Loop ◽  
Linear Quadratic ◽  
Nsga Ii ◽  
Control Loops ◽  
Degree Of Stability ◽  
Optimal Values ◽  
And Control

Abstract: The growing penetration levels of passive and rectifier interfaced active loads can cause instability in an islanded inverter based microgrid. This may be due to the dynamics associated with passive loads and interaction between the control loops of active loads and inverter-interfaced DGs. This paper proposes a decentralized supplementary control loop around each inverter-interfaced distributed generation (DG) unit to mitigate the instability caused by both the active as well as passive loads. The proposed control loop is based on a robust linear quadratic regulator with prescribed degree-of-stability (LQRPDS). In order to obtain the optimal values of the state and control weighting matrices of LQRPDS, a bi-objective optimization problem has been formulated and solved using a fast and elitist multi-objective non-dominated sorting genetic algorithm (NSGA-II). The proposed decentralized supplementary control loop does not interfere with the steady state performance and provides robust control performance under various load configurations as well as step load disturbances. Eigenvalue analysis and time-domain simulations have been used to validate the effectiveness of the proposed decentralized supplementary control loop.

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