PhD research student
Qualifications: BA (Industrial Engineering, Iran), MSc (Industrial Engineering, Iran)
Thesis title: Bi-objective Location-Efficiency Mathematical Modelling in a Three-Echelon Renewable Energy Supply Chain with Demand Forecasting Using Chaos Theory and a Backpropagation Neural Network
PhD start date: 1 April 2025
Supervisors: John Williams, Brendon Woodford
Email mehma823@student.otago.ac.nz
Research summary
My research is a multidisciplinary study in three key dimensions: Environment, Society, and Policy, also highlighting the combination of Renewable Supply Chain with a focus on location efficiency modelling, Time Series, and Chaos Theory. The study investigates the behaviour of energy consumption time series based on dynamical characteristics, Phase Space Reconstruction (PSR), Machine learning methods to forecast end-users' demand, and then optimal locations for energy infrastructures based on the forecasted demand to answer all citizens' energy needs will be identified.
Research interests
Logistics and Supply Chain Management, Mathematical Models, Humanitarian Logistics, Chaos Theory, Renewable Energy, Machine Learning.