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ABSTRACT:

Existing models for disaster preparedness and response address network design and resource allocation challenges. However, these models typically adopt a global optimization point of view, which may not be attainable since they do not consider the actual decision-making process after a disaster occurs. In this talk, I will present a new mathematical model to optimally determine the design of a relief supply chain. The model is based on the realization that at times of crises, the instructions given by the authorities to the affected population regarding which facility they should visit, are often not followed, which may cause some facilities to be congested, while others to be under-utilized. The model incorporates practical considerations such as the population behavior and equitable supply allocation. An efficient optimal solution method and a heuristic algorithm based on the Tabu-search method were developed and were tested on real data obtained from the Geophysical Institute of Israel. The results indicate that the suggested modeling approach improves the entire supply chain performance.
To further improve the system performance, an incentive mechanism is added to the model, whose objective is to increase compliance with the centralized planning. A comprehensive numerical experiment and sensitivity analysis were conducted, which reveal an interesting insight, namely: even a low investment in a small proportion of the population can lead to a relatively large improvement. In addition, it is shown that proper planning of the incentive mechanism may affect the relief supply chain design decisions.
In the second part of my talk* I will introduce the Sample Referral Design (SDR) problem. The goal is to design a supply chain network for delivering test samples that diagnose and monitor the HIV epidemic, in an environment where test resources are scarce and costly. The problem is based on a case study from Tanzania, in which the objective is to minimize the expected number of infants who die due to delays in return of results. A prediction model for Early Infant Diagnosis (EID) results is developed, as well as a simulation model that describes the system. The objective is addressed through restructuring the sample referral network using analytical tools from queuing theory. The results indicate that our model can lead to significant improvements and has a great potential to save lives of many infants.
The work was performed under the supervision of Prof. Michal Tzur.
* This part was performed jointly with Dr. Dan Yamin

BIO: Reut Noham is a PhD student in the Department of Industrial Engineering, Faculty of Engineering at Tel-Aviv University. She holds a BSc. and MSc. in Industrial Engineering, both from Tel-Aviv University. Reut’s research is in the field of Humanitarian logistics, Supply chain management and Network design in healthcare systems. Her research is based on data-driven applications alongside with the theoretical analysis and mathematical modeling. Her work was funded by the Ministry of Science, Technology and Space in Israel.