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• The paper develops a new closed-loop hybrid system dynamics-optimization approach. • The system dynamics model and hybrid model are compared. • Energy production from waste is considered in the paper. • Social, environmental, and economic aspects are studied in the models. System dynamics approach is a tool to study socio-economic systems and make strategic decisions. However, it is not an appropriate planning method. For this purpose, optimization models are more powerful options. This paper represents a new combination of a system dynamics simulation with an optimization model to consider both aspects. For this purpose, a system dynamics model is developed to simulate the system in a feed-forward path. Then, it is connected to an already developed multi-objective optimization model on the feedback path in a real-time manner to constitute a hybrid closed-loop model. The hybrid model is applied to study the waste management system in Tehran. The focus of the hybrid model is on waste-to-energy technologies and how the system decides to install them. Comparing the hybrid model's results with those of the system dynamics model proves that the optimized policies considerably improve the system financially and environmentally. As far as technologies are concerned, the hybrid model suggests more capacities and diversity of technologies rather than the open-loop system dynamics model. Overall, the framework can be applied to all decision-making models that can add an advantage to system dynamics models. Moreover, the system dynamics model obtained through several scenarios shows that raising awareness, increasing Feed-in-Tariff, and investment in waste-to-energy technologies positively affect the waste management system. [ABSTRACT FROM AUTHOR]

In this paper, a new approach is proposed for the system identification of the stock returns. Allocating an accurate model to the nonlinear and stochastic financial system, which can accurately manifest the system, is considered one of the critical issues in control and system engineering. We employ an autoregressive with exogenous variable (ARX) process and propose a new model that accurately exhibits the stock returns system dynamics. The proposed model can identify the system with more than 96% accuracy. Furthermore, one of the indispensable approaches in the financial market, which presents a significant investment role, is forecasting the trends accurately. Therefore, we propose and implement a new algorithm, multiple adaptive forecast (MAF), based on adaptive control systems, estimation, and stochastic processes to detect the future trends of prices. In addition, we study some companies' stocks from Tehran Stock Exchange in this paper. Simulation results indicate that the proposed model obtains more accurate outcomes than the two other models. Ultimately, as inferred from the simulation results, employing the proposed model combined with the MAF algorithm would yield more competent outcomes for creating the portfolio for short-term investment. [ABSTRACT FROM AUTHOR]

• Designing a two-echelon blood bank network based on ABO/Rh substitution priority. • Considering the RBC inventory level in the HBB and BC simultaneously as a CTMC. • Modeling the (r , Q) inventory policy for all RBC types as eight dependent 2D-CTMCs. • Creating an imperfect queueing-inventory system in HBB integrating with eight CTMCs. • Developing a screening system in SL with false positive and false negative errors. The uncertain nature of blood supply and demand in a blood supply chain network (BSCN) makes it impossible completely meet the blood demands on time. That's why there is a vital need to develop a comprehensive model for optimally managing blood networks considering all challenges they face with. Although many researches have studied blood inventory management (BIM) in BSCNs, there is still a lack of models simultaneously considering blood inventory levels in hospital blood banks (HBBs) and blood centers (BCs) as a stochastic process. Also, most models have not evaluated the impact of false positive (FP) and false negative (FN) errors in the screening laboratories (SLs) of BCs. Moreover, no study has modeled the HBB demands as a queueing-inventory model with multiple blood types according to ABO/Rh substitution priority. To fill these gaps, this paper develops a two-echelon blood bank network considering ABO/Rh factors and substitution priority in which inventory levels of all red blood cell (RBC) types in an HBB and BC are simultaneously modeled as eight dependent continuous-time Markov chains (CTMCs). An unobservable M/M/m queueing-inventory system with infected RBCs and (r , Q) policy in the HBB is proposed. The blood requests arrive at the requests queueing system in the HBB, and after processing and departing from the queue, they leave the HBB with exactly-one RBC unit with the same ABO and Rh (Rhesus) groups or compatible with other types of RBC. Lost sales occur if all possible types of RBC for transfusion are unavailable. HBB continuously checks each RBC type's inventory level and places an order to the BC once the inventory level of each one of them reaches a predetermined reorder point. Due to transfusion-transmissible infections (TTIs), an SL conducts a 100 % quality-assured screening in the BC modeled as an M / M / m queueing system. Due to unreliable inspectors, FP and FN errors are considered in the SL. The stationary distribution of the number of requests at the HBB, the number of RBCs in the SL, and joint stationary distributions of inventory level of all RBC types in the HBB and BC are derived. Afterward, each RBC type's waiting-time distribution in the HBB is obtained. Some performance measures and the long-run total cost are extended, and a mixed integer nonlinear programming model (MINLP) is presented. Then, we propose the grasshopper optimization algorithm (GOA) to solve the complicated model. Also, this model is implemented in a case study in Tehran. Results show RBCs types O + and A + have the most lost sales, and the management should absorb more donors with these blood groups. Afterward, an approximation method to deal with multiple HBBs is suggested. Finally, several remarkable managerial and practical implications are presented. [ABSTRACT FROM AUTHOR]

