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Towards sustainability in energy-intensive manufacturing supply chains with integration of renewable generation systems (Advert Reference: SF21/BL/MOS/FATTAHI)


   Faculty of Business and Law

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Manufacturing industries account for one third of the electricity consumption in the world. Several companies (e.g., Honda, Anheuser-Busch, Apple) installed biomass-based, wind, and solar generators in their energy-intensive facilities to attain low-carbon operations. In Europe, Germany and Denmark have the highest rate of renewable generation systems (RGS) penetration in industries because of national policies.

Several studies emphasize on the importance of using RGSs in energy-intensive facilities of supply chains (SC), such as Sun et al. (2021); Ezici et al. (2020); Benedek et al. (2018); Stindt (2017), and a few ones address the integration of RGSs in SC planning. Fattahi et al. (2018) study the usage of wind and solar generators in mining SCs. Golari et al. (2017) propose a model addressing the operation of manufacturing facilities with intermittent power from renewable energy resources. Li et al. (2017) propose a distributed generation (DG) system to achieve a zero-carbon power supply model and the integration of onsite renewable energy into facilities. Villarreal et al. (2012) seek to design a DG system that can provide electricity for wafer fabs.

Based on an extensive literature survey and Wee et al. (2012), two research gaps are not addressed, which are major barriers in usage of RGSs in manufacturing SCs:

1-     The investment decisions of industries on RGSs are policy-driven and long-term, and an investment modelling framework is needed for SCs, which considers uncertainties influencing on SC actors and consequences of the political decisions.

2-     Energy production of several RGSs, such as solar and wind generators, is uncertain, and a decision support system (DSS) is needed for SC planning with uncertain renewable energy.

The main aim of this research will be to achieve sustainability in industry sector by the investment on RGSs and optimize planning decisions for SCs with intermittent and uncertain renewable energy generation.

Therefore, main objectives are:

The first objective is to analyse the influence of the existing uncertain environment related to the decision of SC energy-intensive actors, such as suppliers and manufacturers, for investment on RGSs. In particular, the purpose is to understand the effect of social, political, and economic uncertainties on SC actors’ decisions. One theoretical contribution is to propose a novel simulation approach regarding the investment decision of SC actors on potential RGSs and develop a bundle of scenarios that describe possible development paths of energy policy for SCs.

The second objective is to propose and implement a DSS for planning of energy-intensive manufacturing SCs with RGSs. This DSS will integrate various uncertainty types (such as energy generation, fuel price, demand, etc.). Theoretically, new mathematical model based on recent advances in decision-making under uncertainty will be developed to address this objective. Finally, quantitative assessment of the environmental and social responsibility performance of the whole SC in using RGSs will be considered as a sub-objective.

This study will be interdisciplinary and involves literature from energy planning, SC management, Operations Research, and sustainable development. Quantitative methods, including data analytics and optimization approaches, related to decision-making under uncertainty and uncertainty modelling will be applied. 

This project is supervised by Dr Mohammad Fattahi

Eligibility and How to Apply:

Please note eligibility requirement:

·       Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.

·       Appropriate IELTS score, if required.

For further details of how to apply, entry requirements and the application form, see

https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/ 

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF21/…) will not be considered.

Deadline for applications: Open

Start Date: March 2022 or October 2022

Northumbria University takes pride in, and values, the quality and diversity of our staff and students. We welcome applications from all members of the community.


References

Benedek, J., Sebestyén, T. T., & Bartók, B. (2018). Evaluation of renewable energy sources in peripheral areas and renewable energy-based rural development. Renewable and Sustainable Energy Reviews, 90, 516-535.
Ezici, B., Eğilmez, G., & Gedik, R. (2020). Assessing the eco-efficiency of US manufacturing industries with a focus on renewable vs. non-renewable energy use: An integrated time series MRIO and DEA approach. Journal of Cleaner Production, 253, 119630
Fattahi, M., Mosadegh, H., & Hasani, A. (2018). Sustainable planning in mining supply chains with renewable energy integration: A real-life case study. Resources Policy.
Golari, M., Fan, N., & Jin, T. (2017). Multistage stochastic optimization for production‐inventory planning with intermittent renewable energy. Production and Operations Management, 26(3), 409-425.
Li, B., Tian, Y., Chen, F., & Jin, T. (2017). Toward net-zero carbon manufacturing operations: an onsite renewables solution. Journal of the Operational Research Society, 68(3), 308-321.
Stindt, D. (2017). A generic planning approach for sustainable supply chain management-How to integrate concepts and methods to address the issues of sustainability? Journal of cleaner production, 153, 146-163.
Sun, J., Ruze, N., Zhang, J., Shi, J., & Shen, B. (2021). Capacity planning and optimization for integrated energy system in industrial park considering environmental externalities. Renewable Energy, 167, 56-65.
Wee et al. (2012). Renewable energy supply chains, performance, application barriers, and strategies for further development. Renewable and Sustainable Energy Reviews, 16(8), 5451-5465.
Villarreal et al. (2012). Designing a sustainable and distributed generation system for semiconductor wafer fabs. IEEE Transactions on Automation Science and Engineering, 10(1), 16-26.

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