فهرست مطالب kamran shahanaghi

Since 1952, when the mean-variance model of Markowitz introduced as a basic framework for modern portfolio theory, some researchers have been trying to add new dimensions to this model. However, most of them have neglected the nature of decision making in such situations and have focused only on adding non-fundamental and thematic dimensions such as considering social responsibilities and green industries. Due to the nature of stock market, the decisions made in this sector are influenced by two different parameters: (1) analyzing past trends and (2) predicting future developments. The former is derived objectively based on historical data that is available to everyone while the latter is achieved subjectively based on inside-information that is only available to the investor. Naturally, due to differences in the origin of their creation the bridge between these two types of analysis in order to optimize the portfolio will be a phenomenon called "ambiguity". Hence, in this paper, we revisited Markowitz's model and proposed a modification that allow incorporating not only return and risk but also incorporate ambiguity into the investment decision making process. Finally, in order to demonstrate how the proposed model can be applied in practice, it is implemented in Tehran Stock Exchange (TSE) and the experimental results are examined. From the experimental results, we can extract that the proposed model is more comprehensive than Markowitz's model and has greater ability to cover the conditions of the stock market.

With increasing life expectancy, aging in Iran becomes more feminine. This study aimed to prioritize the criteria affecting the health of Iranian elderly women.

 In this descriptive-analytical study, factors affecting the health of the elderly women, with the opinion of 20 health-policy and health services management experts employed in governmental and non-governmental organizations, were approved. In the second phase, the weight and rank of these criteria were calculated and analyzed using Hierarchical Analysis Process. Excel software was used to analyze data.

 The analysis showed that the individual factor is the most important in terms of factors affecting the health of elderly women. In terms of the type of interventions, the provision of preventive health services, in terms of decision-making, executive interventions at the queue level, in terms of planning, integrated approach, and continuous planning with the participation of public and private sectors were ranked first.

 Given the influence of the previous periods of life on healthy elderly, decision-makers should prioritize preventive services in all periods of life regarding age and gender context. Interventions should be presented with an integrated approach with continuous planning at the levels of government, organization, and queue, as well as with the participation of both public and private sectors. The existence of an information management system, supervision, and evaluation, provides the ground for continuous feedback and improvement of programs. Therefore, it is recommended that the government play a more significant role in ensuring the health of the elderly.

Despite the passing of more than 30 years from introducing the UTilitès-Additives (UTA) method and its extensive presentation in academic communities, this method is still not very popular among portfolio managers. Many portfolio managers still question the usefulness of the UTA method and prefer to rely on other multi-criteria decision making (MCDM) approaches. Therefore in this study, we examined the features of one of the most popular variants of the UTA methods, called UTASTAR, and on this basis, we have been developed this traditional approach in such a way that it would have more ability to meet the expectations of portfolio managers. In this way, to demonstrate how the proposed method can be applied in practice it is implemented in Tehran stock exchange (TSE) and to validate its efficiency, we designed an experiment, which is a novel approach in operations research but common in psychology and experimental economics. From the experimental results, we can extract that the outstanding features of the proposed method, compared to the original UTASTAR method are as follows: (1) it can provide a more accurate estimation of the portfolio managers’ attitude because in addition to the sequential preferences of the alternatives it also considers the relative preferences; (2) it has always feasible solutions although it requires more comparison data and (3) it allows portfolio managers to observe the inconsistency of their decisions and take corrective action if desired.0

Studies have indicated the feminization of aging in the world including Iran. The present study aims to investigate women’s aging trend and effects, status difference, factors affecting the health of older women, and how to achieve proper health for older women in Iran.

Methods & Materials: 

This review study was conducted according to PRISMA checklist. A comprehensive search was conducted in online databases including PubMed, Scopus, Elsevier, IranMedex, IranDoc, MagIran, NoorMags, SID, Google Scholar, and websites of the World Health Organization and the United Nations Population Fund on articles published until June 2020 using the keywords Elderly, Aged, Healthy Aging, Older Women and Health Services in English and Persian.