• Reveals the role of alternative optimal solutions for portfolio selection using DEA cross-evaluation. • Improves the result of the mean-variance portfolio selection method. • Identifies superior stocks in the Tehran stock market. This paper deals with the role of alternative optimal solutions existing in the data envelopment analysis (DEA) models for cross-efficiency evaluation in portfolio selection. The paper shows that incorporating alternative optimal solutions for constructing cross-efficiency matrix improves the result of the mean-variance portfolio selection method. This improvement means that building portfolios with lower risk and higher expected return is possible when alternative optimal solutions are considered. The proposed method in this paper is applied to stock portfolio selection in the Tehran stock market. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a hybrid model which combines Extended Kalman Filter (EKF) and Genetic Programming (GP) for forecasting of water demand in Tehran is developed. The initial goal of the current work is forecasting monthly water demand using GP for achieving an explicit optimum formula. In the proposed model, the EKF is applied to infer latent variables in order to make a forecasting based on GP results of water demand. The available dataset includes monthly water consumption of Tehran, the capital of Iran, from 1992 to 2002. Five best formulas based on GP results on this dataset are presented. In these models, the first five to three lags of observed water demand are used as probable and independent inputs. For each model, sensitivity of the results for each input is measured mathematically. A model with the most compatibility of the computed versus the observed water demand is used for filtering based on EKF method. Results of GP and hybrid models of EKFGP demonstrate the visible effect of observation precision on water demand prediction. These results can help decision makers of water resources to reduce their risks of online water demand forecasting and optimal operation of urban water systems. [Copyright &y& Elsevier]

Abstract: The key issue for decision making in stock trading is selection of the right stock at the right time. In order to select the superior stocks (alternatives) for investment, a finite number of alternatives have to be ranked considering several and sometimes conflicting criteria that often are vague and have uncertainty conditions. Therefore, we are faced with a special Multiple Criteria Decision Making (MCDM) problem. The purpose of this paper is to develop a fuzzy expert system for selecting superior stocks in order to encounter the uncertainty of stock portfolio recommendation and model the recommendation rules which experts at Tehran Stock Exchange (TSE) use for portfolio recommendation. The results of implementing the designed fuzzy expert system at TSE were affirmative. [Copyright &y& Elsevier]

Abstract: The well-known fuzzy rule-based Takagi–Sugeno–Kang (TSK) model is combined with a set of fuzzy regressions (FR) to investigate the impact of the climate change on the electricity consumption duration. The electricity demand forecasts in the short-terms have a vital application in electricity markets. Knowing that the energy is a product of the relation between the climate change and the average consumption duration of the peak load. The paper introduces a type III TSK fuzzy inference machine combined with a set of linear and nonlinear fuzzy regressors in the consequent part to model effects of the climate change on the electricity demand. However, a simplified version of the model is applied to daily data of the average temperature in Tehran, 2004. First, based on an initially fitted nonlinear curve, an optimization model is employed to cluster data into three groups of cold, temperate and hot. The fuzzy data have been expanded to reduce the temperature volatile property. Then the relation is estimated by the fuzzy regressions in company with the TSK model. Numerical results show high efficiency of the proposed combined fuzzy model, as well as a minor decrease in the average absolute error. [Copyright &y& Elsevier]