Optimal Inventory Policy for Stochastic Demand Using Monte Carlo Simulation and Evolutionary Algorithm
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 stochasti...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Petra Christian University
2017-01-01
|
Series: | JIRAE (International Journal of Industrial Research and Applied Engineering) |
Subjects: | |
Online Access: | http://jirae.petra.ac.id/index.php/jirae/article/view/19329 |
id |
doaj-8ed0d7bffff1443aa1308a02774c82b8 |
---|---|
record_format |
Article |
spelling |
doaj-8ed0d7bffff1443aa1308a02774c82b82020-11-24T21:54:11ZengPetra Christian UniversityJIRAE (International Journal of Industrial Research and Applied Engineering)2407-72592407-72592017-01-0121811Optimal Inventory Policy for Stochastic Demand Using Monte Carlo Simulation and Evolutionary AlgorithmI Gede Agus Widyadana0Alan Darmasaputra Tanudireja1Hui Ming Teng2 Petra Christian University Petra Christian University Chihlee Institute of Technology 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.http://jirae.petra.ac.id/index.php/jirae/article/view/19329Inventory; Stochastic Demand; Monte Carlo Simulation; Evolutionary Algorithm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
I Gede Agus Widyadana Alan Darmasaputra Tanudireja Hui Ming Teng |
spellingShingle |
I Gede Agus Widyadana Alan Darmasaputra Tanudireja Hui Ming Teng Optimal Inventory Policy for Stochastic Demand Using Monte Carlo Simulation and Evolutionary Algorithm JIRAE (International Journal of Industrial Research and Applied Engineering) Inventory; Stochastic Demand; Monte Carlo Simulation; Evolutionary Algorithm |
author_facet |
I Gede Agus Widyadana Alan Darmasaputra Tanudireja Hui Ming Teng |
author_sort |
I Gede Agus Widyadana |
title |
Optimal Inventory Policy for Stochastic Demand Using Monte Carlo Simulation and Evolutionary Algorithm |
title_short |
Optimal Inventory Policy for Stochastic Demand Using Monte Carlo Simulation and Evolutionary Algorithm |
title_full |
Optimal Inventory Policy for Stochastic Demand Using Monte Carlo Simulation and Evolutionary Algorithm |
title_fullStr |
Optimal Inventory Policy for Stochastic Demand Using Monte Carlo Simulation and Evolutionary Algorithm |
title_full_unstemmed |
Optimal Inventory Policy for Stochastic Demand Using Monte Carlo Simulation and Evolutionary Algorithm |
title_sort |
optimal inventory policy for stochastic demand using monte carlo simulation and evolutionary algorithm |
publisher |
Petra Christian University |
series |
JIRAE (International Journal of Industrial Research and Applied Engineering) |
issn |
2407-7259 2407-7259 |
publishDate |
2017-01-01 |
description |
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. |
topic |
Inventory; Stochastic Demand; Monte Carlo Simulation; Evolutionary Algorithm |
url |
http://jirae.petra.ac.id/index.php/jirae/article/view/19329 |
work_keys_str_mv |
AT igedeaguswidyadana optimalinventorypolicyforstochasticdemandusingmontecarlosimulationandevolutionaryalgorithm AT alandarmasaputratanudireja optimalinventorypolicyforstochasticdemandusingmontecarlosimulationandevolutionaryalgorithm AT huimingteng optimalinventorypolicyforstochasticdemandusingmontecarlosimulationandevolutionaryalgorithm |
_version_ |
1725868292021682176 |