Firouzabad Institute of Higher EducationAnnals of Optimization Theory and Practice2588-36664220210731A new integer solution approach for fractional linear programming problem11213397010.22121/aotp.2021.292397.1072ENSrikantGuptaJaipuria Institute of Management, India0000-0002-4158-2866Ather AzizRainaDepartment of Mathematics, Govt. Degree College Darhal, Jammu and Kashmir, IndiaJournal Article20210627In mathematical programming different types of cuts have been developed in the past to get an integer value of the decision variables. In this paper, we have developed a new integer cut for getting an integer solution of the fractional linear programming problem (FLPP). This technique allows the decision-maker to solve the formulated FLPP to be conveniently using the Branch and Bound approach in order to achieve the optimum final solution. The process of development of the integer cut is shown with sufficient detail and a numerical illustration is used for clarification purpose.http://aotp.fabad-ihe.ac.ir/article_133970_792b46078853b9b0c4a727a023112fc2.pdfFirouzabad Institute of Higher EducationAnnals of Optimization Theory and Practice2588-36664220210731Application of the induced generalized averaging hybrid aggregation operators using interval-valued Pythagorean fuzzy environment132913433510.22121/aotp.2021.286814.1067ENKhaistaRahmanShaheed Benazir Bhutto University, PakistanJournal Article20210518Induced aggregation operators are more suitable for aggregating the individual preference relations into a collective fuzzy preference relation. Therefore the focus of our this paper is to develop some induced generalized aggregation operators using interval-valued Pythagorean fuzzy numbers, such as induced generalized interval-valued Pythagorean fuzzy ordered weighted averaging (I-GIVPFOWA) operator, induced generalized interval-valued Pythagorean fuzzy hybrid averaging (I-GIVPFHA) operator. Some desirable properties, such as idempotency, boundedness, and monotonicity corresponding to the proposed operators have been investigated. The main advantage of the proposed operators is that these operators are able to reflect the complex attitudinal character of the decision-maker using order inducing variables and provide much more complete information for decision-making. Furthermore, these operators are applied to decision-making problems in which experts provide their preferences in the Pythagorean fuzzy environment to show the validity, practicality, and effectiveness of the new approach.http://aotp.fabad-ihe.ac.ir/article_134335_d740578f0d2ef6c15caf755d7950798e.pdfFirouzabad Institute of Higher EducationAnnals of Optimization Theory and Practice2588-36664220210731Estimation of electrical energy consumption in Tamil Nadu using univariate time-series analysis313713397110.22121/aotp.2021.292718.1073END.KarthikaDepartment of Mathematics, School of Advanced Sciences and Technology, VIT, Vellore, Tamil Nadu, India0000-0001-5561-0840K.KarthikeyanDepartment of Mathematics, School of Advanced Sciences and Technology, VIT, Vellore, Tamil Nadu, IndiaJournal Article20210629Demand estimation for power utilization might be a key achievement factor for the occasion of any nation. This might be accomplished if the requirement is estimated precisely. In this paper, Distinctive Univariate methods of forecasting Autoregressive Integrated Moving Average (ARIMA), ETS, Holt's model, Holt Winter's Additive model, Simple exponential model was used to figure forecast models of the power consumption in Tamil Nadu. The goal is to look at the presentation of these five methodologies and the experimental information utilized in this investigation was data from previous years for the monthly power consumption in Tamil Nadu from 2012 to 2020. The outcomes show that the ARIMA model diminished the MAPE value to 5.9097%, while those of ETS, Holt's Model, Holt winter's technique, SES is 6.3451%, 9.7708%, 7.3439%, and 9.5305% separately. Depend on the outcomes, we conclude that the ARIMA approach beat the ETS and Holt winter's techniques in this situation.http://aotp.fabad-ihe.ac.ir/article_133971_77911a662000c8e24bfe83aef4831dcb.pdfFirouzabad Institute of Higher EducationAnnals of Optimization Theory and Practice2588-36664220210731Fuzzy logic based controller for optimization of voltage unbalance compensation in an autonomous electric microgrid395413284510.22121/aotp.2021.286982.1068ENAmirKhaledianDepartment of Electrical Engineering, Faculty of Electrical and Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran0000-0003-4998-4920Journal Article20210519Recently, there has been an increasing utilization of distributed