جستجوی مقالات مرتبط با کلیدواژه "simulated annealing" در نشریات گروه "مواد و متالورژی"

This work uses artificial neural networks to evaluate heat transfer in a mini-channel heatsink using an alumina/water nanofluid. The multi-layer perceptron (MLP) and radial basis function (RBF) neural networks are employed for the modeling. To apply the artificial neural network analysis, 60 data of experimental works are utilized. The outcomes depicted that the simulated annealing (SA) technique significantly increased the performance of the RBF network, although the optimal MLP structure was discovered by trial and error. The optimized RBF network carried over more data with less than 2% errors as compared to the MLP. While the results of the MLP network showed that the average relative error for the test data set was 2.0496%, this value was 1.417% for the RBF network. The modeling time is a significant determining element when choosing the optimal technique. The RBF network optimization took longer than 60 minutes, even though all MLP structures were run 100 times in less than 15 minutes. In summary, artificial neural networks are effective instruments for simulating these kinds of processes, and their application can save a lot of time-consuming experimentation. Additionally, the RBF network outperforms the MLP in terms of precision while requiring less processing time.

Climate change, greenhouse gases, and global warming are global issues today. Of course, this global issue cannot be separated from the issue of emissions. Various methods to solve generator scheduling problems by considering emissions or Economic Emission Dispatch (EED) have been published, but not to the extent of calculating the cost to reduce emissions. The main objective of this research is to determine the cost of reducing the emission of electricity generation in Indonesia through solving the EED problem. The method proposed to solve the EED problem is an annealing simulation algorithm and tested using an electrical system of eight generators, four different loads, and five combinations of cost and emission weights. This method is tested with various loads (conditions), and each condition is tested with various combinations of cost weights and emission weights. The results obtained are compared with the results of the calculation of the Cuckoo algorithm, and the whale optimization algorithm. The simulation results show that it costs US$258.81 to reduce 1 ton of emissions. This paper can be used as a material for further consideration for the government and generator providers in making policies related to the operation of power plants by considering emissions.

Nowadays, there is a great deal of attention for regulations of carbon emissions to enforce the decision-makers of production and distribution networks to redesign their systems satisfactorily. The literature has seen a rapid interest in developing novel metaheuristics to solve this problem as a complicated optimization problem. Such difficulties motivate us to address a production-distribution network design problem considering carbon emissions policies among the first studies in this area by a novel hybrid whale optimization algorithm. Accordingly, a mixed integer non-linear programming model has been developed. To tackle the proposed problem, a new hybrid metaheuristic based on whale optimization algorithm and simulated annealing as a successful optimizer is employed to solve the proposed problem. The calibration of the algorithms has been designed by Taguchi method, comprehensively. Finally, an extensive analysis has been evaluated through a comparative study along with some assessment metrics of Pareto solutions.

In this study, Back-propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS) methods were applied to estimate the particle size of silica prepared by sol-gel technique. Simulated annealing algorithm (SAA) employed to determine the optimum practical parameters of the silica production. Accordingly, the process parameters, i.e. tetraethyl orthosilicate (TEOS), H2O and NH3 were introduced to BPNN and ANFIS methods. Average mean absolute percentage error (MAPE) and correlation relation (R) indexes were chosen as criteria to estimate the simulation error. Comparison of proposed optimum condition and the experimental data reveal that the ANFIS/SAA strategies are powerful techniques to find the optimal practical conditions with the minimum particles size of silica prepared by sol-gel technique and the accuracy of ANFIS model was higher than the results of ANN. Moreover, sensitivity analysis was employed to determine the effect of each practical parameter on the size of silica nano particles. The results showed that the water content and TEOS have the maximum and minimum effect on the particle size of silica, respectively. Since, water acts as diluent and synthesis of monodisperse silica in diluent solution will decrease the growth probability of nucleate, leading to a the lower silica particle size.

In the last decade, theory of constraint application in project management lead to make a new approach for project scheduling and control as a critical chain. In this paper, a multi-objective optimization model for multi-project scheduling on critical chain is investigated. The objectives include time, cost and quality. In order to solve the problem, a Simulated Annealing algorithm is developed and then analyzed to investigate the effect of each objectives. The number of activities in each project is not considered the same. Time, cost and quality value are calculated by solving the proposed algorithm and then the total utility amount is obtained. Sensitivity analysis is performed based on various amount of each objective weights. Then the effect of objectives weight variation is investigated on utility function value. In addition the results show that the proposed algorithm are able to solve problem optimally in large scale.
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This paper addresses a multi-objective mathematical model for the mixed-model two-sided assembly line balancing and worker assignment with different skills. In this problem, the operation time of each task is dependent on the skill of the worker. The following objective functions are considered in the mathematical model: (1) minimizing the number of mated-stations (2), minimizing the number of stations, and (3) minimizing the total human cost for a given cycle time. Furthermore, maximizing the weighted line efficiency and minimizing the weighted smoothness are two indices considered simultaneously in this paper. Since this problem is well-known as NP-hard class, a particle swarm optimization (PSO) algorithm is developed to solve it. The performance of the proposed PSO algorithm is evaluated with a simulated annealing (SA) algorithm existed in the literature over several benchmarked test problems for the conditions of the current problem in terms of running time and solution quality. The results show the proposed algorithm is an efficient algorithm .