Increased population of older women and the feminization of aging is the result of the development and success of the health system. Poor aging management is a serious threat to health care, socio-economic and sustainable development systems. The differences in economic, social, demographic, and health indicators were more unfavorable in older women compared to older men due to different effects of health factors on women. The geriatric programs in Iran are often temporary without participation of the elderly, and are for managing individual, clinical, and case conditions. Provision of integrated care services is only at the first level of health and government services and lacks strategic, gender, and inter-sectoral approaches.

 Achieving older women’s health requires policy-making and planning at the levels of government, organization and service delivery with a gender justice approach. Interventions to achieve the goal should be elderly-centered, interactive, multi-disciplinary, continuous, participatory, coordinative at clinical, organizational and systemic levels with analysis, feedback, integrated information system, research and development. Training and employment of multi-skilled manpower considering the different needs of older women are recommended.

The occurrence of disruptions has undeniable impacts on supply chain (SC) performance and severely affects its costs and revenues. SC resilience (SCR) reduces the impacts of these disruptions. Among the issues in the SCR, although the recovery of the SC after the disruption is of vital importance, it has not been considered as it should be. To fill this gap, this paper enumerates some important issues in SC recovery planning and proposes a dynamic model for it. One of the features of the proposed model is to consider the recovery time and cost in order to achieve the pre-disruption SC performance. Then, we demonstrate the application of this model in the recovery of a two-echelon poultry SC. Since the developed model is a nonlinear dynamic model, we use the direct collocation method to solve it. The outputs of the sensitivity analysis show that changes in many parameters result in significant changes in model variables. Based on the results, it can be said that the development of appropriate models for recovery plays an important role in the analysis of possible alternatives for SC recovery and can help SC managers to deal with disruptions by comparing alternative recovery options.

Real time and accurate fault detection has attracted an increasing attention with a growing demand for higher operational efficiency and safety of industrial gas turbines as complex engineering systems. Current methods based on condition monitoring data have drawbacks in using both expert knowledge and quantitative information for detecting faults. On account of this reason, this paper proposes an online fault detection and isolation method based on belief rule base (BRB), which can deal with modeling behavior of complex systems when semi-quantitative information is available. Although it is difficult to obtain accurate and complete quantitative information, some expert knowledge can be collected and represented by a BRB, which is essentially an expert system. As such, a new BRB based diagnosis model is proposed to detect and isolate faults of the system in real-time when aiming at isolating various damages and determining the severity of each. Moreover, a recursive algorithm is developed for online updating the parameters of the fault diagnosis model. Equipped with the recursive algorithm, the proposed diagnosis model can determine the severity of fault in real-time when two types of faults are dependent and competitive. To prove its potential application, experimental results demonstrate that the proposed model can track the fault severity very well, and the faults can be diagnosed accurately in real time. Thus, R2 values of 0.99782 and 0. 99782 were obtained for the fault estimation of fouling and erosion, respectively, indicating the accurate performance of the proposed model.

In the most past hub location problem (HLP) studies, failure probability is limited to a constant value; while in reality, it depends on various factors including natural disasters such as floods and earthquakes, commodity flow, institutional elements such as strikes, and etc. This paper looks into the problem of hub network design through a new model. The problem is to maximize the network reliability and to minimize the total routing cost simultaneously. In order to provide a more realistic model, the hub’s failure probabilities were considered as a function of the hub’s incoming traffic. Reserve hub elements are also taken into account in the model with the aim of increasing network reliability. To solve the model, a non-dominated sorting genetic algorithm (NSGA-II) is presented. The parameters of the algorithm are tuned using Taguchi method. The proposed solution is then carried out in a case study where the results confirm the acceptable performance of the model and also sensitivity analysis is performed in order to describe the effects of critical parameters into objective functions.

