Detecting attractors in production systems by using system dynamics and neural networks

2002 ◽  
Author(s):  
D. Thiel
Keyword(s):  
Neural Networks ◽  
System Dynamics ◽  
Production Systems

System dynamics modelling of fixed and dynamic Kanban controlled production systems: a supply chain perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jagan Mohan Reddy K. ◽  
Neelakanteswara Rao A. ◽  
Krishnanand Lanka ◽  
PRC Gopal
Keyword(s):  
Supply Chain ◽  
System Dynamics ◽  
Cycle Time ◽  
Production System ◽  
Demand Uncertainty ◽  
Production Systems ◽  
Decision Variable ◽  
Content Type ◽  
Pull Production ◽  
Kanban System

Purpose Pull production systems have received much attention in the supply chain management environment. The number of Kanbans is a key decision variable in the pull production system as it affects the finished goods inventory (FGI) and backorders of the system. The purpose of this study is to compare the performance of the fixed and dynamic Kanban systems in terms of operational metrics (FGI and backorders) under the demand uncertainty. Design/methodology/approach In this paper, the system dynamics (SD) approach was used to model the performance of fixed and dynamic Kanban based production systems. SD approach has enabled the feedback mechanism and is an appropriate tool to incorporate the dynamic control during the simulation. Initially, a simple Kanban based production system was developed and then compared the performance of production systems with fixed and dynamic controlled Kanbans at the various demand scenarios. Findings From the present study, it is observed that the dynamic Kanban system has advantages over the fixed Kanban system and also observed that the variation in the backorders with respect to the demand uncertainty under the dynamic Kanban system is negligible. Research limitations/implications In a just-in-time production system, the number of Kanbans is a key decision variable. The number of Kanbans is mainly depended on the demand, cycle time, safety stock factor (SSF) and container size. However, this study considered only demand uncertainty to compare the fixed and dynamic Kanban systems. This paper further recommends researchers to consider other control variables which may influence the number of Kanbans such as cycle time, SSF and container size. Originality/value This study will be useful to decision-makers and production managers in the selection of the Kanban systems in uncertain demand applications.

Towards a Methodology for Monitoring and Analyzing the Supply Chain Behavior

2011 ◽  
pp. 186-208
Author(s):  
Reinaldo Moraga ◽  
Luis Rabelo ◽  
Alfonso Sarmiento
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Supply Chain ◽  
System Dynamics ◽  
Eigenvalue Analysis ◽  
Background Information ◽  
Short And Long Term ◽  
And Performance

In this chapter, the authors present general steps towards a methodology that contributes to the advancement of prediction and mitigation of undesirable supply chain behavior within short- and long- term horizons by promoting a better understanding of the structure that determines the behavior modes. Through the integration of tools such as system dynamics, neural networks, eigenvalue analysis, and sensitivity analysis, this methodology (1) captures the dynamics of the supply chain, (2) detects changes and predicts the behavior based on these changes, and (3) defines needed modifications to mitigate the unwanted behaviors and performance. In the following sections, some background information is given from literature, the general steps of the proposed methodology are discussed, and finally a case study is briefly summarized.

Optical production systems using neural networks and symbolic substitution

1988 ◽  
Vol 27 (24) ◽  
pp. 5185 ◽  
Author(s):  
Elizabeth Botha ◽  
David Casasent ◽  
Etienne Barnard
Keyword(s):  
Neural Networks ◽  
Production Systems ◽  
Optical Production ◽  
Symbolic Substitution

scholarly journals Exploring agricultural production systems and their fundamental components with system dynamics modelling

2016 ◽  
Vol 333 ◽  
pp. 51-65 ◽  
Author(s):  
Jeffrey P. Walters ◽  
David W. Archer ◽  
Gretchen F. Sassenrath ◽  
John R. Hendrickson ◽  
Jon D. Hanson ◽  
...  
Keyword(s):  
System Dynamics ◽  
Agricultural Production ◽  
Production Systems ◽  
System Dynamics Modelling ◽  
Dynamics Modelling ◽  
Agricultural Production Systems

Identification and Control of a Nonlinear System using Neural Networks by Extracting the System Dynamics

2007 ◽  
Vol 53 (1) ◽  
pp. 43-50 ◽  
Author(s):  
Madhusudan Singh ◽  
Smriti Srivastava ◽  
J R P Gupta ◽  
M Handmandlu
Keyword(s):  
Neural Networks ◽  
Nonlinear System ◽  
System Dynamics ◽  
And Control

Artificial Neural Networks for the analysis of spread⿿mooring configurations for floating production systems

2016 ◽  
Vol 59 ◽  
pp. 254-264 ◽  
Author(s):  
Aline Aparecida de Pina ◽  
Bruno da Fonseca Monteiro ◽  
Carl Horst Albrecht ◽  
Beatriz Souza Leite Pires de Lima ◽  
Breno Pinheiro Jacob
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Production Systems ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document