Agent-Based Modelling and Heuristic Approach for Solving Complex OEM Flow-Shop Productions under Customer Disruptions

Tunde Victor Adediran, Ammar Al Bazi, Luiz Eduardo dos Santos

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The application of the agent-based simulation approach in the flow-shop production environment has recently gained popularity among researchers. The concept of agent and agent functions can help to automate a variety of difficult tasks and assist decision-making in flow-shop production. This is especially so in the large-scale Original Equipment Manufacturing (OEM) industry, which is associated with many uncertainties. Among these are uncertainties in customer demand requirements that create disruptions that impact production planning and scheduling, hence, making it difficult to satisfy demand in due time, in the right order delivery sequence, and in the right item quantities. It is however important to devise means of adapting to these inevitable disruptive problems by accommodating them while minimising the impact on production performance and customer satisfaction.
In this paper, an innovative embedded agent-based Production Disruption Inventory-Replenishment (PDIR) framework, which includes a novel adaptive heuristic algorithm and inventory replenishment strategy which is proposed to tackle the disruption problems. The capabilities and functionalities of agents are utilised to simulate the flow-shop production environment and aid learning and decision making. In practice, the proposed approach is implemented through a set of experiments conducted as a case study of an automobile parts facility for a real-life large-scale OEM. The results are presented in term of Key Performance Indicators (KPIs), such as the number of late/unsatisfied orders, to determine the effectiveness of the proposed approach. The results reveal a minimum number of late/unsatisfied orders, when compared with other approaches.
Original languageEnglish
Pages (from-to)29-41
Number of pages13
JournalComputers & Industrial Engineering
Volume133
Early online date2 May 2019
DOIs
Publication statusPublished - Jul 2019

Fingerprint

Decision making
Customer satisfaction
Heuristic algorithms
Adaptive algorithms
Automobiles
Scheduling
Planning
Industry
Experiments
Uncertainty

Keywords

  • Agent-based modelling
  • Heuristic algorithm
  • OEM flow-shop
  • Production disruption

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

Cite this

Agent-Based Modelling and Heuristic Approach for Solving Complex OEM Flow-Shop Productions under Customer Disruptions. / Adediran, Tunde Victor; Al Bazi, Ammar; Eduardo dos Santos, Luiz.

In: Computers & Industrial Engineering, Vol. 133, 07.2019, p. 29-41.

Research output: Contribution to journalArticle

@article{c695c2e0fc364070b417069d8f898c9a,
title = "Agent-Based Modelling and Heuristic Approach for Solving Complex OEM Flow-Shop Productions under Customer Disruptions",
abstract = "The application of the agent-based simulation approach in the flow-shop production environment has recently gained popularity among researchers. The concept of agent and agent functions can help to automate a variety of difficult tasks and assist decision-making in flow-shop production. This is especially so in the large-scale Original Equipment Manufacturing (OEM) industry, which is associated with many uncertainties. Among these are uncertainties in customer demand requirements that create disruptions that impact production planning and scheduling, hence, making it difficult to satisfy demand in due time, in the right order delivery sequence, and in the right item quantities. It is however important to devise means of adapting to these inevitable disruptive problems by accommodating them while minimising the impact on production performance and customer satisfaction. In this paper, an innovative embedded agent-based Production Disruption Inventory-Replenishment (PDIR) framework, which includes a novel adaptive heuristic algorithm and inventory replenishment strategy which is proposed to tackle the disruption problems. The capabilities and functionalities of agents are utilised to simulate the flow-shop production environment and aid learning and decision making. In practice, the proposed approach is implemented through a set of experiments conducted as a case study of an automobile parts facility for a real-life large-scale OEM. The results are presented in term of Key Performance Indicators (KPIs), such as the number of late/unsatisfied orders, to determine the effectiveness of the proposed approach. The results reveal a minimum number of late/unsatisfied orders, when compared with other approaches.",
keywords = "Agent-based modelling, Heuristic algorithm, OEM flow-shop, Production disruption",
author = "Adediran, {Tunde Victor} and {Al Bazi}, Ammar and {Eduardo dos Santos}, Luiz",
year = "2019",
month = "7",
doi = "10.1016/j.cie.2019.04.054",
language = "English",
volume = "133",
pages = "29--41",
journal = "Computers & Industrial Engineering",
issn = "0360-8352",
publisher = "Elsevier",