Determination of degradation status and estimating remaining useful life are two main activities in prognostics and health management. These two main activities can be perceived as the problem of multi-sensor data fusion. These sensors contain information including speed, pressure, and temperature. In terms of evidence theory, the information obtained from each of these sensors can be regarded as a part of the evidence and determination of degradation status and estimating remaining useful life based upon this information can be considered as the problem of multi-sensor data fusion. In this article, the Dempster-Shafer theory has been employed as a tool for modeling and multi-sensor data fusion as an indicator of the health status of the turbofan. In this regard, initially, the theory of evidence has been reviewed and then how to model the remaining useful life estimation problem by multi-sensor data fusion within the framework of the concepts of this theory including mass function, focal elements and rules of evidence composition is explained. This paper has introduced a new approach for combining the results of different estimates of remaining useful life through determining the weighs. Furthermore, it has offered two different scenarios to specify the similarity between the system under study and the available evidence. Finally, to appraise the proposed method, the turbofan engines data set (C-MAPSS) has been used as it has been extensively addressed in literature by researchers. According to the results, the proposed method revealed better score and performance compared with other available methods in the literature.

Estimation of remaining useful life (RUL) is one of most interesting subjects in prognostic and health management. Performing an analysis of the results of such estimation can increase the reliability and the safety of the system, and reduce the unnecessary costs. In this paper, a similarity-based combination method is proposed to combine several run-to-failure historical datasets in order to directly estimate the RUL. In this method, reference datasets are clustered and the initial RUL is calculated based on the artificial neural networks trained by the reference datasets. By using the extended Dempster-Shafer, the similarity between the initial RUL and the average RUL for each dataset is obtained. The proposed methodology is tested and validated on Commercial Modular Aero-Propulsion System Simulation (C-MAPSS), test-bed developed by NASA. The results of the evaluation show that the proposed method outperforms other methods in the literature.

One of the main challenges facing project managers is the evaluation of project progress and its earned value as well as the estimation of cost and time in project completion. In this paper, we attempt to develop a method which is based on confidence limits. The proposed method is of two approaches which distinguish the risks of activities implementation and the critical path in determination of confidence interval for completion cost and time. The above approach has applied a p-factor for weight determination in confidence intervals. In the final part, we will discuss the ways of applying these proposed methods and their importance with an example and also demonstrate the superiority of the above methods over the unmodified ones in a confidence interval.

Production is a key economic activity with potential long-term social benefits that can be thoroughly realised only if governments comply with their duties towards domestic production. Governments are responsible for the production of sustainable agricultural products via appropriate allocation of subsidies and regulation of price policies that would help take advantage of the potentials underlying agricultural production. In this paper, a model is developed to investigate the interaction between two decision makers in the stackelberg game, government as leader and agriculture as follower, with the ultimate aim of providing benefits to all sectors in the society in the sustainable agriculture paradigm. The proposed model is validated and its efficiency demonstrated via a case study of cotton production as a strategic agricultural production. The model is first solved using a combination of fuzzy mathematical and grey quadratic programming methods to account for the inherent uncertainty in a number of problem parameters. The model is then analyzed against various government-producer interaction scenarios and finally, the analysis results are compared.

Passenger transferring between origins to destinations, regarding the matter of the variety of services are the main goal in a public transportation (air and ground systems). So, considering the different types of vehicles and increasing different levels of services are the essential issues in hierarchical hub location problems. As well as the effect of disruption (Deliberately and unintentionally), changing the scope of the connectivity in a distribution systems, locations, re–allocations and designing the sustainable network in the presence of disruption are the problems which are less reviewed in a hierarchical hub location field. In this paper a linear model used for designing a sustainable capacitated hierarchical hub location problems considering multi modes of transportation based on two scenarios, with or without any disruption. This model has been solved with GAMS 23.4.3 optimization software and Monte Carlo simulation method (MATLAB R2016a). Based on the results of the network without any disruption, the disruption probability obtained to be used in a network under disruption. The results show that, the model and the proposed solution method, can satisfy the demands in the presence of disruption, changing the location of non–central hub nodes and the re–allocation in each levels of hierarchy. Increasing the discount factors, disruptions and the number of non-central hub nodes increases the total costs of the hierarchical hub network. Also increasing the levels of services, considering the different paths and vehicles can expand the decision making ability in a network.

