Scientia Iranica
Volume:29 Issue: 3, May & Jun 2022

This paper presents a novel approach for the statistical monitoring of online social networks where the edges represent the count of communications between ties at each time stamp. Since the available methods in the literature are limited to the assumption that the set of all interacting individuals is fixed during the monitoring horizon and their corresponding attributes do not change over time, the proposed method tackles these limitations due to the properties of the random effects concepts. Applying appropriate parameters estimation technique involved in a likelihood ratio testing (LRT) approach considering two different statistics, the longitudinal network data are monitored. The performance of the proposed method is verified using numerical examples including simulation studies as well as an illustrative example.

Traditionally the performance of firms is evaluated by financial criteria, but this study presents a new qualitative comprehensive framework that incorporates the ethical criteria into the portfolio models and it is widely matched with the preferences of socially responsible investors. The increase of corporate deceptions has caused investors or fund managers consider the ethical assessments in their investment management. Therefore, it is essential to develop models that capture the ethical criteria along with the financial criteria in the investment processes. In this study, a multi-stage methodology is proposed and linguistic Z-numbers are applied to represent the evaluation information, and the Muirhead mean (MM) aggregation operators are employed to fuse the input data under linguistic Z-number environment. Hence, we firstly develop four linguistic Z-number Muirhead mean operators. Then, using the max-score rule and the score-accuracy trade-off rule, three qualitative portfolio models are proposed. These models have been aimed to maximize the financial performance of portfolio as main objective and have been distinguished by the ethical goal that the investor follows. The obtained results of numerical example validates the capability of the models for constructing more diversified portfolio based on a trade-off between financial and ethical criteria according to investors’ preferences.

The advent of new technologies, globalization of markets, customers' varied needs, and increasingly fierce competition, is making SMEs seek to improve engagement with their suppliers and cost management practices in order to survive. Thus, SMEs clearly need to focus on the interests of the entire supply chain by enacting win-win policies. In this study, we investigate a two-level inventory model featuring a manufacturer and a buyer in the competitive market with the policy of producing new products. Imperfect quality products and the capacity to rework are also considered in the model. In other words, because of the competitive nature of the market, any increase in price leads to a decrease in demand. The mathematical model is proposed over two scenarios: a scenario where shortage can occur, and one with no possibility of shortage. The objective function of the mathematical model revolves around the central goal of maximizing the total profit of the supply chain considering both independent and joint optimization by the supply chain members. A new algorithm is proposed to solve the mathematical model whose applicability is evaluated by giving a numerical example to the analysis software MATLAB. The final results are analyzed and discussed using sensitivity analysis approach.

This research address the optimization of product portfolio problem under uncertainty using the principles of financial portfolios theory. Since the success of the product portfolio is a strategic decision and it depends on the return’s future changes, the return is best to be considered as an uncertain parameter. The innovation of this research is the use of robust optimization approach and providing an exact solution algorithm based on the model of Bertsimas and Sim. Given the assumption of uncertainty in the returns, the product portfolio model is developed based on the robust counterpart formulation of Bertsimas and Sim. An exact solution algorithm is also presented to reduce the solution time. The results obtained by implementing in a real case study of the dairy industry in Iran show that increasing the confidence level decreases the portfolio’s total returns and increases its total risk. A comparison between the proposed algorithm and similar methods shows that, on average, it makes 3% improvement in the solution time.

Assessing the performance of health systems assists health decision-makers to ensure the accountability for their decisions. Medical diagnostic laboratories are one of the most important and sectors in the healthcare system of all countries. Thus, an assessment of the performance of medical diagnostic laboratories is of particular importance. This paper aims to propose a network data envelopment analysis (NDEA) model to assess the performance of medical diagnostic laboratories and decomposing the efficiency of the system with intricate internal structure based on sustainable development indicators. In addition, the proposed model is designed according to the internal structure of the medical diagnostic laboratory, which includes three main laboratory processes (the pre-test, the test and the post-test) with a combination of additional inputs and outputs (including both desirable and undesirable). The proposed model is a multiplicative DEA approach to estimate and decompose the efficiencies of system. Thus, a heuristic method is used as a suitable solution to convert a multiplicative NDEA approach into an equivalent linear program. The performance proposed model is shown through a real study in Iran. The computational results demonstrate the applicability of the proposed model in determining the most efficient laboratory using undesirable sustainability indicators.

Measuring the performance of laboratories as one of the most significant areas of healthcare plays a key role in the quality of laboratories management. In this paper, we consider a three-stage network comprised of a leader and two followers in respect to the additional desirable and undesirable inputs and outputs. The suggested model simulates the internal structure of a diagnostic lab (the pre-test, the test and the post-test). The criteria for evaluation are achieved by using the Fuzzy Delphi technique. Due to the social, economic and environmental impacts of health care systems, the significance of sustainability criteria is obvious in the case study indicators. We utilize the non-cooperative approach multiplicative model to measure the efficiency of the overall system and the performances of decision-making units (DMUs) from both the optimistic and pessimistic views. The non-cooperative models from these view cannot be converted into linear models. Therefore, a heuristic method is suggested to convert the nonlinear models into linear models. Finally, after obtaining the efficiencies based on the double-frontier view, the DMUs are ranked and classified into three clusters by the k-means algorithm.

