Application of Artificial Neural Network (ANN) in modeling of combined cycle power plant (CCPP) with dry cooling tower (Heller tower) has been investigated in this paper. Prediction of power plant output (megawatt) under different working conditions was made using multi-layer feed-forward ANN and training was performed with operational data using back-propagation. Two ANN network was constructed for the steam turbine (ST) and the main cooling system(MCS). Results indicate that the ANN model is effective in predicting the power plant output with good accuracy.

The current study measures the mechanical behavior of both natural and the monobloc elastomeric disc prosthesis (CadiscTM-L) by employing a finite element method (FEM) to study the fiber-reinforced constitutive formulation provided in the literature. The three-dimensional geometry was created by computed tomography (CT) scan imaging technique. Frontal pure rotational, sagittal, and axial momentum of 7.5 N·m were applied on the top of L3 while the lower half of the L5 was fixed in all directions. This investigation was performed considering two stages: (1) intact L3–L5 lumbar spine (INT model), and (2) Cadisc implemented between L4 and L5 (IMP model). The numerical results for the INT model were validated by experimental data from the literature. Several parameters including the inter-segmental rotation, range of motion in flexion-extension, axial rotation and lateral bending were analyzed. Our numerical results show that the IMP model has a 50% reduction in the ‘range of motion’ and a 33% reduction in flexion in lateral bending compared to the INT model. These outcomes of this paper reveal the feasibility of applying a fibre-reinforced constitutive formulation to generate an accurate three-dimensional FEM model.

New product development is a modern approach to deal with the environmental changes and an introduction to enter the competitive atmosphere is a challenging condition. By realizing the critical aspects of success of the new product and selecting a compatible model of variables in these aspects, companies may create a unique context for developing the product; but whether in terms of the multiplicity of the decision-making choices or the inherent conflicts of the response atmosphere’s variables, the complexities of the current world have caused disruption and inefficiency in the field of optimal selection and decision making. The aim of this study is to identify the critical aspects of development of the new product in the Sedan automobile class and to offer a morphological model of that by investigating the ideas and comments of the experts and scholars. In this regard, by using the Importance-Performance Analysis (IPA) method, the proposed method assesses the mode of the organization; and with the General Morphological Analysis, it assesses and searches for the most compatible decision-making variables to find the fully optimal and compatible solutions. After studying the subject literature, seven critical aspects are selected for the product development and three purposes are chosen for limiting the morphological model. Finally, four fully compatible configurations and one configuration are proposed with the entrance of the aspects determined from the output of IPA method. The innovative solution in solving the morphological model and its integration with IPA method are considered as the innovations of this study.

Leasing currently plays an important role for the global economy. The equipment leasing earning acquired through leasing rather than cash or credit, has reached a dominant level. With this regards, this paper represents a basic mixed-integer non-linear programming model. The study deliberates a firm that leases new products and remanufactured leased merchandises. The proposed study considers the end of lease contract, which contains several options: Return the leased product, return the used product and purchase other remanufactured product and buying the leased product. The primary objective is to maximize the discrepancy between the revenue and the costs of a firm, which leases new products as well as selling remanufactured ones. The product deteriorates with time and the difference between a new and used good is obvious. The product must undergo a remanufacturing procedure before being sold as a remanufactured product.

In an environment where markets go through a volatile process, and rapid fundamental changes occur due to technological advances, it is important to ensure and maintain a good performance measurement. Organizations, in their performance evaluation, should consider different types of financial and non-financial indicators. In systems like direct sales stores in which decision units have multiple inputs and outputs, all criteria influencing on performance must be combined and examined in a system, simultaneously. The purpose of this study is to evaluate the performance of different products through direct sales of a firm named Shirin Asal with a combination of Balanced Scorecard, fuzzy AHP and TOPSIS so that the weaknesses of subjectivity and selective consideration of evaluators in evaluating the performance indicators are reduced and evaluation integration is provided by considering the contribution of each indicator and each indicator group of balanced scorecard. The research method of this case study is applied. The data collection method is a questionnaire from the previous studies, the use of experts' opinions and the study of documents in the organization. MATLAB and SPSS were used to analyze the data. During this study, the customer and financial perspectives are of the utmost importance to assess the company branches. Among the sub-criteria, the rate of new customer acquisition in the customer dimension and the net income to sales ratio in financial dimension are of the utmost importance.

