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This study explores the intricate integration and synchronization of supplier selection with the optimal scheduling of multi-mode resource-constrained projects, which is a genuine and complex challenge prevalent in the construction industry, by proposing new multi-objective mathematical modeling considering various items. Within this context, a multifaceted network of concurrent projects (multi-project) is examined with different suppliers' resources (multi-supplier) to minimize the overall projects' delay times and associated costs. The mathematical model formulation also incorporates diverse implementation modes (multi-mode) and the time value of money (TVM). To use and unravel the complexities of the proposed model, two distinct algorithms, including a multi-objective whale optimization algorithm (WOA) based on the Pareto archive and the well-known non-dominated sorting genetic algorithm II (NSGA-II), are employed. The algorithms were subjected to a comparative analysis of several sample problems and evaluated against multi-objective criteria, including quality metric (QM), diversity metric (DM), spacing metric (SM), number of solutions (NOS), mean Ideal distance (MID), and computational time. The evaluation reveals that the tailored multi-objective WOA outperforms NSGA-II, exhibiting greater solution precision and diversity. The WOA demonstrates an enhanced ability to efficiently explore the problem's feasible solution space, albeit at the increased computational time to pinpoint optimal solutions. Notably, the validity and practicality of the proposed model and method were field-tested within the context of construction projects in Iran, with the obtained results juxtaposed against the real-world data. The comparative analysis indicates that implementing the scheduling approach and solution methodology espoused by the multi-objective WOA led to significant improvements, with financial gains of up to 6% and time savings reaching 16%. Overall, this research substantiates the proposed model and algorithms' benefits in reducing project costs and delays, offering valuable insights for construction industry practitioners. [ABSTRACT FROM AUTHOR]

In this study, the optimization of the location of oil production and injection wells, the number of production and injection wells, and the economic evaluation in the water injection process has been carried out using three algorithms: PSO, GA, and HGAPSO. Optimizing the oil extraction process by optimizing well locations, the optimal number of production and injection wells, and economic evaluation can improve the performance and efficiency of the process. In this study, the optimization of the water injection process into oil reservoirs using the PSO, GA, and HGAPSO algorithms has been addressed. Additionally, economic evaluation, considering costs and profits from oil production using the best-optimized parameters, has been conducted, and the best Net Present Value (NPV) has been calculated. Initially, the optimal well location problem is modeled, taking into account reservoir characteristics and geological conditions. Using the PSO and GA algorithms, the best well location and number of water injection wells have been obtained, leading to improved efficiency and cost reduction. Then, the GA algorithm is used to combine and modify viewpoints and optimize the number and location of wells. This algorithm explores optimal solutions in the well location space. Economic evaluation is performed using the best-optimized parameters by the algorithms. Costs of water injection, oil production costs, and profits from increased oil production are considered. By calculating the best Net Present Value (NPV), optimal decisions regarding the water injection process and enhancing the performance of oil reservoirs are made. Simulation results indicate that the GA algorithm outperforms other algorithms and can be used as a robust and effective method for optimizing well locations and the number of production and injection wells in the oil injection process. Ultimately, this study assists the oil industry in making better decisions about optimizing water injection and improving the efficiency and profitability of the process using the introduced optimization algorithms. [ABSTRACT FROM AUTHOR]

Optimizing resource utilization and train scheduling is essential to satisfy passengers and reduce operating costs. This study develops the train schedule under scenario-oriented stochastic conditions. The proposed approach is a multi-objective mathematical-based mixed integer linear programming (MILP) approach; the objective is to minimize the average passenger expectation and the total number of train operation cycles. The non-dominated sorting genetic algorithm (NSGA-II) has been developed with multi-crossover and multi-mutation operators, then hybrid with simulating annealing (SA) operator (NSGA-II-SA). The model with four meta-heuristic algorithms has been technically analyzed. In a case study, the train schedule in the double-track rail network of the Tehran–Mashhad railway of Iran has been compared with the golden point. Experimental results show that a proposed approach can suitably fit the problem considering important metrics with an improvement of %7.34 and %6.89 for the average passenger waiting time and the total number of train operation cycles, respectively. [ABSTRACT FROM AUTHOR]

