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Coal is an important component in the energy industry and plays a key role in energy-producing facilities. Moisture is a common condition that has a considerable impact on coal. Coal drying has long been a question of great interest in a wide range of fields. Defining parameters in the coal drying is obtained by experiments. High costs, time constraints, and repetition of an experiment are one of the most frequently stated problems with experimental works. Using qualitative methods with experiments can be more useful for identifying and characterizing the coal drying process. The purpose of this article is finding the effective parameters in the coal drying process by using a hybridized prediction method. Genetic Algorithm (GA) and Artificial Neural Network (ANN) are hybridized with each other to identify and characterize the coal drying process. GA-ANN algorithm is applied to the coal drying process to predict the moisture of coal, but it does not provide a decent result at first. Later, the Design of Experiment (DoE) methodology is performed to determine the main effects of six parameters. Two scenarios are generated because two parameters are not statistically significant. The first scenario excludes the air relative humidity parameter, and the second scenario excludes the air relative humidity and the velocity of air parameters. Following the application of the DoE method, GA-ANN reaches decent results in scenario-2. WOS:000526510500001 2-s2.0-85083901460

The overall service quality level of Emergency Departments (EDs) can be improved by accurate forecasting of patient visits. Accordingly, this study aims to evaluate the use of three metaheuristic approaches integrated with Artificial Neural Network (ANN) in forecasting daily ED visits. To do this, five performance measures are used for evaluating the accuracy of the proposed approaches, including Bayesian ANN, Genetic Algorithm-based ANN (GA-ANN), and Particle Swarm Optimization algorithm-based ANN (PSO-ANN). The outputs of this study show that the PSO-ANN model provides the most dominant performance in both the training and testing process. The lowest error is obtained with a mean absolute percentage error (MAPE) of 6.3%, Mean Absolute Error (MAE) of 42.797, Mean Squared Error (MSE) of 2499.340, Root Mean Square Error (RMSE) of 49.933, and R-squared (R2) of 0.824 on the training dataset. The lowest error with an MAPE of 6.0%, MAE of 40.888, MSE of 2839.998, RMSE of 53.292, and R2 of 0.791 is also obtained on the testing process.

An effective short-term natural gas forecasting method contributes to social contributions and allows industrial chain elements to function effectively and minimize economic losses. We dealt with a comparative framework on the applicability of different methods in daily natural gas service (NGS) consumption forecasting. In this context, time series, machine learning, evolutionary and population-based approaches, and their hybrid versions are applied to the NGS data. Hybridized approaches are tested in the scope of NGS consumption forecasting for the first time in the literature in this study. The case of Turkey is handled, and its NGS data is used to demonstrate the comparative framework's applicability. The comparative study is assessed in the lights of common forecasting accuracy measures of mean absolute percentage error (MAPE), R-squared (R-2), and mean squared error (MSE). According to each method's results, the seasonal autoregressive integrated moving average with exogenous regressors (SARIMAX) and artificial neural network (ANN) hybrid model provides the most dominant performance with respect to MAPE. The lowest error was obtained with a MAPE value of 0.357 in this hybrid model constructed under seven neurons in its ANN structure. This model is followed by another hybrid model, autoregressive integrated moving average (ARIMA)-ANN, with a MAPE value of 0.5 under nine neurons in terms of accuracy performance. The worst performance value belongs to the Genetic algorithm-ANN hybrid model with a MAPE value of approximately 26%. WOS:000609600900001 2-s2.0-85099744653

Soil plays a vital role in the climate system. This paper performs decision tree regression to estimate soil moisture (SM) by considering different parameters that include air temperature, time, relative humidity, and soil temperature. Besides, this paper investigates the effects of the parameters of decision tree regression by utilizing the response surface. The obtained estimation results of two distinct soil areas, Field and Forest, indicate that two different soil areas have distinct estimation quality. Furthermore, numerical results of the training stage show that the estimation of SM for Field and Forest soil performing decision tree regression offers 0.0019 and 0.0025 mean absolute error (MAE), respectively. Moreover, numerical results show that the interaction of the parameters of the performed algorithm plays a vital role in the estimation stage of Field and Forest soils. WOS:000525759400001 2-s2.0-85083407257 PubMed: 32291529

