A parameter-tuned genetic algorithm to optimize two-echelon continuous review inventory systems

This paper deals with a two-echelon inventory system for a non-repairable item where the system consists of one warehouse and m identical retailers and uses continuous-review (R, Q) ordering policy. To find an effective stocking policy for this system, a mathematical model with the objective of minimizing the total annual inventory investment subject to constraints on the average annual order frequency, expected number of backorders, and budget is formulated. The mathematical model of the problem at hand is shown to be nonlinear integer-programming and hence a parameter-tuned genetic algorithm is proposed to solve it efficiently. A numerical example is provided at the end to illustrate the applicability of the proposed methodology.