generators (DGs) in electric power systems not only to supply the demand power of the grid but also to enhance the power quality. In this paper, a new control scheme is proposed for optimization of voltage unbalance compensation in an autonomous microgrid. Adaptive fuzzy PI controller (AFPIC) is applied to a voltage compensation loop which modifies the droop control based inverter reference voltage. Virtual impedance loop is used to enhance the operation of droop control. Voltage unbalance factor (VUF) is calculated by positive and negative sequence components of the output voltage and compared with the maximum threshold. Second-order generalized integrator (SOGI) is used to extract the voltage positive and negative sequence. In the proposed method, microgrid has the dynamic potential of decreasing the VUF below the standard value in all kinds of load conditions while the conventional methods are designed only for a specific load operating point. Proper voltage regulation is also satisfied by back to back embedded voltage and current controllers. A sample microgrid is analyzed in presence of single phase unbalanced load and Matlab simulation results are given to show the effectiveness of the proposed controller.http://aotp.fabad-ihe.ac.ir/article_132845_c1f2075ebea74a087ca1c953b9a8ce00.pdfFirouzabad Institute of Higher EducationAnnals of Optimization Theory and Practice2588-36664220210731Machine learning approaches to mental stress detection: a review556713397210.22121/aotp.2021.292083.1074ENVishakhaAryaDepartment of Computer Science, School of Computing, DIT University, Dehradun, 248001 IndiaAmit KumarMishraDepartment of Computer Science, School of Computing, DIT University, Dehradun, 248001 IndiaJournal Article20210629Purpose of Review: Machine Learning has shown exponential growth in ingesting a huge <br />amount of data and give accurate outcomes equivalent to the human level. It provides a <br />glance at the future where complex data, analysis and analytical model together help <br />innumerable people suffering from health issues. This paper reviews the current application <br />of ML in the health sector, their limitation, predictive analysis, and areas that are hard-to-diagnose and need advance research.<br />New Findings: We have reviewed 30 papers on mental stress detection using ML that used <br />Social networking sites, student’s record, Questioner technique, clinical dataset, real-time data, Bio-signal technology, wireless device and suicidal tendency. Collectively, these studies show high accuracy and potential of ML algorithms in mental health, and which ML algorithm yields the best result. <br />Summary: With the advancement of ML, it has unfolded many areas like traditional clinical <br />trials which are not sufficient to collect all the information about a person. Currently, define <br />under DSM-V stage to detect these illnesses at the preliminary stage, diagnosing and treating <br />before any mishap. It has re-defined the mental health practicing reducing cost and time, <br />making it easier and convenient for patients to reach better health care whenever they need it.http://aotp.fabad-ihe.ac.ir/article_133972_e76e2b3325f15997830e07bee5314cb5.pdfFirouzabad Institute of Higher EducationAnnals of Optimization Theory and Practice2588-36664220210731On discounted discrete scheduled replacement model698213360510.22121/aotp.2021.283204.1065ENTijjani AWaziriSchool of Continuing Education, Bayero University Kano, Nigeriahttps://orcid.org/00Journal Article20210425An operating unit sometimes cannot be replaced at the exact optimum replacement time for some reasons. The unit may be rather replaced at idle times, such as a day, a week, a month, a year and so on. So to address such problem of replacing a unit at idle times, this paper come up with a discounted discrete scheduled replacement model for a unit. It is assumed that the replacement is at scheduled times NT(N=1,2,3,…) for a fixed T>0, such that the model constructed involves minimal repair and discounting rate (∝ >0). The unit considered in this paper is subjected to three categories of failures, which are Category I, Category II and Category III failures. Category I failure is an un-repairable one, which occurs suddenly. While Category II and Category III failures are both repairable, which occurs due to time and usage, and the two failures are minimally repaired. A numerical example is provided, so as to investigate the characteristics of the model presented and determine the optimal discrete replacement time (N^*) of the unit.http://aotp.fabad-ihe.ac.ir/article_133605_e5d0423ea94c196c8d26e8d72aad1730.pdf