The cost of road construction or its repair is among the most concerns for the pavements engineers. Cracking and degradation is common mode of failure in asphalt pavements that occurs due to increasing traffic loads or even environmental conditions. For facing with these damages, some solutions are proposed including correction, quality improvement and increasing the asphalt resistance. In this research, by adding different percent amounts of black nano-carbon and polyester fibers as modifier in the asphalt mixtures and conducting several Marshall tests, it was observed that adding these two additives can improve generally the Marshall results. Polyester fiber causes preventing crack and damages of asphalt because of armed effect specification. According to the results, Marshall stability is increased up to 61%. Furthermore, an economic analysis was performed to investigate the cost of using such modified asphalt mixtures for constructing 1 km of a six line road and suitable percentages of additives were found from mechanical-economic analyses.

Municipal solid waste management is one of the main challenges in mega cities. Consequences of landfilling and importance of using alternative energies contribute to the popularity of incineration facilities for city authorities. This article tries to determine the location and allocation decisions on landfills and incineration facilities regarding social and environmental effects. The proposed model minimizes revenue of selling recovered energy and minimizes visual pollution impacts. In addition, the environmental impacts of incineration and landfilling operations are considered as part of constraints. Using the data of Tehran's waste management organization, numerical results show the number of active transfer stations and traffic reduce leading to an increase on waste collection network's efficiency. The results also show a decrease on the landfilling and the amount of hazardous methane emissions as well as an improvement on power and heat energy recovery.

This paper optimizes joint mixed bundling, assortment planning and pricing problems for complementary retail categories that comprise substitutable items. These categories are divided into one primary category and multiple secondary categories and based on cross-selling effect, customers of primary category can opt to buy from other categories. It is assumed that the bundle can comprise one product of each offered complementary categories. A mixed-integer nonlinear programming is proposed that maximizes total profit by optimizing strategy of bundling, assortment selection and prices of single products and the bundle under a maximum-surplus customer choice model. Then because of computational considerations, this model is linearized and converted to a mixed-integer linear programming; whereby exact solution for even large-scale problems can be obtained. The results show that if cross-selling effect between product categories is overlooked or bundling of products is not allowed, then significant profit losses, will be resulted. To the best of our knowledge, this is the first study on bundling strategies in assortment selection decisions.

During the past few years, there have been tremendous efforts on development of Iranian banking industry in an attempt to fight against monopoly of banking system. Bank Pasargad is one of the biggest Iranian banks established for offering retail, commercial as well as investment banking services. The firm was established in 2005 as part of the government & apos; s privatization of the banking system. This paper presents an empirical investigation to determine the most efficient locations for development of this bank. The study uses three main criteria, customer services (C1), Equipment (C2) and Profitability (C3) to compare different alternatives for establishing new branches in city of Tehran, Iran. Using fuzzy data envelopment analysis the study has determined that center of the city could receive the most important attraction followed by north region, west region as well as south west.

Locating, routing and inventory control in production and distribution centers are the most important decisions in supply chain management. Because of the dependence of these decisions to each other, considering these three aspects simultaneously can have a huge impact on cost reduction. In this study, first, a location-inventory model is developed by utilizing METRIC approach and then, METRIC approach is applied to the location-inventory-routing model. The intended supply chain includes a supplier, distributors and retailers, and the inventory control policy is implemented for both the distributors and retailers. Retailers' demand follows Poisson distribution and the lead-time is also considered probabilistic and is affected by the shortage in distribution centers. Given that the presented model belongs to the class of NP-hard problems, a hybrid metaheuristic solution method is also presented to solve the resulted problem. The proposed hybrid metaheuristic algorithm contains a Simulated Annealing algorithm, to optimize the location-routing problem, and a Genetic Algorithm, to optimize the inventory problem. Also, to evaluate the performance of hybrid algorithm, a comparison between the results of the proposed hybrid algorithm and the exact solutions obtained from Lingo software is provided and, finally, the results are analyzed.

Surface roughness and cutting forces are considered as important factors to determine machinability rate and the quality of product. A number of factors like cutting speed, feed rate, depth of cutting and tool noise radius influence the surface roughness and cutting forces in turning process. In this paper, an Artificial Neural Network (ANN) model was used to forecast surface roughness and cutting forces with related inputs, including cutting speed, feed rate, depth of cut and tool noise radius. The machined surface roughness and cutting force parameters related to input parameters are the outputs of the ANN model. In this work, 24 samples of experimental data were used to train the network. Moreover, eight other experimental tests were implemented to test the network. The study concludes that ANN was a reliable and accurate method for predicting machining parameters in CNC turning operation.

Web mining has been widely used to discover knowledge from various sources in the web. One of the important tools in web mining is mining of web user’s behavior that is considered as a way to discover the potential knowledge of web user’s interaction. Nowadays, Website personalization is regarded as a popular phenomenon among web users and it plays an important role in facilitating user access and provides information of users’ requirements based on their own interests. Extracting important features about web user behavior plays a significant role in web usage mining. Such features are page visit frequency in each session, visit duration, and dates of visiting a certain pages. This paper presents a method to predict user’s interest and to propose a list of pages based on their interests by identifying user’s behavior based on fuzzy techniques called fuzzy clustering method. Due to the user’s different interests and use of one or more interest at a time, user’s interest may belong to several clusters and fuzzy clustering provide a possible overlap. Using the resulted cluster helps extract fuzzy rules. This helps detecting user’s movement pattern and using neural network a list of suggested pages to the users is provided.

Iran’s 4th development plan has enrolled the federal government to increase energy prices and remove any subsidies paid to energy sector in an attempt to increase productivity and provide better social welfare. It is, therefore, important to investigate the effects of the law on different industries and the subsequent effects on economic growth. This study investigates the impacts of subsidies on the market value of listed companies in Tehran Stock Exchange in Petrochemical and Steel industries. The study selects two firms, Esfahan & apos; s Mobarakeh Steel Company and Fanavaran Petrochemical Co. using Auto Regressive Distributed Lags (ARDL) over the period 2008-2013. The results have indicated that there was a meaningful relationships between removal of subsidies and market value of the firms.

Nowadays, many organizations focus on discovering their customers’ hidden patterns to maintain their competitive position through customer analysis. In fact, more and more organizations are realizing that customers are their most valuable resources. This paper performs a research using data associated with 300 clients of an insurance company in city of Anzali, Iran and they are analyzed using K-Means clustering method. Using demographic variables including gender, age, occupation, education level, marital status, place of residence and clients’ incomes, the study determines the optimal numbers of clusters in order to achieve necessary data for grouping customers. Next, the study uses the method of association rules to find hidden patterns for the insurance industry.

According to the 4th Iran’s development plan, there must an increase in energy prices and removal of subsidies paid to energy sector. Therefore, it is necessary to study the effects of removing subsidies paid on energy on different industries and the subsequent impact on economic growth. This study aims to investigate the effects of subsidies on the market value of listed companies in Tehran Stock Exchange in automotive and cement industries. The study selects two firms, Fars & Khuzestan Cement Co. and Irank Khodro using Auto Regressive Distributed Lags (ARDL) over the period 2008-2013. The results of study have indicated that there was a meaningful relationship between removal of subsidies and market value of the firms.