The life of many people in the world is under the danger because of the accidents and unpredicted diseases, which require relief. Since, most of such cases occur in unpredictable condition, for an appropriate planning, the uncertain conditions should be investigated. Consequently, this paper investigates the integrated and multi-step process of locating, assigning and routing the air and land emergencies in the uncertain conditions in order to reach the stable solution that faces minimum changes against different conditions. In the presented model, the emergency demand is defined in the stochastic and fuzzy environment in order to show the actual conditions. The findings indicate that the models intend to create an optimum balance between cost minimization and increasing the demand satisfaction rate. However, in general conditions, the decisions should be made considering the exisitng conditions.

In every production plant, it is necessary to have an estimation of production level. Sometimes there are many parameters affective in this estimation. In this paper, it tried to find an appropriate estimation of production level for an industrial factory called Barez in an uncertain environment. We have considered a part of production line, which has different production time for different kind of products, which means both environmental and system uncertainty. To solve the problem we have simulated the line and because of the uncertainty in the times, fuzzy simulation is considered. Required fuzzy numbers are estimated by the use of bootstrap technique. The results are used in production planning process by factory experts and have had satisfying consequences. Opinions of these experts about the efficiency of using this methodology, has been attached.

This paper deals with the determination of machine numbers and production schedules in manufacturing environments. In this line, a two-stage fuzzy stochastic programming model is discussed with fuzzy processing times where both deterioration and learning effects are evaluated simultaneously. The first stage focuses on the type and number of machines in order to minimize the total costs associated with the machine purchase. Based on the made decisions, the second stage aims to schedule orders, while the objective is to minimize total tardiness costs. A dependent-chance programming (DCP) approach is used for the defuzzification of the proposed model. As the resulted formulation is a NP-hard problem, a branch and bound (B&B) algorithm with effective lower bound is developed. Moreover, a genetic algorithm (GA) is proposed to solve problems of large-sizes. The computational results reveal the high efficiency of the proposed methods, in particular the GA, to solve problems of large sizes.

The Transfer Point Location Problem is about locating optimum transfer point between the facility and a set of demand points, such that the maximum distance or the sum of the distances between the customers and the facility through the transfer point is minimized in certain environment. Thus, in this thesis the goal is to construct the modeling of the aforesaid problem, in case of multi objectives with respect to locating the single or multiple transfer point(s), in the certain environment and network topology when one or more facility exist. The objectives are about minimizing total cost of transfer points set up and transportation, minimizing total time of transfer and maximizing demand covering. In addition, due to high computational complexity of problem for acquiring a solution near to optimum in limited time, one type of proposed hybrid genetic algorithm is used. At last, the validation and the application of the developed model in certain environment are shown by a case study of ground wheat distribution system in Andimeshk of Khozestan.

Natural disasters, such as earthquakes, affect thousands of people and can cause enormous financial loss. Therefore, an efficient response immediately following a natural disaster is vital to minimize the aforementioned negative effects. This research paper presents a network design model for humanitarian logistics which will assist in location and allocation decisions for multiple disaster periods. At first, a single-objective optimization model is presented that addresses the response phase of disaster management. This model will help the decision makers to make the most optimal choices in regard to location, allocation, and evacuation simultaneously. The proposed model also considers emergency tents as temporary medical centers. To cope with the uncertainty and dynamic nature of disasters, and their consequences, our multi-period robust model considers the values of critical input data in a set of various scenarios. Second, because of probable disruption in the distribution infrastructure (such as bridges), the Monte Carlo simulation is used for generating related random numbers and different scenarios; the p-robust approach is utilized to formulate the new network. The p-robust approach can predict possible damages along pathways and among relief bases. We render a case study of our robust optimization approach for Tehran’s plausible earthquake in region 1. Sensitivity analysis’ experiments are proposed to explore the effects of various problem parameters. These experiments will give managerial insights and can guide DMs under a variety of conditions. Then, the performances of the “robust optimization” approach and the “p-robust optimization” approach are evaluated. Intriguing results and practical insights are demonstrated by our analysis on this comparison.

