Open AccessA Genetic Algorithm for Economic Order Quantity with a Short Product Life Cycle
Author(s) -
Bing Chang Ouyang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/730/1/012017
Subject(s) - mathematical optimization , constant (computer programming) , genetic algorithm , function (biology) , product (mathematics) , time horizon , fitness function , nonlinear system , order (exchange) , work (physics) , computer science , mathematics , algorithm , economics , engineering , mechanical engineering , physics , geometry , finance , quantum mechanics , evolutionary biology , biology , programming language
Considering a finite time horizon crossing over multiple stages of a product life cycle, this study presents a genetic algorithm to deal with an economic order quantity model with multiple demand rates under a non-periodic policy. In real, the demand of the product life cycle is a non-linear function but we assumes it as four-segment linear or constant approximations in this work. In addition, a multiple-segment to be combined with linear or constant functions can be approximated a nonlinear function. This study does not focus on this, but it provides a genetic algorithm to deal with this proposed inventory problems. The particular of this research is that we develop a proposed replenishment scheme by the differentiating equation of the total cost with respect to replenishment time. Then, calculate the total cost of the proposed scheme as the fitness function to evaluate the populations. In this paper, an explicit procedure to obtain an approximating solution is provided and numerical examples to illustrate the proposed model are shown as well.