Vahid Khodaee

PhD Candidate

Vahid Khodaee

PhD Candidate

Vahid Khodaee started his PhD program at the Kühne Logistics University under the supervision of Prof. Dr. Maria Besiou and Prof. Dr. Andreas Kilian Gernert in September 2022. His main areas of research are Humanitarian Logistics and Sustainable Operations.
Prior to the KLU, he received his Master's degree in Industrial Engineering in Systems Optimization at the Sharif University of Technology, a highly regarded university in Iran. The title of his Master thesis was “Developing a Mathematical Model for Optimum Drug (Vaccine) Distribution of Infectious Diseases in Epidemic Conditions: The COVID-19 Case Study”. Vahid received his Bachelor's degree in Industrial Engineering from Amirkabir University of Technology (Tehran Polytechnic) in September 2019.

Besides his studies, he gained some practical experience as a Supply Chain Specialist in Padideh Shimi Paydar Industrial Group for six months. Moreover, he was a teaching assistant for one year during his Master's study in the Operations Research course.



Academic Positions

2021 - 2022

Teaching Assistant, Sharif University of Technology, Tehran, Iran


Since 2022    

PhD Candidate in Supply Chain Management , Kühne Logistics University, Hamburg, Germany

2019 - 2022 Master of Science in Industrial Engineering, Sharif University of Technology, Tehran, Iran
2017 - 2019 Bacherlor of Science, Amirkabir University of Technology, Tehran, Iran
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Journal Articles (Peer-Reviewed)


Abstract: This research develops a humanitarian cold supply chain model with equity consideration for COVID-19 vaccine distribution during a pandemic, considering deprivation cost and an important social concept named equity. The proposed comprehensive plan minimizes all incurred costs, including transportation costs, shortage costs, deprivation costs, and holding costs, while aiming at eliminating infection and mortality rates. The proposed three-echelon supply chain model includes suppliers, distributors, and affected regions (ARs), as destinations. We apply the proposed model to the actual vaccine distribution data during the COVID-19 outbreak in Europe. A mixed integer programming (MIP) model is developed to minimize the costs and satisfy the demand goals in the vaccine distribution plan. A sensitivity analysis demonstrates how total and deprivation costs affect each other, helping the managers establish a trade-off between them. The results show that appropriate supply chain planning can minimize logistics and social costs. The proposed model can help policymakers, and decision-makers better understand the importance of equity and implement a fair distribution of vaccines, considering the deprivation cost as a social cost.

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