Performance trajectory-based optimised supply chain dynamics

Kyu Ho Shin, Ick Hyun Kwon, Jung Hoon Lee, Chang Ouk Kim

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

This research presents a multi-objective policy design based on simulating system dynamics, a simulation technique capable of explicitly modelling the feedback loops of decision rules and evaluating the dynamics of complex processes and systems. The novel feature of our approach is that performance is not measured by a single value, but rather performance measures are optimised based on their trajectories, such as the degree of inventory oscillation and the amplification ratio between the order rates of two parties over time (e.g. the bullwhip effect). A multi-objective genetic algorithm termed NSGA-II is employed to generate a set of nondominated solutions. In order to demonstrate the performance of our approach, we adapt and evaluate the dynamic method for a well-known case study: the beer game model of a two-stage supply chain.

Original languageEnglish
Pages (from-to)87-100
Number of pages14
JournalInternational Journal of Computer Integrated Manufacturing
Volume23
Issue number1
DOIs
Publication statusPublished - 2010 Jan 1

Fingerprint

Supply chains
Trajectories
Beer
Amplification
Dynamical systems
Genetic algorithms
Feedback

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

@article{7f2203cea171471fac49f117ec34ff5f,
title = "Performance trajectory-based optimised supply chain dynamics",
abstract = "This research presents a multi-objective policy design based on simulating system dynamics, a simulation technique capable of explicitly modelling the feedback loops of decision rules and evaluating the dynamics of complex processes and systems. The novel feature of our approach is that performance is not measured by a single value, but rather performance measures are optimised based on their trajectories, such as the degree of inventory oscillation and the amplification ratio between the order rates of two parties over time (e.g. the bullwhip effect). A multi-objective genetic algorithm termed NSGA-II is employed to generate a set of nondominated solutions. In order to demonstrate the performance of our approach, we adapt and evaluate the dynamic method for a well-known case study: the beer game model of a two-stage supply chain.",
author = "Shin, {Kyu Ho} and Kwon, {Ick Hyun} and Lee, {Jung Hoon} and Kim, {Chang Ouk}",
year = "2010",
month = "1",
day = "1",
doi = "10.1080/09511920903440305",
language = "English",
volume = "23",
pages = "87--100",
journal = "International Journal of Computer Integrated Manufacturing",
issn = "0951-192X",
publisher = "Taylor and Francis Ltd.",
number = "1",

}

Performance trajectory-based optimised supply chain dynamics. / Shin, Kyu Ho; Kwon, Ick Hyun; Lee, Jung Hoon; Kim, Chang Ouk.

In: International Journal of Computer Integrated Manufacturing, Vol. 23, No. 1, 01.01.2010, p. 87-100.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Performance trajectory-based optimised supply chain dynamics

AU - Shin, Kyu Ho

AU - Kwon, Ick Hyun

AU - Lee, Jung Hoon

AU - Kim, Chang Ouk

PY - 2010/1/1

Y1 - 2010/1/1

N2 - This research presents a multi-objective policy design based on simulating system dynamics, a simulation technique capable of explicitly modelling the feedback loops of decision rules and evaluating the dynamics of complex processes and systems. The novel feature of our approach is that performance is not measured by a single value, but rather performance measures are optimised based on their trajectories, such as the degree of inventory oscillation and the amplification ratio between the order rates of two parties over time (e.g. the bullwhip effect). A multi-objective genetic algorithm termed NSGA-II is employed to generate a set of nondominated solutions. In order to demonstrate the performance of our approach, we adapt and evaluate the dynamic method for a well-known case study: the beer game model of a two-stage supply chain.

AB - This research presents a multi-objective policy design based on simulating system dynamics, a simulation technique capable of explicitly modelling the feedback loops of decision rules and evaluating the dynamics of complex processes and systems. The novel feature of our approach is that performance is not measured by a single value, but rather performance measures are optimised based on their trajectories, such as the degree of inventory oscillation and the amplification ratio between the order rates of two parties over time (e.g. the bullwhip effect). A multi-objective genetic algorithm termed NSGA-II is employed to generate a set of nondominated solutions. In order to demonstrate the performance of our approach, we adapt and evaluate the dynamic method for a well-known case study: the beer game model of a two-stage supply chain.

UR - http://www.scopus.com/inward/record.url?scp=74949106972&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=74949106972&partnerID=8YFLogxK

U2 - 10.1080/09511920903440305

DO - 10.1080/09511920903440305

M3 - Article

AN - SCOPUS:74949106972

VL - 23

SP - 87

EP - 100

JO - International Journal of Computer Integrated Manufacturing

JF - International Journal of Computer Integrated Manufacturing

SN - 0951-192X

IS - 1

ER -