This paper provides an integrated model for a two-stage assembly flow shop scheduling problem and distribution through vehicle routing in a soft time window. So, a mixed-integer linear programming (MILP) model has been proposed with the objective of minimizing the total cost of distribution, holding of products, and penalties of violating delivery time windows. To solve this problem, an improved meta-heuristic algorithm based on whale optimization algorithm (WOA) has been developed. A comparison of the integrated and non-integrated model in a case study of industrial gearboxes production shows that the integrated model compared to the non-integrated model has saved 15.6% and 13.6% in terms of delay time and total costs, respectively. Computational experiments also indicate the efficiency of improved WOA in converging to optimal solution and achieve better solution in comparison to the genetic algorithm (GA).

The aim of this study was to present and evaluate the effectiveness of an integrated model of emotionally focused approach and Gottman model on feeling of loneliness in married women.
For this study, quasi-experimental design with pre-test, post-test, follow up and control group was used. The study population included married women who referred to Tehransar health center. The sample consisted of 26 volunteer participants, which were selected using convenience sampling method, in two groups, including experimental (n = 13) and control (n = 13) groups. All participants were asked to complete the Social and Emotional Loneliness Scale for Adults (SELSA-S).
Multivariate analysis of covariance (MANCOVA) results showed that the integrated model of emotionally focused approach and Gottman model significantly decrease feeling of loneliness and its subscales (romantic loneliness, family loneliness, social loneliness). Additionally, using repeated measures in follow-up conducted one month later showed satisfactory results about the effectiveness of the integrated model in the experimental group.
It can be concluded that the specifically designed integrated model of emotionally focused approach and Gottman model can be used to decrease feeling of loneliness in married women.

The main objective of this article is to comparative study of diffusion, farm structure and integrated models regarding on farm sustainable water resources management adoption. The research design was a descriptive and correlation surveying method that has been used of Discriminant analysis. A random sample of wheat farmers selected from Ahwaz Township of Iran in 2008 (Population=1370, Sample=110). The results of the study indicated that different among age, education level, technical knowledge, income, crop yield, attitude and knowledge regarding sustainable water resources management based of adoption levels of farmers were significantly. In this study diffusion, farm structure and integrated models used to predicting adoption behavior of farmers. The findings indicated that the Discriminant model developed based on the integrated model is a better predictor for farmer’s adoption behavior than diffusion and farm structure model. Therefore, private and governmental agencies must emphasize application of integrated model in extension of sustainable water resources management.

One of the recent financial technologies is Block chain-based currency known as Cryptocurrency that these days because of their unique features has become quite popular. The first known Cryptocurrency in the world is Bitcoin, and since the cryptocurrencies market is a contemporary one, Bitcoin is currently considered as the pioneer of this market. Since the value of the previous Bitcoin prices data have a non-linear behaviour, this study aims at predicting Bitcoin price using Grey model, Back Propagation Artificial Neural Network and Integrated Model of Grey Neural Network. Then, the prediction’s accuracy of these methods will be measured using MAPE and RMSE indices and also Bitcoin price data for a five-year period (2014-2018). The results had indicated that wen estimating Bitcoin daily prices, Back Propagation Artificial Neural Network model has the lowest absolute error rate (5.6%) compared to the Grey model and the integrated model. Additionally, for the monthly prediction of Bitcoin price, the integrated model, with the lowest absolute error rate (9%), has a better performance than the two other models.

An integrated model of maintenance planning and statistical process control is developed for a production process. The process has two operational states including an in-control state and an out-of-control state, where the process failure mechanism is supposed as a general continuous distribution with non-decreasing failure rate. Based on the information obtained from the control chart, three types of maintenance actions may be implemented on the process. The integrated model optimally determines the parameters of the control chart and maintenance actions so that the expected cost per time unit is minimized. To evaluate the performance of the integrated model, a stand-alone model is developed. In the stand-alone model, only maintenance planning is considered. Finally, a real case study is presented to clarify the performances of these models.

This is a needs assessment study that aimed to develop an integrated model for insurance industry employees. The model was supposed to overcome shortcomings involved in needs assessment including costly and time-consuming processes and a lack of clarity in terms of tasks and administrative procedures. To this end، a company with 6 departments and 295 employees was approached. Initially، two models were combined to provide an integrated model for needs assessment. Then، job descriptions in various departments were considered along with required knowledge، attitude، and skills for each position. Data analysis revealed training needs of employees in different categories including instant، strategic، continuous، and neutral. Finally، the integrated model was revised and finalized based on findings.

