Decision analysis in the UK energy supply chain risk management: tools development and application.

Vafadarnikjoo, Amin (2020) Decision analysis in the UK energy supply chain risk management: tools development and application. Doctoral thesis, University of East Anglia.

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Abstract

The aims of this thesis are developing decision-making tools for risk identification, risk causal relationships analysis, risk prioritisation, and long-term risk mitigation strategy recommendations in the UK energy supply chain. The thesis is comprised of four study phases in eight chapters. In phase I, a framework is introduced including 12 risk dimensions, and 5 classification perspectives. Then, in phase II, the Neutrosophic Revised Decision-Making Trial and Evaluation Laboratory (NR-DEMATEL) method has been utilised in order to analyse the 12 identified risk dimensions based on the causal interrelationships between them. Additionally, a novel Hesitant Expert Selection Model (HESM) to systematically assist researchers with the expert selection process is proposed. In phase III, two extensions of the original Best-Worst Method (BWM) are proposed in order to contribute to the theoretical development and application of the BWM in energy supply chain risk prioritisation. The Neutrosophic Enhanced BWM (NE-BWM) and hybrid Spanning Trees Enumeration and BWM (STE-BWM) are introduced to enhance the efficiency of the original BWM in dealing with uncertainty in experts’ subjective judgements. In phase IV, a novel stratified decision-making model is introduced. It is based on Concept of Stratification (CST), game theory and Shared Socio-economic Pathway (SSP) to deal with long-term risk mitigation planning for the most critical identified risks. The model has been applied in the region of Highland and Argyll in Scotland based on the primary data obtained from experts to prioritise flooding risk mitigation strategies which were recommended by the Scottish Environment Protection Agency (SEPA). The stratified decision-making model is aimed at taking into account both UK socio-economic situations and flooding risk impacts for the long-term decision making.

Item Type:	Thesis (Doctoral)
Faculty \ School:	Faculty of Social Sciences > Norwich Business School
Depositing User:	Chris White
Date Deposited:	08 Dec 2020 15:56
Last Modified:	09 Dec 2020 10:54
URI:	https://ueaeprints.uea.ac.uk/id/eprint/77909
DOI:

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