Transportation NETWORKs are essential infrastructures of the present societies and are progressively growing in importance. Therefore, the NETWORK should develop in accordance with the society's needs. Transportation NETWORK expansion is a costly process and often is not returnable. Usually, there are not enough resources to satisfy the hole future demand; therefore, available resources must be utilized in an efficient manner. It has been shown that classic transportation planning approach and decision theory should be revised and a more flexible planning approach must be taken to deal with the uncertainties and the internal or external changes. The concept of flexibility has been used in many scientific disciplines including transportation planning. Most of the studies of NETWORK flexibility have focused on the concept of capacity flexibility. NETWORK flexibility can be considered as an effective factor in NETWORK reliability. A NETWORK should be flexible enough to be considered as a reliable one. In this study, a model of transportation NETWORK DESIGN has been developed based on increasing the flexibility, especially increasing the survivability of the NETWORK facing daily variations including daily crashes or rush-hour blockings. NETWORK survivability is the ratio of the demand that will be answered after the occurrence of a failure in a NETWORK. Improving the properties of a NETWORK will affect the flow pattern all around the NETWORK. In most studies, utility theory has been used to model user answer to realize the model to a greater degree. It has been shown that utility theory faces basic shortcomings and regret theory has been introduced as an alternative way to handle the users' behavior. In this study, a stochastic user equilibrium has been developed based on regret theory to model the user reply to the NETWORK expansion. Results show that the NETWORK improves based on the survivability model, which has better performance when a link is blocked.

To Adequately DESIGN density and distribution of rain- gauges in rainfall NETWORKs of each region, is an effective step toward success of water projects, regional programming and proper use of information. In this research, the locations of new rain- gauges in rainfall NETWORK of Gav-khuni basin have been determined using transinformation entropy concept based on annual rainfall data of stations (1356-1385).Sequential and genetic algorithms have been used in order to seleet the proper rain- gauges sites. Two objectives of maximizing the minimum transinformation entropy and maximizing the mean of transinformation entropy have been defined for each algorithm. Then the performances of different models have been compared. The results imply on the better performance and relative supremacy of genetic algorithm, with maximizing the minimum entropy (maximum supremacy is +1.31, +1.34 and +0.12% in zone 1, 2 and 3, respectively) and maximizing the mean entropy (maximum supremacy is +0.35, +0.21 and +0.02% in zone 1, 2 and 3 respectively) objectives to sequential algorithm.

New technologies require new approaches to create valuable opportunities in the supply chain to integrate not only the physical progress of goods and services but also massive information and financial data. Using the technology of the day and analyzing the existing data and presenting the reports to the managers of the organization at the right time enable them to make suitable and intelligently decisions due to market fluctuations. It causes to move their effective steps toward the organizations strategic goals. Nowadays, the flexibility of the organizations is very important due to the changing customer’ s needs. On the other hand, the suitable time and amount of ordering has a significant impact to reduce the costs and to increase the organization's agility. Cloud technology, as a key feature in today's world, can contribute on data transfer in various performance models of the supply chain and also in analyzing various business parts. For this purpose, this research fallows to explore the use of cloud technology to value the supply chain processes and presents the problem as a mathematical model. The mathematical model has been thoroughly solved and analyzed in the large dimensions of the problem using the best-developed optimization algorithm. The results showed that with the implementation of the proposed supply chain NETWORK, transportation and costs reduce significantly and increase company revenue, which lead the companies to the green supply chain. Moreover, by removing intermediaries, the goods are delivered to the final customer with a better price and quality, which result in more customer satisfactions.

In this paper a novel non-parametric approach for DESIGNing geodetic NETWORKs based on  evolutionary algorithms is proposed. Since conventional analytical optimization methods are based on gradient approaches, they have some drawbacks including approximate parameters requirement and inability to work with discrete objective functions and/or constraints e.g. visibility matter. To prevent from above problems, the standard genetic algorithm method is utilized. Primary tests demonstrate the capability of proposed method to DESIGN the optimal NETWORK without approximate values and with visibility constraint satisfaction.