In this paper a new integrated model of the dynamic environmental/economic dispatch (DEED) problem and emergency demand response program (EDRP) has been presented by which their interactions are investigated. DEED schedules the online generators power output over the whole dispatch period subject to some practical constraints so that the fuel costs and emission are optimized simultaneously. EDRP is one of the incentive-based demand response program in which incentives are paid to the customers to reduce their consumption during peak hours or shift it to the off-peak or valley hours. The proposed integrated model is a multi-objective optimization problem which aims to minimize both the fuel costs and emission and determine the optimal incentive of EDRP under some of practical constraint of units such as valve-point loading effect, multiple fuels, prohibited operating zones, and spinning reserve requirements. The proposed model has been applied on a ten generation units test system. The results indicate the effectiveness of the integrated model in reducing fuel costs and emission, improving load curve characteristics, spinning reserve, and consequently the network reliability.

The purpose of this paper is to study the ontological foundations of Merleau-Ponty’s and Lipman’s approaches in order to develop an integrated model in the Philosophy for Children program. The main research questions are: what are the ontological foundations of Merleau-Ponty’s and Lipman’s approaches and, on this basis, what is the integrated model of the two approaches in the philosophy for children program? In this paper, in identifying the ontological foundations, the analytical-inferential research method has been used, and in presenting the integrated model, the integration composition research method has been used. The results indicate similarities and differences between the Merleau-Ponty’s and Lipman’s approaches. Given the basic commonalities in the ontological foundations and the emphasis on the importance of childhood and its impact on adulthood in the two approaches, by eliminating disparate elements and overlapping commonalities through necessary corrections, a combination of the two approaches can be used to improve the philosophy for children program.

Conditional optimization is one of the best methods to increase production from hydrocarbon reservoirs. Integrated production modeling can optimize and manage production from reservoirs. In this research, a new method for optimizing integrated production systems is presented. The components of the integrated model are the reservoir, well, surface choke, pipeline, and separator models. The effect of separator inlet flow regimes and surface separator operation conditions on the efficiency of separator operation and oil production systems in this model cannot be neglected. This model includes the separator pressure controller and the objective function is to maximize the oil in the storage tank. The limitation of optimization is the non-formation of clotted flow at the inlet of the separator. The optimization model has excellent performance for optimizing integrated systems in stable, transient, and quasi-stable reservoirs. The optimization model can perform well in situations where the choke changes from critical to subcritical or vice versa. The amount of accumulated oil in the storage tank for the integrated model with clotted flow control is equal to BBL820, and for the integrated model without clotted flow control is equal to BBL 934.3 and for Olga software is equal to BBL 741.5. Using the optimal integrated model with clotted flow control, in addition to increasing oil production in the storage tank, also prevents the formation of slug flow.

River runoff forecasting in watersheds has a special place in the management and planning of water resources for the design of water facilities, water intake from rivers, consumption management and etc. In the present study, the performance of some data integration models including simple averaging, weighted averaging and integrated artificial neural network model in monthly discharge modeling has been evaluated and compared. For this purpose, monthly flow prediction in upstream basin of Jiroft Dam was examined using Artificial Neural Network (ANN) models, Adaptive Neural-Fuzzy Inference System (ANFIS), ARIMA model and Support Vector Regression (SVR) model as an individual model. Then, the individual models were trained and validated using selected predictor variables and their results were selected for use in the integration process. Large-scale climatic signals including NAO, ENSO and PDO are also used in hydrological forecasts of river flow and the performance of single and integrated models in two modes with and without considering these signals has been compared based on the evaluation of three criteria Nash-Sutcliffe (NSE), Coefficient of determination (R2) and Mean Square Error (MSE). Results of this study indicated that the integrated approach significantly increases the accuracy of predictions. In addition, large-scale climatic signals were found to improve results, especially during the test period. For example, the results of the integrated model of artificial neural network with large climatic scale signals show that this model has the best performance among the integrated models. Also, the NSE criterion has improved by 0.04 in training compared to the integrated model of artificial neural network without large-scale signals and the MSE error has been reduced by 0.001.