displacement between levels, and maximize the RELIABILITY of delivery for suppliers. This NETWORK is including supplier centers, production/resuscitation centers, distribution/collection centers, customer centers, and disposal centers. A new Linear Integer programming model is formulated. Besides, fuzzy parameters and multi-objective function are used to approach the real world. In this regard, a deterministic two-step approach is used to consider the uncertainty in the proposed model. Finally, the performance and efficiency of the proposed model and solution methods are simulated in the numerical example and examined and suggestions are presented for using this model in the real world.

Today, the electricity power system is the most complicated engineering system has ever been made. The integrated power generating stations with power transmission lines has created a NETWORK, called complex power NETWORK. The RELIABILITY estimation of such complex power NETWORKs is a very challenging problem, as one cannot find any immediate solution methods in current literature. In this paper, we advanced a new method for estimating the RELIABILITY of such NETWORKs, which is based on 1) decomposition of the whole NETWORK into subNETWORKs called islands, 2) estimating each island’ s RELIABILITY in exact form using the NETWORK RELIABILITY theory, and 3) assembling the islands back together to estimate the whole NETWORK RELIABILITY, again in exact form. We applied the new method on Iran’ s power NETWORK with 105 generation stations and 16460 kilometres of transmission lines.

Tehran is the capital of Iran and is one of the largest cities in the world. With its history of earthquakes, high population density, and a population of over 8.5 million, it must be prepared to cope with disaster. This article introduces a new method for estimating the RELIABILITY of a roadway NETWORK that considers the physical characteristics of the road and link capacity degradation. The proposed method was applied to a real road NETWORK and the connectivity RELIABILITY of the city was calculated. The proposed technique for computing connectivity RELIABILITY is sensitive to the characteristics of the road and adjacent buildings. The results indicate that northern Tehran, with a low population density and newer buildings and highway NETWORK, shows higher RELIABILITY for origin and destination trips than other regions. This region has a higher probability of connectedness to other regions after a disaster. The weakest area of the NETWORK lies in the southern and central regions with older buildings and no highway NETWORK. These regions show low connectivity RELIABILITY and high population density. These results provide useful insights into the vulnerability of the Tehran NETWORK in response to a major earthquake.