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This paper presents modeling and optimization of batch production based on layout, cutting and project scheduling problems by considering scenario planning. In order to solve the model, a novel genetic algorithm with an improvement procedure based on variable neighborhood search (VNS) is presented. Initially, the model is solved in small sizes using Lingo software and the combined (proposed) genetic algorithm; then the results are compared. Afterwards, the model is solved in large sizes by utilizing the proposed algorithm and simple genetic algorithm. The main findings of this paper show: (1) The suggested algorithm is valid and able to achieve optimal and near-optimal solutions. This conclusion was made after proving the validity of the proposed method by solving a case study by employing the classical method (employing Lingo 11). And when the results were compared with the ones obtained by the proposed algorithm, they were found to be the same in both cases. (2) The combined genetic algorithm is more effective in obtaining optimal boundaries and the solutions close to them in all cases compared to the classical (simple) genetic algorithm. In other words, the main finding of this paper is a combined genetic algorithm to optimize batch production modeling problems, which is more efficient than the methods provided in the literature.

Among the multiple causes of project success in construction of big projects (such as highway projects), punctually delivering of preconstruction activities is increasingly recognized as a quite frequent cause that has not received adequate amount of attention. In this research, after providing an introduction about the different phases and activities in a construction project, the reasons behind the importance of preconstruction activities is briefly summarized. Scheduling of these types of activities is complex in nature mostly because of the unclear path toward delivering them; therefore, inaccurate scheduling of them is common. Since it is a very demanding and difficult responsibility to organize the literature efficiently, the literature is classified into five different groups to accelerate this effort; These groups are environmental clearance, design, right-of-way acquisition, utility adjustment, and finally advertising and letting; In this research, these categories and their definitions are provided in detail. After conducting a comprehensive literature review, the results of this study show that there is a lack of a quantitative model for predicting schedules associated with various preconstruction activities. Beside many other causes behind this need, the main reason is that the approach of most of the previous studies was qualitative and the few ones that have conducted a quantitative research on historical data had a small sample size.

Optimization of electrical power consumption in multi-chiller system leads to save more energy in building or industrial locations. Also, this optimization problem is one of the most important issues in multi-chiller system. Furthermore, uncertainty modeling of cooling demand is necessary because of variation in cooling demand should be considered. Therefore, this work proposes a robust optimization approach for uncertainty modeling of cooling demand in order to obtain robust chiller loading in the uncertain environment which cooling demand is supplied by multi-chiller system. Minimizing of electrical power consumption in multi-chiller system is considered as objective function. The proposed robust scheduling of multi-chiller system is modeled as non-linear programming which is solved via CONOPT solver under General Algebraic Modeling System (GAMS) optimization software. The proposed optimization model is studied in the deterministic and robust optimization strategies and obtained results are compared with each other. Also, the effects of changes in the robust control parameter are analyzed on optimal chiller loading which decision maker can select the decision without risk as risk-neutral strategy via deterministic method or the most robust decision as risk-averse strategy via robust optimization approach. Comparison results show that capability of proposed approach in the uncertain environment.

In the restructured power systems, power price in the market can change under different operating conditions of the system, which may impose difficulties for system operators to get optimal scheduling of the system. In order to investigate pernicious effects of any uncertain parameter in the system under study, especially power price, the robust optimization method can be an important tool. In this chapter, the optimal energy management problem of the DC microgrid (MG) is investigated in the presence of power price uncertainty under the demand response (DR) program. To show the impact of the DR program, the problem has been solved in two cases: without- and with-DR cases. The problem has been formulated as the mixed-integer linear programming method and solved using the CPLEX solver in GAMS optimization software. According to obtained results, DC MG is robust against the power price uncertainty.

In this paper, the ultimate boundary region of a financial risk system is studied through an optimization idea. For this system, the analytical expression of the ultimate boundary region is derived based on the optimization method and the Lagrange multiplier method. The ultimate bound which is useful in chaos synchronization is demonstrated through numerical simulations. Utilizing the bound obtained, a linear controller is proposed to achieve the chaos synchronization. All the numerical simulation results are in line with the theoretical analysis.

This paper presents a new prediction model based on empirical mode decomposition, feature selection and hybrid forecast engine. The whole structure of proposed model is based on nonstationarity and non-convex nature of wind power signal. The hybrid forecast engine consists of three main stages as; empirical mode decomposition, an intelligent algorithm and three stage neural network. All parameters of proposed neural network will be optimized by intelligent algorithm. Effectiveness of the proposed model is tested with real-world hourly data of wind farms in Canada, Spain and Texas. In order to demonstrate the validity of the proposed model, it is compared with several other wind speed and power forecast techniques where, different prediction horizons are considered from day-ahead to one week forecasting. Obtained results confirm the validity of the developed approach in prediction model for different forecast horizons.

Wax deposition in petroleum industry is one of the major problems requiring accurate predictive procedures to reduce the deficiencies and effective designing of the process. An adaptive neuro fuzzy...