Logo

Optimal Inventory Policy for Stochastic Demand Using Monte Carlo Simulation and Evolutionary Algorithm

Widyadana, I Gede Agus and TANUDIREJA, ALAN DARMASAPUTRA and Teng, Hui-Ming (2017) Optimal Inventory Policy for Stochastic Demand Using Monte Carlo Simulation and Evolutionary Algorithm. International Journal of Industrial Research and Applied Engineering, 2 (1). pp. 8-11. ISSN 2407-7259

[img] PDF
Download (428Kb)

    Abstract

    Research on inventory models has been conducted intensively, including the model for stochastic demand. However, inventory models for stochastic demand are not easy to solve using an exact algorithm. In this paper, we develop a Monte Carlo simulation method to solve inventory problems with stochastic and intermittent demand. Simulation is conducted to evaluate continuous and periodic review policies. The simulation models are optimized using the evolutionary algorithm. The models are applied to data from one bicycle shop in Indonesia for five different items. The result shows that the economic order quantity (R,Q) policy is better than the (s,S) policy for two items and it is better than the (S,T) policy for three items.

    Item Type: Article
    Uncontrolled Keywords: Inventory, Stochastic Demand, Monte Carlo Simulation, Evolutionary Algorithm
    Subjects: H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
    Divisions: Faculty of Industrial Technology > Industrial Engineering Department
    Depositing User: Admin
    Date Deposited: 25 Jul 2017 15:59
    Last Modified: 11 Sep 2017 20:54
    URI: http://repository.petra.ac.id/id/eprint/17661

    Actions (login required)

    View Item