Soil plays a vital role in the climate system. This paper performs a hybrid method that comprises particle swarm optimization (PSO) and artificial neural network (ANN) to estimate soil moisture (SM) by considering different parameters that include air temperature, time, relative humidity and soil temperature. Besides, this paper investigates the effects of the parameters of PSOANN by using from the response surface. PSO algorithm involves changing the weights of ANN. Paper chooses the coefficient of determination and mean absolute error to measure the performance of the performed hybrid PSO-ANN. The numerical results show that hybrid PSO-ANN is applied to estimate SM successfully Toprak, iklim sisteminde hayati bir rol oynar. Bu makale, hava sıcaklığı, zaman, bağıl nem ve toprak sıcaklığını içeren farklı parametreleri göz önüne alarak toprağın nemini (TM) tahmin etmek için parçacık sürüsü optimizasyonundan (PSO) ve yapay sinir ağından (YSA) oluşan bir hibrit yöntem uygular. Ayrıca, bu makale tepki yüzeyinden yararlanarak PSO-ANN parametrelerinin etkilerini araştırmaktadır. PSO algoritması, YSA'nın ağırlıklarını değiştirme sürecinde yer almaktadır. Korelasyon katsayısı ve ortalama mutlak hata, uygulanan hibrit PSO-ANN'nin performansını ölçmek için seçilmiştir. Sayısal sonuçlar, PSO-ANN hibritinin TM'yi başarılı bir şekilde tahmin etmek için uygulandığını göstermektedir.

Soil moisture (SM) is a significant factor in the climate system. The accurate determination of SM has high importance in food production to satisfy the increasing demand for food and the chemical processes of soil. This paper applies decision tree regression to estimate SM considering different parameters including air temperature, time, relative humidity, and soil temperature. The presented method holds a mighty advantage to determine SM since the stimulant of the decision tree regression is an algorithm that generates a decision tree from given instances. Besides, usage of decision tree regression provides an opportunity to save time. Numerical results show that the presented method offers a high coefficient of determination value (R-2), low mean squared error (MSE), and mean absolute error (MAE). The depth of the decision tree equals to five by providing higher fitness values than other depth levels. The best fitness values in the training stage are 0.00019, 0.007, and 0.842 for MSE, MAE, and R-2, respectively. In conclusion of the paper, applied decision tree regression can handle the data of SM estimation in satisfying fitness criterion. WOS:000511528400022 2-s2.0-85076208991

Diabetes mellitus (DM) is a group of metabolic disorders with one common manifestation: elevated blood sugar or hyperglycemia. The diagnosis of diabetes is the most crucial point due to chronic hyperglycemia. This chapter improves the performance of the Classification and Regression Trees (CART) algorithm because the accurate classification of diabetes depends on the algorithm efficiency. Authors use the accuracy rate for the objective function in the prediction process by Genetic Algorithm (GA). The proposed GA-CART algorithm provides the best performance at 96.05%.

Coal is still a significant energy source for the world. Due to the utilization of low-rank coal, drying is a key issue. There are lots of attempts to develop efficient drying processes. The most prominent method seems as thermal drying. For thermal drying processes, the most important subject is the coal moisture content change with time. In this study, convective drying experiments were utilized to develop a new model based on decision tree regression method to predict coal moisture content. The developed model gives satisfactory results in prediction of instant coal moisture content with changing drying conditions. With the decision tree depth of six, the best test results were achieved as 0.056 and 0.802 for MSE and R-2 analyses, respectively. WOS:000519514800001 2-s2.0-85081742769

In this paper, an improved particle swarm optimization (IPSO) algorithm is proposed to solve the technician routing and scheduling problem (TRSP). The TRSP consists of the assignment of technicians into teams, the assignment of teams to tasks, the construction of routes, and the selection of the day on which a service is provided by considering the proficiency level of workers and the proficiency requirement of the task. The paper considers the planning horizon as a multi-period covering 5 days, which further increases the complexity of the problem. Then a task can be fulfilled in any one of 5 days. The IPSO algorithm includes a particle swarm optimization (PSO) algorithm and one neighborhood operator. One neighborhood operator is used to avoid the local solution trap since the global best solution found by PSO is falling into a local solution trap. Further, the proposed algorithm's performance is experimentally compared with the branch-and-cut algorithm for the solution of the TRSP, on the benchmark instances generated from the literature. The computational results show that IPSO provides better solutions considering the branch-and-cut algorithm within reasonable computing time. WOS:000572011900003 2-s2.0-85091300461

Drying of low rank coal is a very significant topic due to its high economic value. One of the most important parameter in the designing of a dryer is the exiting moisture content of the material a...

PEKEL, Engin/0000-0002-5295-8013 WOS:000535788500003 The number of flight (NF) is one of the key factors for the administration of the airport to evaluate the apron capacity and airline companies to fix the size of the flight. This paper aims to estimate the monthly NF by performing particle swarm optimization (PSO) and artificial neural network (ANN). Performed PSO-ANN algorithm aims to minimize the proposed evaluation criterion in the training stage. PSO-ANN based on the proposed evaluation criterion offers satisfying fitness values with respect to correlation coefficient and mean absolute percentage error in the training and testing stage.

