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

I Gede Agus Widyadana,Alan Darmasaputra Tanudireja,Hui Ming Teng

doi:10.9744/jirae.2.1.8-11

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.

Highlights

Continuous and periodic review inventory policies have been analyzed by many researchers and applied in many organizations
The inventory models were started with Economic Order Quantity (EOQ) by Harris in 1913
An exact optimal solution for a periodic-review inventory model with multiple retailers and stochastic demand was developed by Wang [3]

Summary

Introduction

Continuous and periodic review inventory policies have been analyzed by many researchers and applied in many organizations. Li and Jin [6] applied EOQ in agri-products for stochastic demands They used a system dynamic simulation model to find the optimal lot size and replenishment interval. A single simulation run of inventory process to set a target level required for a given service level was developed by Betts [9] He concluded that the new model is easy to use by inventory managers in practice since the model does not require user modeling of functional form or parameters of the demand distributions. An (s,S) inventory policy with stochastic demand and lead time was developed and solved by Ekren and Ornek [11] using simulation They concluded that they obtained good results within reasonable computation times to solve a complex problem by using simulation. We use a continuous review policy, which is economic order quantity (R,Q), and some periodic review policies (s,S and S,T)

Research Methods

Result and Discussion

Conclusion