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Inventory optimization focuses on stabilizing the stockpile management in the company with the expenditure involved in a successful volatile system. The major challenge is to meet the customer's demand in a well-organized way for profitable management. The nonlinear programming problem (NLP) from fixed order inventory systems, was solved by using the geometric programming method with fuzzification executed by decagonal fuzzy numbers and graded-mean representation as a defuzzification method. In this paper, we had minimized the total cost for an inventory model to obtain the optimal quantity and compared it with the fuzzy inventory model and illustrated with a numerically. [ABSTRACT FROM AUTHOR]

Businesses use inventory modelling to estimate the ideal level of inventories to be kept in a production process. This procedure entails controlling the quantity of goods or raw materials to be stoi-ed. the frequency of orders, and the supply flow. The primary task would be to reduce the overall cost incurred during the production process. This study examines a finite-life inventory model that permits a very small number of de fective products. illustrates the optimal order quantity. and explores how to optimal the second-degree total cost function while validating its optimality with a Hessian matrix. In addition. a numerical analysis is performed, and the findings are described. [ABSTRACT FROM AUTHOR]

Climate change, rainfall, weather forecasting is of great concern during the past two decades as scientists and researchers are cautious in building standard numerical models to simulate and forecast the weather parameters in efficient and reliable way. In India, the monsoon is largely responsible for rainfall. India experiences three distinct seasons throughout the year as a result of the monsoon, which originates from the reversal of the predominant wind direction from Southwest to Northeast. Between June and October, the Southwest monsoon, sometimes known as the "wet" season, brings significant rainfall across the majority of the nation. The focus of this research work is to analyse the data of rainfall existed in the past 100 years (1901–2000) and implementing artificial intelligent methods to frame certain classification of algorithm which can forecast the level of rainfall in the future. Data from 1901–2000 of Chennai district has been taken into account for this research. Statistical evaluations are done based on the database and the tabulated results shows the significance of rainfall. Wavelet analysis of multi resolution criteria is obtained to extract the information of heavy rainfall. Mann Kendall (MK) test statistics is utilized for classifying the rainfall data in four levels viz., very-low, low, moderate, high and very high. Trend analysis for the 17 years is tested using Neuro Fuzzy optimisation algorithm. The efficient training of Neuro fuzzy algorithm forecasts the possible trend using the classification analysis of MK test. [ABSTRACT FROM AUTHOR]

In the field of applied mathematics, optimization techniques formulate to the maximizing and minimizing for an objective function. The purpose of the optimization problems plays a vital role in the field of inventory management. The aim is to minimize the total cost, which comprises many fluctuating costs such as shortage, ordering, and holding cost. In this paper, the defective items were under the classification synchronous and asynchronous under a rework strategy process. The rework strategy is separating and accumulating the imperfect items at the time of completion of the process. This study considered asynchronous defective items and tried to minimize the total cost incurred. The optimality of the non-linear programming was achieved by the Hessian matrix, which results in the minimization of the total cost incurred. Furthermore, the usage of hexagonal fuzzy numbers formulates many real-life problems that arise due to flawed knowledge. There might be several situations in decision-making problems where optimization techniques require six parameters or more. The inclusion of Python coding has further made numerical working simpler. Furthermore, sensitivity analysis is carried out. [ABSTRACT FROM AUTHOR]