In this study, a new methodology was applied to estimate the coal moisture content during the drying process. Adaptive-network-based fuzzy inference system (ANFIS) was applied to predict the coal moisture content at any time during the drying process. The experiments were carried out for different drying air temperatures (70, 100, 130 and 160 °C), drying air velocities (0.4, 0.7 and 1.1 m/s), bed heights (80, 130 and 150 mm) and sample sizes (20, 35 and 50 mm), and the experimental results were used to validate applicability of the ANFIS in the coal drying process. The ANFIS network achieves quite satisfying scientific results with acceptable deviations. The MSE and R 2 values were calculated as 1.899 and 0.998, respectively, for the testing stage. The results of this study show the applicability of the ANFIS in the coal drying processes to predict the coal moisture content at any time. Therefore, it is not necessary to carry out all the experiments: by using the ANFIS, the drying curves of some other cases which are not performed can be estimated easily. Herewith, the necessary number of the experiments decreases.

This paper aims to find the optimal depot locations and vehicle routings for spare parts of an automotive company considering future demands. The capacitated location-routing problem (CLRP), which has been practiced by various methods, is performed to find the optimal depot locations and routings by additionally using the artificial neural network (ANN). A novel multi-stage approach, which is performed to lower transportation cost, is carried out in CLRP. Initially, important factors for customer demand are tested with an univariate analysis and used as inputs in the prediction step. Then, genetic algorithm (GA) and ANN are hybridized and applied to provide future demands. The location of depots and the routings of the vehicles are determined by using the variable neighborhood descent (VND) algorithm. Five neighborhood structures, which are either routing or location type, are implemented in both shaking and local search steps. GA-ANN and VND are applied in the related steps successfully. Thanks to the performed VND algorithm, the company lowers its transportation cost by 2.35% for the current year, and has the opportunity to determine optimal depot locations and vehicle routings by evaluating the best and the worst cases of demand quantity for ten years ahead. © 2018, Faculty of Transport and Traffic Engineering. All rights reserved.

A fuzzy capacitated location routing problem (FCLRP) is solved by using a heuristic method that combines variable neighborhood search (VNS) and evolutionary local search (ELS). Demands of the customer and travel times between customers and depots are considered as fuzzy and deterministic variables, respectively in FCLRP. Heterogeneous and homogeneous fleet sizes are performed together to reach the least multi-objective cost in a case study. The multi-objective cost consists of transportation cost, additional cost, vehicle waiting cost and delay cost. A fuzzy chance constrained programming model is added by using credibility theory. The proposed method reaches the solution by performing four stages. In the first stage, initial solutions are obtained by using a greedy heuristic method, and then VNS heuristic, which consists of seven different neighborhood structures, is performed to improve the solution quality in the second stage. In the third stage, a perturbation procedure is applied to the improved solution using ELS algorithm, and then VNS heuristic is applied again in the last stage. The combination of VNS and ELS is called VNSxELS algorithm and applied to a case study, which has fifty-seven customers and five distributing points, effectively in a reasonable time. (C) 2019 Elsevier B.V. All rights reserved. WOS:000488100900052 2-s2.0-85073652680

In this study, a two stage methodology was applied to predict the exit coal moisture content during the drying process. The first stage included a design of experiment (DoE) study which made easy to determine the significance levels of drying parameters. At the end of the DoE stage, it was determined that the most significant parameter was bed height, and the least significant parameter was exit air relative humidity. The second stage included an adaptive neuro-fuzzy inference system (ANFIS) method which was applied to estimate the exit coal moisture content at any time. The experimental studies were conducted with different levels of the parameters (air temperature, air velocity, bed height, particle size, and air relative humidity). At the end of the second stage, the applicability of the ANFIS in the estimation of the exit coal moisture content was showed with satisfying results. R2 value was increased from 0.465 to 0.842. [Received: March 28, 2017; Accepted: November 3, 2017]

A successful fleet scheduling in any public transport system has a prominence. Metrobus is a bus rapid transit (BRT) system in Istanbul and has a high usage percentage among the public transport. The aim of this study is to establish a successful fleet scheduling by using simulation in the Metrobus. In addition, it aims to meet the satisfaction of passengers. Metrobus system is regarded as a discrete system in the study. The distributions of the passengers, which are boarding and disembarking the system, are determined with regard to stochastic process and a discrete-event model is constructed to determine at which bus-stop one boards and disembarks from the Metrobus. A simulation experiment is presented with the results by applying several different scenarios. At least 12.7% decrease in queue for Zincirlikuyu direction and at least 9.9% decrease in queue for Avclar direction is achieved in the developed simulation model. The results prove that simulation-based fleet scheduling can be applied for the Metrobus successfully.