In the current situation,taking into consideration the environmental and social issues are related with the production and distribution of products in supplychain systems,due tothe global concerns related with emitting lots of greenhouse gaseswithin the manufacturing process and overlooking the major needs of publicThis paper proposes a newmultiobjectivemodel in the area ofclosed loop supplychainproblem integrated with lot sizing by considering lean,agility and sustainability factors simultaneously.In this regard,responsiveness, environmental,social and economic aspects are regarded in the model besides the capacity and service level constraints.Inaddition,strategic and operational backup decisions are developed to increase the resiliency of the system against disruption of the facilities and routs simultaneously.Next,a robust possibilistic programming approach is applied to handle the uncertainty of the model.To increase the responsiveness of the system,a fuzzyc-means clusteringmethod isapplied to select the potential locations based on the proximity to local customers.In the following, a new hybrid metaheuristic algorithm comprised of a PMOPSO algorithm and aMOSEO is developed to deal with large size problems efficiency and to assess the impact of using a single-based initial solution as the income for the second phase of the proposed hybrid algorithm.To ensure about the effectiveness of the proposed hybrid algorithm,the results of this algorithm arecompared with a NSGA-II.

Traditional ordinary least square (OLS) regression is commonly utilized to develop regressionratio-type estimators with traditional measures of location. Abid et al. (2016b) extended this idea anddeveloped regression-ratio-type estimators with traditional and non-traditional measures of location. In this article, the quantile regression with traditional and non-traditional measures of location is utilized and a class of ratio type mean estimators are proposed. The theoretical mean square error (MSE) expressions are also derived. The work is also extended for two phase sampling (partial information). The pertinence of the proposed and existing group of estimators is shown by considering real data collections originating from different sources. The discoveries are empowering and prevalent execution of the proposed group of estimators is witnessed and documented throughout the article.

Considering the two-way spillovers of market information, this paper establishes multivariate GARCH models to study the impact of Shenzhen-Hong Kong Stock Connect (SHSC) on the complex co-movements relation between the stock markets of Shenzhen and Hong Kong from the aspects of dynamic correlation and volatility spillover. On the one hand, a t-Copula DCC-GARCH model which combines the Copula function with the DCC-GARCH model is established to model the return rate series of stock index in different stages, and the characteristic that the dynamic correlation coefficient changes with time is analyzed emphatically. On the other hand, a BEKK-GARCH model is established to measure the changes of the volatility spillover effect between the stock markets in Shenzhen and Hong Kong. The results show that the opening of SHSC has gradually increased the dynamic correlation coefficient of the two stock markets, and the openness degree of the two stock markets has increased. At the same time, the volatility spillovers of stock markets in Shenzhen and Hong Kong have shifted from one-way spillover to two-way spillovers, which indicates that the SHSC mechanism has strengthened the correlation degree and has improved the ability of risk spillover in the two stock markets.

Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) is considered a significant topic in project management studies and many kinds of research have been carried out in this field which have been proposed numerous approaches. However, in most of them, the viewpoints of clients and contractors, two important stakeholders of the project are not directly considered. The current research tries to introduce a new approach of RCMPSP in order to schedule the project portfolio and allocate the budget as a limited resource simultaneously. In this way, first, the client's and contractors' budget satisfaction is defined. Then some budget allocation models have been proposed to maximize the clients' and the contractors’ satisfaction. These models consider constraints such as the minimum cost required for each project, the maximum budget for each period, and the flexibility for the start date of each project. To illustrate the proposed models, a real case of the project portfolio is considered.

Recently, the researchers in the field of urban transportation network planning have become increasingly interested in network reliability, publishing research works focused on the calculation of various types of network reliability. Accurate calculation of network reliability has led the transportation network optimizers toward new approaches . Travel time reliability is among the most important reliabilities investigated when analyzing urban transportation networks, with various approaches based on different assumptions proposed for calculating it. In the present research, the uncertainty associated with the demand for travel and the flows passing across links and also the correlations among the links comprising a route were considered to calculate the travel time for each of the network links. Moreover, it was shown that this process follows shifted log-normal distribution. These calculations are expected to serve as a basis for the employment of travel time reliability of a network within the modeling of an urban transportation system, so as to increase the accuracy and reliability of the simulations. Finally, in order to validate the model, an urban network with 12 nodes, 21 links, and 4 origin-destination pairs was subjected to the travel time reliability assessment by calculating the travel time over all forming links.

Nowadays, because of the advancement of technology and subsequently unpredictable events, it is important for addressing risk management as an important part of projects and business. In this paper, a novel approach based on Monte Carlo simulation has been proposed for risk assessment, which considers the co-occurrence of risks. In this method, the output of extended and classic Monte Carlo simulation is applied for co-occurrence-based risk assessment (CORA) and prioritization. Also, the magnitude in each source of uncertainty has been determined by a new approach. The proposed model investigates risk’s relationship and determines the type of effect as resonance or reduction in addition to identifying and analyzing the risks. Also, a system dynamic model is applied to illustrate the relationships of risks. Finally, this method is applied to a petrochemical project. Five risks as temperature, rain, labor, cost, and inflation are considered in this project. Based on the numerical results, the most important risk is inflation. Also, there is a significant different between the result of the proposed model in comparison with model that ignore the co-occurrence of risks. CORA helps the manager to consider all aspect of risks and help them to have a better decision.