In this paper, a flowshop scheduling problem is studied. The importance of this study is that it considers different constraints simultaneously. These constraints are Lot Streaming, Position based learning factors, sequence dependent setup times and the fact that the flowshop line is no-wait. Lot streaming divide the lots of products into portions called sublots in order to reduce the lead times and work-in-process, and increase the machine utilization rates. The objective is to minimize the makespan. To clarify the system, mathematical model of the problemis presented. Since the problemis strongly NP-hard, two hybrid metaheuristics algorithms are proposed to solve the problem. These algorithms are based on the Variable Neighborhood Search (VNS), which is proved as an effective method for combinatorial optimization problems. In the proposed VNS, an efficient scheme for neighborhood search based on Tabu Search (TS) and Simulated Annealing (SA) is presented to strengthen the local searches. At the last part, computation results are provided to evaluate the efficiency of VNSSA and VNSTS. In order to verify the effectiveness of proposed algorithms, Relative percentage Deviation along with statistical analysis is presented.The computational results show that VNSSA outperforms VNSTS in most instances.

In this study, we consider the production environment of no-wait reentrant flow shop with the objective of minimizing makespan of the jobs. In a reentrant flow shop, at least one job should visit at least one of the machines more than once. In a no-wait flowshop scheduling problem, when the process of a specific job begins on the first machine, it should constantly be processed without waiting in the line of any machine until its processing is completed on the last one. Integration of the properties of both of these environments, which is applied in many industries such as robotic industries, is not investigated separately. First, we develop a mathematical model for the problem and then we present three methods to solve it. Therefore, we construct simulated annealing (SA), genetic algorithm (GA) and a bottleneck based heuristic (BB) algorithms that solve the problem. Finally, the efficiency of the proposed methods is numerically analyzed.

This paper proposes a mixed integer programming model for single-item capacitated lot sizing problem with setup times, safety stock, demand shortages, outsourcing and inventory capacity. Due to the complexity of problem, three meta-heuristics algorithms named simulated annealing (SA), vibration damping optimization (VDO) and harmony search (HS) have been used to solve this model. Additionally, Taguchi method is conducted to calibrate the parameters of the meta heuristics and select the optimal levels of the algorithm’s performance influential factors. Computational results on a set of randomly generated instances show the efficiency of the HS against VDO and SA.

In this paper the problem of serial batch scheduling in a two-stage hybrid flow shop environment with minimizing Makesapn is studied. In serial batching, it is assumed that jobs in a batch are processed serially, and their completion time is defined to be equal to the finishing time of the last job in the batch. The analysis and implementation of the prohibited transference of jobs among machines of stage one in serial batch is the main contribution of this study. Machine set-up and ready time for all jobs are assumed to be zero and no Preemption is allowed. Machines may not breakdown, but at times they may be idle. As the problem is NP-hard, a simulated annealing and genetic algorithm are proposed to provide near-optimal solutions. Since this problem has not been studied previously, therefore, a lower bound is developed for evaluating the performance of the proposed SA and GA solutions. Many test problems have been solved using SA and GA; results show both solving procedures provide near-optimum solutions regarding the lower bound solution. In the case of large scale problems, solutions provided by GA overcome those from SA algorithm.

Cross-docking plays an importation role in distribution networks. In the recent years, a cross-docking design network problem is addressed as a new research area in logistics management. This paper presents a new mathematical model for the location of cross-docking facilities and vehicle routing scheduling problems in the distribution networks. For this purpose, a two-phase mixed integer programming (MIP) is formulated. Then, a new heuristic-based simulated annealing (SA) is developed for solving the proposed MIP model. Finally, the presented heuristic algorithm is subsequently tested on a number of small and large-scale instances. The computational results for different sized instances illustrate that the proposed algorithm performs effectively in a reasonable time.

This paper presents a new mathematical model, in which the location of hubs is fixed and their capacity is determined based on facilities and factories allocated to it. In order to feed the client's nodes, different types of vehicles of different capacities are considered, in which the clients are allocated to hubs, and types and numbers of vehicles are allocated to the factory's facilities. To come up with solutions we propose to use three meta-heuristics, namely genetic algorithm, particle swarm optimization, and simulated annealing. The efficiency and computational results of the foregoing algorithms are compared with one another. Finally, the conclusion is presented.