سمین اربابی

Designing an efficient construction supply chain involves numerous complexities. Therefore, the imperative of designing an appropriate supply chain, considering the high diversity of resources (nonrenewable), holds significant importance.  Given the variable nature of the project environment, the dynamic progress of the project, and the risks associated with construction projects, the project may deviate from the scheduled timeline. On the other hand, the quality of resources plays a crucial role in the construction quality of construction projects. Therefore, in this study, a multi-objective mixed-integer programming model is proposed for the design of a two-echelon supply chain network in the construction industry. The objectives of the model include minimizing supply chain costs, minimizing deviation from resources time delivery, and maximizing the quality of resources. This framework is capable of dynamically scheduling resources in terms of timing and delivery, as well as selecting appropriate suppliers restricted to authorized facilities within a network. Both classical methods and the Benders Decomposition Algorithm are employed to solve the presented model. The problem was solved for three sizes. The Benders Decomposition Algorithm was able to efficiently solve the problem for all three sizes in a very short amount of time.

Supplier selection and determining appropriate replenishment policies is one of the major concerns in production systems and supply chains. This paper describes the supply chain of integrated buyers and suppliers of perishable product. The retailer supplies the product it needs from different manufacturers. Products purchased by manufacturers are perishable products, and their Perishable rate is expressed as a fixed percentage of retailers' and manufacturers' inventories. In this situation, the selection of suppliers and the determination of the quantity to purchase from each supplier have a great impact on the optimization of the supply chain. Based on the fixed demand of the end customers, the retailer tries to determine its optimal replenishment policy, and based on this optimal policy, the process of purchasing raw materials from manufacturers, as well as the time and amount of purchase from each manufacturer will be determined. Among these, the supplier that can deliver the product within the specified time and at the lowest cost will be selected. The main purpose of this paper is to select suppliers and determine replenishment policies in a way that minimizes the total cost of this chain of supply, including retailer and supplier costs. This model uses a mixed-integer linear programming approach. Finally, this model was checked with a numerical example.

Periodontal disease is one of the most common infectious diseases of the mouth. Correct and early diagnosis can reduce the incidence of adverse effects. The aim of this study was to evaluate the accuracy and efficiency of artificial neural network in the diagnosis of periodontal disease.
Material and The Diagnostic Study were performed from 230 periodontal disease cases in Zahedan Dentistry School in the period of time between 2015 and 2016. 5 variables including age, gender, plaque index, probing pocket depth, and clinical attachment loss index were evaluated in these people. The artificial neural network model with propagation algorithm of Levenberg-Marquardt training function was used. Positive predictive value and negative predictive value were used to evaluate the network at the test stage.
The results show that the back propagation network with the structure of 5-20-4-2 and the Levenberg-Marquardt algorithm and the use of the same transfer functions in all layers (sigmoid hyperbolic tangent) can be used as an efficient teaching function in Diagnosis of periodontal disease. The values of positive and negative predictive values were 94.7 and 80 percent respectively. The software output yielded significant amounts of time (4.5870) and regression for train, test and overall (0.7454, 0.9749, 0.9254).
It seems, artificial neural networks can be helpful for diagnosis of periodontal disease at least time.