}

TY - JOUR

T1 - Agent-Based Modelling and Heuristic Approach for Solving Complex OEM Flow-Shop Productions under Customer Disruptions

AU - Adediran, Tunde Victor

AU - Al Bazi, Ammar

AU - Eduardo dos Santos, Luiz

PY - 2019/7

Y1 - 2019/7

N2 - The application of the agent-based simulation approach in the flow-shop production environment has recently gained popularity among researchers. The concept of agent and agent functions can help to automate a variety of difficult tasks and assist decision-making in flow-shop production. This is especially so in the large-scale Original Equipment Manufacturing (OEM) industry, which is associated with many uncertainties. Among these are uncertainties in customer demand requirements that create disruptions that impact production planning and scheduling, hence, making it difficult to satisfy demand in due time, in the right order delivery sequence, and in the right item quantities. It is however important to devise means of adapting to these inevitable disruptive problems by accommodating them while minimising the impact on production performance and customer satisfaction. In this paper, an innovative embedded agent-based Production Disruption Inventory-Replenishment (PDIR) framework, which includes a novel adaptive heuristic algorithm and inventory replenishment strategy which is proposed to tackle the disruption problems. The capabilities and functionalities of agents are utilised to simulate the flow-shop production environment and aid learning and decision making. In practice, the proposed approach is implemented through a set of experiments conducted as a case study of an automobile parts facility for a real-life large-scale OEM. The results are presented in term of Key Performance Indicators (KPIs), such as the number of late/unsatisfied orders, to determine the effectiveness of the proposed approach. The results reveal a minimum number of late/unsatisfied orders, when compared with other approaches.

AB - The application of the agent-based simulation approach in the flow-shop production environment has recently gained popularity among researchers. The concept of agent and agent functions can help to automate a variety of difficult tasks and assist decision-making in flow-shop production. This is especially so in the large-scale Original Equipment Manufacturing (OEM) industry, which is associated with many uncertainties. Among these are uncertainties in customer demand requirements that create disruptions that impact production planning and scheduling, hence, making it difficult to satisfy demand in due time, in the right order delivery sequence, and in the right item quantities. It is however important to devise means of adapting to these inevitable disruptive problems by accommodating them while minimising the impact on production performance and customer satisfaction. In this paper, an innovative embedded agent-based Production Disruption Inventory-Replenishment (PDIR) framework, which includes a novel adaptive heuristic algorithm and inventory replenishment strategy which is proposed to tackle the disruption problems. The capabilities and functionalities of agents are utilised to simulate the flow-shop production environment and aid learning and decision making. In practice, the proposed approach is implemented through a set of experiments conducted as a case study of an automobile parts facility for a real-life large-scale OEM. The results are presented in term of Key Performance Indicators (KPIs), such as the number of late/unsatisfied orders, to determine the effectiveness of the proposed approach. The results reveal a minimum number of late/unsatisfied orders, when compared with other approaches.

KW - Agent-based modelling

KW - Heuristic algorithm

KW - OEM flow-shop

KW - Production disruption

UR - http://www.scopus.com/inward/record.url?scp=85065083945&partnerID=8YFLogxK

U2 - 10.1016/j.cie.2019.04.054

DO - 10.1016/j.cie.2019.04.054

M3 - Article

VL - 133

SP - 29

EP - 41

JO - Computers & Industrial Engineering

JF - Computers & Industrial Engineering

SN - 0360-8352

ER -