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Timely, effective delivery of relief resources to the sufferers in a natural disaster is quite crucial. All governments especially those having high risk of natural disasters must have a comprehensive and implementable plan to suitably manage humanitarian issues after a natural disaster occurs. They should construct sufficient facilities and prepare and store enough relief items (e.g., tents, medical packages, and meals) to be able to quickly respond to necessary needs of injured and homeless people; otherwise, it might lead to humanitarian crises with high numbers of casualties. In this study, we extend the mathematical two-stage stochastic optimization model proposed by Mete and Zabinsky (Int J Prod Econ 126:76-84, 2010) simultaneously considering two phases: pre- and post-disaster. All decisions pertinent to the pre-disaster phase, such as the locations of warehouse and pre-positioned relief items, should be taken in the first stage; however, other decisions pertinent to the post-disaster phase, such as the allocations of warehouses to the demanding points are taken in the second stage. In this study, we consider bi-directional relations between warehouses, which can increase the flexibility of the constructed network to handle the needs of injured people in a shorter time interval. Furthermore, whereas the relief process should be implemented within a specific time interval (e.g., 3 days), our extended optimization model is constructed for a multi-period and multi-product situation. We construct the extended model based on a professional report prepared by the Japanese International Cooperation Agency in a study of the seismic micro zoning of Tehran on April 13, 1999. The respective data in this report has also been updated based on the reports of Tehran Municipality to be matched with the current situation of Tehran. The results of our extended multi-period model for the respective real case study in this paper verify suitable responses and better services to the affected areas compared to what are provided through the respective single-period optimization model. [ABSTRACT FROM AUTHOR]