Using A Genetic Algorithm to Solve the Inventory Model with A Practical Application
Abstract
The research aims to shed light on the Genetic Algorithm to improve traditional solutions and find the best policy for the problem of inventory with continuous review by finding the optimal reorder point and the optimal economic quantity to avoid the risk of stock out (shortage), reduce total costs, and reach the optimal solution with little time and effort. The research was conducted in the stores of the Department of Pharmacy of the Ninawa Health Department for the period from January 1, 2021, to January 1, 2023, on two types of diabetes drugs. An analysis of the demand data for the study sample was conducted using the statistical program (SPSS Statistics Version 22) to determine the type of inventory. It was found that the kind of inventory is Deterministic. On this basis, the mathematical model of the study problem was built, and given the complexity of the traditional solution steps, the solution steps were applied to the program (Winqsb.V2) and the model solution was reached. Additionally, the solution steps were programmed for the genetic algorithm and applied using the (R programming language (. The results of the genetic algorithm showed an improvement in the traditional solution results by reducing total costs. Based on the results of the genetic algorithm, the safety stock, reorder period, and safety period were found.
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