Developing and employing effective design methodologies can significantly improve the economic and environmental viability of renewable production processes. This study contributes by presenting a novel bi-level decision support system (DSS) to aid modelling and optimization of multi technology, multi product supply chains and co-modal transportation networks for biomass based (bio-based) production combining two multi-objective mathematical models. Considering the supply chain configuration optimized by the first level of the DSS, in the second level, the transportation network is designed specifying the most appropriate transportation mode and related transportation option under transfer station availability limitations. A hybrid solution methodology that integrates fuzzy set theory and ε-constraint method is proposed. This methodology handles the system specific uncertainties addressing the economic and environmental sustainability aspects by capturing trade-offs between conflicting objectives in the same framework. To explore the viability of the proposed models and solution methodology, a regional supply chain and transportation network is designed using the entire West Midlands (WM) region of the UK as a testing ground. Additionally, scenario and sensitivity analyses are conducted to provide further insights into design and optimization of the biomass based supply chains.
Bibliographical note© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Funding: TÜBITAK(The Scientiﬁc and Technological Research Council of Turkey)2219- National Postdoctoral Research Scholarship Programme.
- Bi-level decision support system
- Fuzzy ε-constraint method
- Supply chain design
- Sustainable energy production
- Transportation network design
Balaman, Ş. Y., Matopoulos, A., Wright, D. G., & Scott, J. (2018). Integrated optimization of sustainable supply chains and transportation networks for multi technology bio-based production: A decision support system based on fuzzy ε-constraint method. Journal of Cleaner Production, 172, 2594-2617. https://doi.org/10.1016/j.jclepro.2017.11.150