Performance evaluation of the production systems by considering data related to different time periods is one of the most important issues of production theory. In this paper, a new method for measuring the aggregative efficiency of multi-period production systems using More
Performance evaluation of the production systems by considering data related to different time periods is one of the most important issues of production theory. In this paper, a new method for measuring the aggregative efficiency of multi-period production systems using the data envelopment analysis (DEA) technique is proposed. The provided approach could be considered as extension of the radial methods in the literature. An extended Russell based model is presented for the first time to measure aggregative efficiency with respect to the time intervals of the production stages. One of the useful features of the proposed model is that the inefficiency of the existing aggregative approach is detected in one step without need to account for the second stage of optimizing slack variables. Some properties and advantages of the new model is discussed. Finally, to illustrate the applicability of the new approach, two practical examples are investigated and analyzed. The results show the good performance of the proposed method.
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The present study is an applied and descriptive-analytic research, while investigating a multi-period portfolio, below the optimal calculated responses to the analytical form of Skaf and Boyd (2009) for models with limited transaction costs and the impossibility The sal More
The present study is an applied and descriptive-analytic research, while investigating a multi-period portfolio, below the optimal calculated responses to the analytical form of Skaf and Boyd (2009) for models with limited transaction costs and the impossibility The sale of borrowings in the Tehran Stock Exchange is reviewed. In this research, the first 30 active companies in the Tehran Stock Exchange, which have the highest ratings in the period between 2011 and 2016, were first selected, and then, using data envelopment analysis, 8 efficient companies were selected among the 30 companies. The research result Show that the optimal answer is of good quality.envelopment analysis, 8 efficient companies were selected among the 30 companies. The research result Show that the optimal answer is of good quality.
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In this research, a mathematical model has been presented for optimizing multi-period portfolios with a bankruptcy control approach. The goals of optimizing the multi-period portfolios include: 1- maximizing the expected outflow of the investor 2- Minimizing accumulated More
In this research, a mathematical model has been presented for optimizing multi-period portfolios with a bankruptcy control approach. The goals of optimizing the multi-period portfolios include: 1- maximizing the expected outflow of the investor 2- Minimizing accumulated risk 3- Minimizing the uncertainty of the portfolio''''s returns during the investment period, that achievement of these three objectives has been evaluated by two limits of bankruptcy control and the maximum and minimum adjustments of investment amounts during the investment period. The Hybrid Particle Swarm Optimization (Hybrid PSO) algorithm has been considered as the proposed solution for solving the model and a practical example has been presented to illustrate the application of the proposed model, which includes a portfolio with 17 different types of stocks from the companies listed in Tehran Stock Exchange For the three-year period from 2014 to 2016, the daily returns of these companies have been used as inputs for the model. Three different modes for the weights of the goals of optimizing the portfolio of multi- period portfolios have been determined using the sensitivity analysis table. In the end, the state of investment, which the investor equates to all three goals of optimizing the weight, has been the most suitable state for optimizing a multi-period of portfolios. the results has been compared with other algorithms Experimental results have shown that the algorithm proposed by this research for solving the model has been more appropriate than other algorithm.
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In this paper, a bi-objective mixed integer linear programming (BOMILP) model is developed for a pharmaceutical supply chain network design (PSCND) problem. The model helps to make several decisions about the strategic issues such as opening of pharmaceutical manufactur More
In this paper, a bi-objective mixed integer linear programming (BOMILP) model is developed for a pharmaceutical supply chain network design (PSCND) problem. The model helps to make several decisions about the strategic issues such as opening of pharmaceutical manufacturing centers and main/local distribution centers along with optimal material flows over a mid-term planning horizon as the tactical decisions. It aims to concurrently minimize the total costs and flow times as the first and second objective functions. Since the critical parameters are tainted with great degree of epistemic uncertainty, a robust possibilistic programming approach is used to handle uncertain parameters. In order to verify and analyze the proposed model, it is tested on a real case study according to Iran’s National Organization of Food & Drug’s data. Finally, a well-known multi-objective decision making (MODM) techniques i.e. the ɛ-constraint method is applied to yield both trade-off surface and final preferred compromise solution for the real case study whose results were also comprehensively analyzed.
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تحلیل پوششی داده ها به عنوان یک روش غیرپارامتریک برای اندازه گیری کارایی مجموعه ای از واحدهای تصمیم گیری همواره مورد توجه می باشد . عدد کارایی حاصل از مدل های استاندارد ، معیاری برای مقایسه عملکرد هر واحد تصمیم گیری با بقیه واحدها می باشد. علی رغم نقاط قوت فراوان این مد More
تحلیل پوششی داده ها به عنوان یک روش غیرپارامتریک برای اندازه گیری کارایی مجموعه ای از واحدهای تصمیم گیری همواره مورد توجه می باشد . عدد کارایی حاصل از مدل های استاندارد ، معیاری برای مقایسه عملکرد هر واحد تصمیم گیری با بقیه واحدها می باشد. علی رغم نقاط قوت فراوان این مدل ها، از نقاط ضعف آنها می توان به عدم تمایز بین واحدهای کارا اشاره کرد. همچنین، این مدل ها به ساختار داخلی واحدها توجه نمی کنند و دیدگاه جعبه سیاه دارند. در جهت رفع این مشکلات، مدل های تحلیل پوششی داده های نسبتی که هم از لحاظ زمان و هزینه بسیار مقرون به صرفه تر است مورد استفاده قرار می گیرند؛ اما این مدلها ایستا هستند و زمان را در ارزیابی لحاظ نمی کنند. دراین مقاله، روشی برای رتبهبندی واحدهای تصمیمگیری با ساختار شبکه دو مرحلهای چند زمانی با استفاده از تحلیل پوششی دادههای نسبتی پیشنهاد میشود. با استفاده از مدلهای تحلیل پوششی دادههای نسبتی، سه دیدگاه متفاوت برای ارزیابی کارایی در دورههای زمانی معرفی میشود و متناظر با هر دیدگاه، یک عدد کارایی برای هر واحد تصمیمگیری به دست میآید. سپس، سه مقدار کارایی منتج از سه روش مذکور، با استفاده از روش آنتروپی شانون با یکدیگر ترکیب شده و یک معیار کارایی کلی برای هر واحد تعریف میشود. این معیار در نهایت به عنوان شاخص اصلی برای رتبهبندی واحدها درنظر گرفته میشود. نتایج اجرای الگوریتم پیشنهادی بر روی مثال واقعی و مقایسه آن با نتایج روشهای مشابه، قوتاین الگوریتم را آشکار میسازد.
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The purpose of the present research is to provide a multi-period portfolio optimization model in a fuzzy credibility environment, aimed for end-of-period wealth maximization and risk minimization. The investor’s risk was measured using the Average Value at Risk (A More
The purpose of the present research is to provide a multi-period portfolio optimization model in a fuzzy credibility environment, aimed for end-of-period wealth maximization and risk minimization. The investor’s risk was measured using the Average Value at Risk (AVaR) as a coherent risk measure.
The model is designed in such a way that, in addition to considering transaction costs, the investor will have the opportunity to allocate part of his wealth to a risk-free asset. In designing the model, in addition to the cardinality constraints, constraints such as the minimum “proportion entropy” (as the portfolio of diversification degree) and the expected returns of the portfolio in each period are considered.
The results of the model running by MOPSO algorithm indicated that the model objectives in the optimum portfolios were better suited than those when the model was run with random weights. The results also indicated that an increase in the portfolio diversification degree reduced the amount of the final wealth.
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