Closed-loop supply chain network for end-of-life wind turbines considering CO2 emission and employment

End-of-life (EOL) wind turbines (WT) are the issue that will need to be addressed on a mid- to long-term scale, as the use of wind energy is on the rise around the world. In order to deliver efficient and effective operations, it is necessary to establish a systematic approach to handling EOL WTs. Thus, a closed-loop supply chain network is essential for meeting this demand. To accomplish the aforementioned aim, text mining and clustering are used to compress the literature review. Hence, literature is lacking. Next, a multi-objective mathematical model for a closed-loop supply chain network covering CO2 emissions, employment, and transportation and process costs is provided. Due to the model's reduced complexity in component movement from wind farms to disassembly centers, an uncapacitated p-median problem is applied to center selection. The novel meta-heuristic Improved Crow Search Algorithm solves the p-median problem (I-CSA). An integrated local search portion in gap and time improves convergence to an optimal or near-optimal solution in the discretized Crow Search Algorithm (CSA). The thesis proposes a structured, closed-loop supply chain for EOL WTs. Text mining and clustering find literature gaps, another thesis contribution. Finally, a new meta-heuristic approach is used to the uncapacitated p-median problem for the first time.