List of Articles G35

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  1 - Joint Pricing and Inventory Routing Modeling in a Two Echelon Closed Supply Chain
  mohamad mohamadnejad Isa nakhaei kama abadi Ramin Sadeghian Fardin Ahmadi zar
  Abstract This paper studies the pricing issue in a multi-period and multi-product closed-loop supply chain with price-dependent demands. The aim is to assign a location for the collection and disassemble center, vehicle routing, and material ordering in order to maximi More

  Abstract This paper studies the pricing issue in a multi-period and multi-product closed-loop supply chain with price-dependent demands. The aim is to assign a location for the collection and disassemble center, vehicle routing, and material ordering in order to maximize the profit.  In the study, a non-linear mathematical model is presented for small scale problems. Due to the NP-hardness of the problem, two met heuristics, genetic algorithm and particle swarm optimization algorithm, are applied to solve medium and large scale problems. The algorithms are validated by comparing their results with those of the mathematical model. Finally, the performance comparison of the two met heuristics through statistical analysis is demonstrated that the particle swarm optimization algorithm performance outperforms the genetic algorithm. Manuscript profile
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  2 - بررسی روش‌های تأمین مالی با رشد سود آوری شرکت‌های صنایع داروئی در ایران
  علی نعمتی مجتبی کریمی رویا وحیدی مولوی
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  3 - The Compare of Power Fire Flies Algorithm Prediction, Decision Making Tree Algorithm and the Support Vector Machine Regression Algorithm for Systematic Risk Predicti
  alireza eslampour roya darabi
  Financial and economic decisions are always at risk due to future uncertainties. Therefore, one of the ways to help investors is to provide investment risk forecasting patterns. The more predictions are closer to reality, the decisions made on the basis of such predicti More

  Financial and economic decisions are always at risk due to future uncertainties. Therefore, one of the ways to help investors is to provide investment risk forecasting patterns. The more predictions are closer to reality, the decisions made on the basis of such predictions will be correct. In this research, the goal of predicting the systematic risk of companies admitted to Tehran Stock Exchange using artificial neural network software and three night-worm algorithms, decision tree algorithm and backup vector machine regression algorithm. For this research, a sample of 92 companies from listed companies in Tehran Stock Exchange during the period 2013 to 2018 has been used. The results obtained from the research hypothesis test showed that the predictive power of systematic risk in the night cream algorithm is more than the decision tree algorithm and the support vector machine regression algorithm, as well as the predictive power of the decision tree algorithm in relation to the backup vector machine regression algorithm It is higher for systematic risk prediction Manuscript profile
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  4 - Localization of systematic risk assessment patterns Based on financial and non-financial variables
  alireza eslampour roya darabi
  Financial and economic decisions are always at risk due to future uncertainties. Therefore, one way to help investors is to provide investment risk forecasting models. But as these projections are closer to reality, decisions that are based on such predictions will be m More

  Financial and economic decisions are always at risk due to future uncertainties. Therefore, one way to help investors is to provide investment risk forecasting models. But as these projections are closer to reality, decisions that are based on such predictions will be more correct. The main purpose of the present experimental study is to predict systematic risk with emphasis on financial and non-financial variables. The statistical population of this research is the companies accepted in Tehran Stock Exchange. The data of the study consist of 552 Firm-year from 2012 to 2017. To test the hypotheses, the artificial neural networks approach and night worm algorithms, decision tree and regressor of backup vector machine have been used. The results of this study showed that the results obtained from the hypothesis test showed that all three of the algorithms have the power to explain systematic risk. Manuscript profile