One subcategory of project scheduling is the resource constrained project scheduling problem (RCPSP). The present study proposes a differential evolution algorithm for solving the RCPSP making a small change in the method to comply with the model. The RCPSP is intended to program a group of activities of minimal duration while considering precedence and resource constraints. The present study introduces a differential evolution algorithm and local search was added to improve the performance of the algorithm. The problems were then solved to evaluate the performance of the algorithm and the results are compared with genetic algorithm. Computational results confirm that the differential evolution algorithm performs better than genetic algorithm.

Multi-period models of portfolio selection have been developed in the literature with respect to certain assumptions. In this study, for the first time, the portfolio selection problem has been modeled based on mean-semi variance with transaction cost and minimum transaction lots considering functional constraints and fuzzy parameters. Functional constraints such as transaction cost and minimum transaction lots were included. In addition, the returns on assets parameters were considered as trapezoidal fuzzy numbers. An efficient genetic algorithm (GA) was designed, results were analyzed using numerical instances and sensitivity analysis were executed. In the numerical study, the problem was solved based on the presence or absence of each mode of constraints including transaction costs and minimum transaction lots. In addition, with the use of sensitivity analysis, the results of the model were presented with the variations of minimum expected rate of programming periods.

One of the most important questions in outsourcing management is to decide on the allocation of the activities, processes or services either from the inside or from the outside of the organizations through outsourcing operations. It is necessary to determine the factors influencing outsourcing decision making as well as their impacts on decisions. In this paper, the existing approaches and models and their related criteria are studied and the necessary criteria are explored based on different conditions in marine manufacturing industries. These criteria are categorized in four dimensions; namely Capacity, Technology, Strategy and financial, and Risk called CTSR. After determining the weights of the criteria based on paired-wise comparisons, the related calculations are carried out using PROMETHEE method. Finally, a Black, White, Gray (BWG) analysis is executed to determine whether a candidate should be outsourced or not.

Machine failures during production period may impose thousands to millions of dollars to a manufacturing system. In this paper, the impact of machine failures on production lines in a closed-loop supply chain systems is examined. For this purpose, a new method is proposed for scheduling manufacturing workshops in a supply chain systems. The aim is to determine the best production plans in a manufacturing system by considering alternative preventive maintenance programs while machine failures can affect system performance. To solve the model, a hybrid Ant Colony and Simulated Annealing algorithms is developed and the results are compared with branch and bound method. Our findings show that the condition of emerging machine failure affects machines’ capacity which yields to lost sale. The impacts of using appropriate preventive maintenance on reducing lost sale is also examined. The results indicate that the proposed method can significantly reduce the level of sale variation in supply chain systems.

Recently, learning effects have been studied as an interesting topic for scheduling problems, however, most researches have considered single or two-machine settings. Moreover, learning factor has been considered for job times instead of setup times and the same learning effect has been used for all machines. This paper studies the m-machine no-wait flowshop scheduling problem considering truncated learning effect in no-wait flowshop environment. In this problem, setup time is a function of job position in the sequence with a learning truncation parameter and each machine has its own learning effect. In this paper, a mixed integer linear programming is proposed for the problem to solve such problem. This problem is NP-hard so an improved genetic algorithm (GA) and a simulated annealing (SA) algorithm are developed to find near optimal solutions. The accuracy and efficiency of the proposed procedures are tested against different criteria on various instances. Numerical experiments approve that SA outperforms in most instances.