Determining the best location to be profitable for the facility’s lifetime is the important decision of public and private firms, so this is why discussion about dynamic location problems (DLPs) is a critical significance. This paper presented a comprehensive review from 1968 up to most recent on published researches about DLPs and classified them into two parts. First, mathematical models developed based on different characteristics: type of parameters (deterministic, probabilistic or stochastic), number and type of objective function, numbers of commodity and modes, relocation time, number of relocation and relocating facilities, time horizon, budget and capacity constraints and their applicability. In second part, It have been also presented solution algorithms, main specification, applications and some real-world case studies of DLPs. At the ends, we concluded that in the current literature of DLPs, distribution systems and production–distribution systems with simple assumption of the tackle to the complexity of these models studied more than any other fields, as well as the concept of variety of services (hierarchical network), reliability, sustainability, relief management, waiting time for services (queuing theory) and risk of facility disruption need for further investigation. All of the available categories based on different criteria, solution methods and applicability of them, gaps and analysis which have been done in this paper suggest the ways for future research.

In this paper, a complete review of published researches about hierarchical facility location and hub network problems is presented. Hierarchical network is a system where facilities with different service levels interact in a top-down way or vice versa. In Hierarchical systems, service levels are composed of different facilities. Published papers from (1970) to (2015) have been studied and a comprehensively classified and surveys is presented. Mathematical models are classified based on different properties such as: input, output, objective functions, constraints, applications, some of the real world case studies and solution methods. At the ends, according to classification, a conclusion based on the literature and the future research to tackle real world of hierarchical facility location problems and hierarchical hub network problems is presented. This study may be used as a comprehensive reference for researchers in the hierarchical facility location problems, particularly those of hierarchical location-based on hub networks.

In the last two decades due to globalization of supply chain, enormous changes occurred in chains. Leaders in this field, always facing new risks and disorders that should prepare themselves to deal with them. In the recent years, sanctions as one of the most serious risks have damaged the supply chains. However, in the research related to global supply chains, modeling of disruption has been neglected. The purpose of this study was the modeling of drug supply chain at the tactical level in terms of sanction impairing. Physical and financial flow and their disruption in the term of sanction have been modeled simultaneously. To complete the financial flow, financial statements have also been modeled. One of the newest approaches named Robust Possibilistic Programming has been used for the modeling of the uncertainty of the problem. The model has been implemented on a drug organization called Atra and the results have been analyzed and validated by the optimization software Gams. The results show a robust and resistant system in the case of sanction disruption that without violating the constraints of the problem shows the appropriate measures to deal with the problem.

Credit allocation through the usage of Portfolio optimization mainly seeks tomaximize return and minimize the risk of the portfolio; but there are other importantissues including sustainable development which is important for government/public sectors. This paper presents a novel credit allocation approach based on portfoliooptimization and investigates the effects of selected indicators of sustainabledevelopment on credit allocation. In order to evaluate this case study، constraintmean-variance was used as the extension of Markowitz portfolio theory. Selectedindicators were modeled as the mathematical model’s objectives and constraints. Inorder to show the applicability of the model، experimental results were given basedon credit allocation data for National Development Fund of Iran (NDFI). The resultsshow that sustainable development selected indicators exacerbate the return of NDFIportfolio from one side and from the other side، its effect on NDFI risk is somewhatsimilar but lighter.

