Situation reactive approach to Vendor Managed Inventory problem

Choonjong Kwak, Jin Sung Choi, Chang Ouk Kim, Ick Hyun Kwon

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

In this research, we deal with VMI (Vendor Managed Inventory) problem where one supplier is responsible for managing a retailer's inventory under unstable customer demand situation. To cope with the nonstationary demand situation, we develop a retrospective action-reward learning model, a kind of reinforcement learning techniques, which is faster in learning than conventional action-reward learning and more suitable to apply to the control domain where rewards for actions vary over time. The learning model enables the inventory control to become situation reactive in the sense that replenishment quantity for the retailer is automatically adjusted at each period by adapting to the change in customer demand. The replenishment quantity is a function of compensation factor that has an effect of increasing or decreasing the replenishment amount. At each replenishment period, a cost-minimizing compensation factor value is chosen in the candidate set. A simulation based experiment gave us encouraging results for the new approach.

Original languageEnglish
Pages (from-to)9039-9045
Number of pages7
JournalExpert Systems with Applications
Volume36
Issue number5
DOIs
Publication statusPublished - 2009 Jul 1

Fingerprint

Inventory control
Reinforcement learning
Costs
Experiments
Compensation and Redress

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

Kwak, Choonjong ; Choi, Jin Sung ; Kim, Chang Ouk ; Kwon, Ick Hyun. / Situation reactive approach to Vendor Managed Inventory problem. In: Expert Systems with Applications. 2009 ; Vol. 36, No. 5. pp. 9039-9045.
@article{57170e21edf74bc39116dd7745d6911d,
title = "Situation reactive approach to Vendor Managed Inventory problem",
abstract = "In this research, we deal with VMI (Vendor Managed Inventory) problem where one supplier is responsible for managing a retailer's inventory under unstable customer demand situation. To cope with the nonstationary demand situation, we develop a retrospective action-reward learning model, a kind of reinforcement learning techniques, which is faster in learning than conventional action-reward learning and more suitable to apply to the control domain where rewards for actions vary over time. The learning model enables the inventory control to become situation reactive in the sense that replenishment quantity for the retailer is automatically adjusted at each period by adapting to the change in customer demand. The replenishment quantity is a function of compensation factor that has an effect of increasing or decreasing the replenishment amount. At each replenishment period, a cost-minimizing compensation factor value is chosen in the candidate set. A simulation based experiment gave us encouraging results for the new approach.",
author = "Choonjong Kwak and Choi, {Jin Sung} and Kim, {Chang Ouk} and Kwon, {Ick Hyun}",
year = "2009",
month = "7",
day = "1",
doi = "10.1016/j.eswa.2008.12.018",
language = "English",
volume = "36",
pages = "9039--9045",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Limited",
number = "5",

}

Situation reactive approach to Vendor Managed Inventory problem. / Kwak, Choonjong; Choi, Jin Sung; Kim, Chang Ouk; Kwon, Ick Hyun.

In: Expert Systems with Applications, Vol. 36, No. 5, 01.07.2009, p. 9039-9045.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Situation reactive approach to Vendor Managed Inventory problem

AU - Kwak, Choonjong

AU - Choi, Jin Sung

AU - Kim, Chang Ouk

AU - Kwon, Ick Hyun

PY - 2009/7/1

Y1 - 2009/7/1

N2 - In this research, we deal with VMI (Vendor Managed Inventory) problem where one supplier is responsible for managing a retailer's inventory under unstable customer demand situation. To cope with the nonstationary demand situation, we develop a retrospective action-reward learning model, a kind of reinforcement learning techniques, which is faster in learning than conventional action-reward learning and more suitable to apply to the control domain where rewards for actions vary over time. The learning model enables the inventory control to become situation reactive in the sense that replenishment quantity for the retailer is automatically adjusted at each period by adapting to the change in customer demand. The replenishment quantity is a function of compensation factor that has an effect of increasing or decreasing the replenishment amount. At each replenishment period, a cost-minimizing compensation factor value is chosen in the candidate set. A simulation based experiment gave us encouraging results for the new approach.

AB - In this research, we deal with VMI (Vendor Managed Inventory) problem where one supplier is responsible for managing a retailer's inventory under unstable customer demand situation. To cope with the nonstationary demand situation, we develop a retrospective action-reward learning model, a kind of reinforcement learning techniques, which is faster in learning than conventional action-reward learning and more suitable to apply to the control domain where rewards for actions vary over time. The learning model enables the inventory control to become situation reactive in the sense that replenishment quantity for the retailer is automatically adjusted at each period by adapting to the change in customer demand. The replenishment quantity is a function of compensation factor that has an effect of increasing or decreasing the replenishment amount. At each replenishment period, a cost-minimizing compensation factor value is chosen in the candidate set. A simulation based experiment gave us encouraging results for the new approach.

UR - http://www.scopus.com/inward/record.url?scp=60849094048&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=60849094048&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2008.12.018

DO - 10.1016/j.eswa.2008.12.018

M3 - Article

AN - SCOPUS:60849094048

VL - 36

SP - 9039

EP - 9045

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 5

ER -