When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.
Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.
In the age of Industry 4.0 and global warming, it is inevitable for decision-makers to change the way they view the coal supply chain (SC). It is imperative that conventional SC assessment methods be replaced with new, more sustainable ones in light of the fourth industrial revolution. In nature, energy is the currency, and nature is the source of energy for humankind. Coal is one of the most important sources of energy. Despite the fact that coal provides much-needed electricity, as well as steel and cement production, civilization is not able to function without it. This manuscript-based PhD thesis examines the coal SC network as well as the carbon reduction strategies and plans to develop a comprehensive model for sustainable design. Thus, the Extended Exergy Accounting (EEA) method is incorporated into a coal SC under economic order quantity (EOQ) and economic production quantity (EPQs) in an uncertain environment. Using a real case study in coal SC in Iran, four carbon reduction policies (carbon cap, carbon tax, carbon trade, and carbon offset) are examined. Additionally, four carbon policies are compared for sustainable performance of coal SCs in some developed and developing countries (the USA, China, India, Japan, Australia, Turkey, etc.) with the world's most significant coal consumption. As a result of applying the EEA method, the objective function of the four optimization models under each carbon policy is to minimize the total exergy (in Joules as opposed to Dollars/Euros) of the coal SC in each country. These four sustainable models are multi-product EOQ/EPQ models with a single-vendor multi-buyer regarding shortage as a backorder with some real constraints such as inventory turnover ratio, disposal of imperfect quality items (waste) to the environment, transportation costs, produced carbon emissions, and available budgets for each buyer in the SC. The models have been solved using three recent metaheuristic algorithms, including Ant lion optimizer (ALO), Lion optimization algorithm (LOA), and Whale optimization algorithm (WOA), as well as three popular ones, such as Genetic algorithm (GA), Ant colony optimization (ACO), and Simulated annealing (SA), are suggested to determine a near-optimal solution to an exergy fuzzy nonlinear integer-programming (EFNIP). Moreover, the proposed metaheuristic algorithms are validated by using an exact method (by GAMS software) in small-size test problems. Finally, through a sensitivity analysis, this dissertation compares the effects of applying different percentages of exergy parameters (capital, labor, and environmental remediation) to coal SC models in each country. Using this approach, coal SCs in developed and developing countries can determine the best carbon reduction policy and exergy percentage that leads to the most sustainable performance (the lowest total exergy per Joule). The findings of this study may enhance the related research of sustainability assessment of SC as well as assist coal enterprises in making logical and measurable decisions.