Credit scoring is an important topic, and banks collect different data from their loan applicant to make an appropriate and correct decision. Rule bases are of more attention in credit decision making because of their ability to explicitly distinguish between good and bad applicants. The credit scoring datasets are usually imbalanced. This is mainly because the number of good applicants in a portfolio of loan is usually much higher than the number of loans that default. This paper use previous applied rule bases in credit scoring, including RIPPER, OneR, Decision table, PART and C4.5 to study the reliability and results of sampling on its own dataset. A real database of one of an Iranian export development bank is used and, imbalanced data issues are investigated by randomly Oversampling the minority class of defaulters, and three times under sampling of majority of non-defaulters class. The performance criterion chosen to measure the reliability of rule extractors is the area under the receiver operating characteristic curve (AUC), accuracy and number of rules. Friedman’s statistic is used to test for significance differences between techniques and datasets. The results from study show that PART is better and good and bad samples of data affect its results less.

Due to dynamic and complex nature of healthcare systems, the application of simulation for the planning, modeling and analysis of these systems has lagged behind traditional manufacturing practices. Rapid growth in health care system expenditures, technology and competition has increased the complexity of health care systems. Simulation is a useful tool for decision making in complex and probable systems.. The objective of this study was to provide a model for reducing outpatient waiting time by using simulation.. A simulation model was constructed by using the data of arrival time, service time and flow of 357 patients referred to orthopedic clinic of a general teaching hospital in Tehran. The simulation model was validated before constructing different scenarios.. In this study 10 scenarios were presented for reducing outpatient waiting time. Patients waiting time was divided into three levels regarding their physicians. These waiting times for all scenarios were computed by simulation model. According to the final scores the 9th scenario was selected as the best way for reducing outpatient''s waiting time.. Using the simulation as a decision making tool helps us to decide how we can reduce outpatient''s waiting time. Comparison of outputs of this scenario and the based- case scenario in simulation model shows that combining physician''s work time changing with patient''s admission time changing (scenario 9) would reduce patient waiting time about 73.09%..

Efficient disaster management resulting from natural disasters such as earthquake is one of the most challenging subjects of city management especially in the metropolis such as Tehran. In this regard, determination and the implementation of effective prevention strategies or reduce the consequence and damages caused by the earthquake in Tehran is considered as a requirement. But before implementation of any preventive strategy, evaluating their effectiveness on the basis of specified and appropriated criteria was necessary and limitations of time and existing resources for prioritization of these strategies will be also inevitable. By conscious of the importance of such work, in this research reviewing the literature of disaster management and interview with seven experts of disaster management in Tehran, extract the effective prevention strategies and consequences of possible earthquake in Tehran and with definition of appropriate standards, evaluate their effectiveness by decision-making techniques of fuzzy analytical hierarchy process (FAHP). For this, we used linguistic variables for comparison of strategies importance and weight of criteria in the form of paired comparison matrix and then we turn them into a fuzzy paired comparison matrix. Then by using the Chang decision-making technique of FAHP extract conclusive decision making matrix from a fuzzy group of paired comparison matrix. At the end, we prioritize strategies based on specified criteria and strategies with high priority are introduced for prevention and control of consequences of possible earthquake in Tehran.

Currently considering increasing investment in large projects by the private sector and implementation of projects with many techniques such as design, construction and financing plan with financing is very important. This paper is considering uncertainty in time and cost of various activities a project planning, financial resources for loans should be a way to finance a project cost is lowest. Time and cost estimates for this model using fuzzy variables possible, takes place, also an example of project design and construction of water plant that due to special conditions intrinsically design and implementation of events with the time and cost is inconclusive, separately engineering sectors, supply and installation of a fuzzy randomness space, then the model is based on the expected value approach, and most method chance considering the limited time the entire plan and prerequisite constraints, and modeling activities have been resolved. Solution on this method based on optimal combination of fuzzy random simulations and concept validation is using genetic algorithm.