Change search
ReferencesLink to record
Permanent link

Direct link
Analysis of manufacturing supply chains using system dynamics and multi-objective optimization
University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
2013 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Supply chains are in general complex networks composed of autonomous entities whereby multiple performance measures in different levels, which in most cases are in conflict with each other, have to be taken into account. Hence, due to the multiple performance measures, supply chain decision making is much more complex than treating it as a single objective optimization problem. Thus, the aim of the doctoral thesis is to address the supply chain optimization problem within a truly Pareto-based multi-objective context and utilize knowledge extraction techniques to extract valuable and useful information from the Pareto optimal solutions. By knowledge extraction, it means to detect hidden interrelationships between the Pareto solutions, identify common properties and characteristics of the Pareto solutions as well as discover concealed structures in the Pareto optimal data set in order to support managers in their decision making. This aim is addressed through the SBO-framework where the simulation methodology is based on system dynamics (SD) and the optimization utilizes multi-objective optimization (MOO). In order to connect the SD and MOO software, this doctoral thesis introduced a novel SD and MOO interface application which allow the modeling and optimization applications to interact. Additionally, this thesis work also presents a novel SD-MOO methodology that addresses the issue of curse off dimensionality in MOO for higher dimensional problems and with the aim to execute supply chain SD-MOO in a computationally cost efficient way, in terms of convergence, solution intensification and accuracy of obtaining the Pareto-optimal front for complex supply chain problems. In order to detect evident and hidden structures, characteristics and properties of the Pareto-optimal solutions, this work utilizes Parallel Coordinates, Clustering and Innovization, which are three different types of tools for post-optimal analysis and facilitators of discovering and retrieving knowledge from the Pareto-optimal set. The developed SD-MOO interface and methodology are then verified and validated through two academic case studies and a real-world industrial application case study. While not all the insights generated in these application studies can be generalized for other supply-chain systems, the analysis results provide strong indications that the methodology and techniques introduced in this thesis are capable to generate knowledge to support academic SCM research and real-world SCM decision making, which to our knowledge cannot be performed by other methods.

Place, publisher, year, edition, pages
Skövde: University of Skövde , 2013. , 199 p.
Dissertation Series, 2(2013)
Keyword [en]
Supply Chain Management, System Dynamics, Multi-Objective Optimization
National Category
Information Systems
Research subject
URN: urn:nbn:se:his:diva-8661ISBN: 978-91981474-1-4OAI: diva2:668393
Public defence
2013-12-18, Insikten, Portalen, Skövde, 13:00 (English)
Available from: 2013-11-29 Created: 2013-11-29 Last updated: 2013-11-29Bibliographically approved

Open Access in DiVA

Analysis of manufacturing supply chains using system dynamics and multi-objective optimization(13172 kB)3049 downloads
File information
File name FULLTEXT01.pdfFile size 13172 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Aslam, Tehseen
By organisation
School of Technology and SocietyThe Virtual Systems Research Centre
Information Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 3049 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 1657 hits
ReferencesLink to record
Permanent link

Direct link