Due to the complex nature of processing units and the variability in raw oil properties, traditional first-principle methods are not always effective in predicting the output of these units without adapting them. Data-driven models have become popular alternatives because they are more adaptable to the variability in processing units, especially when large amounts of data are available. Feature selection is a crucial aspect of data-driven modeling, especially for processing units. It involves identifying and selecting the most important variables influencing the unit's output. The selected variables are then used as inputs to the model, which helps to reduce noise and improve model accuracy. Accordingly, this article presents a hybrid method that combines feature selection with data-driven modeling to create an accurate model for predicting the output of a Residue Fluid Catalytic Cracking (RFCC) process unit. The Metaheuristic Ant Colony Optimizer (MACO) algorithm was used to extract the best subset of features from the collected data, which were then used with the Artificial Immune Genetic Algorithm with Local Vaccination (AIGA-LV) method to select the optimal features. Finally, the Random Forest (RF) method was used to predict the products of the RFCC process unit. The data were collected from a refinery plant in central Iran for nine months. The proposed model was efficient in feature selection and flexible in handling different scenarios and products. It improved the accuracy to 99.17% at its best and improved the primary model by 3–20%. Therefore, this method could be useful for predicting the output of various industrial processes, especially the RFCC and FCC process units. [ABSTRACT FROM AUTHOR]

Reservoirs are crucial for water storage, flood control, and electricity generation. At the same time, evaporation in dam reservoirs causes water losses. This study evaluates the effect of weather conditions on the prediction of evaporation from the dam reservoir, and there are several developed models to predict evaporation under different scenarios. Using the deep neural network model long short term memory (LSTM) with a series of modern meta-heuristic algorithms, the present research aims to achieve an acceptable accuracy on weather parameters. The present study investigates the ability of the LSTM deep neural network and meta-heuristic algorithms of Artificial Bee Colony (ABC), Horse Herd Optimization (HOA), and Marine Predators (MPA) to develop an evaporation rate forecasting model for a tropical area in the Dez Dam reservoir, Khuzestan province, Iran. To investigate the effect of different input variable patterns on the prediction accuracy of the case model, twenty-eight scenarios were investigated for the input architecture along with the LSTM model and its combination with meta-heuristic algorithms. The models include ABC-LSTM, LSTM-HOA, and LSTM-MPA, respectively. The results showed that the combined LSTM-MPA model with RMSE, MAE, KGE, and WI evaluation criteria equal to 81.926 mm/month, 53.684 mm/month, 0.850, and 0.816, respectively, performs better than other meta-heuristic models. The examined deep meta-heuristic models are suggested for predicting evaporation from the Dez dam reservoir with limited information because the results show that they have more appropriate prediction capability than the LSTM Model. [ABSTRACT FROM AUTHOR]

Precipitation is a key constituent of the water cycle and its accurate measurement is essential for a wide range of hydroclimatic studies. Although advances in technology have provided new sources of precipitation data (e.g., satellite data), gauge measurements are still considered as the most reliable source of information. Accuracy of ground measurements highly depends on the network density and positions of the rain gauges and thus, many studies have attempted to re-design the rain gauge networks around the globe. This is while, a limited number of researches have investigated the potential of utilizing global gridded precipitation products in network optimization context. In this study, a two-step framework was proposed to remove redundant rain gauge stations of the Central Plateau watershed of Iran. In the first step, number of excess rain gauges was detected based on transinformation–distance (T–D) analysis. In the second step, exact location of the extra rain gauges was determined through high degree of freedom and low degree of freedom (LDF) scenarios by a combination of harmony search optimization algorithm and a geostatistical model. According to the results, 58 out of 580 rain gauges were detected to be removed. Results of the proposed scenarios differed in detecting 13 out of 58 gauges. While LDF scenario required less computational time, network re-designed by this scenario was about 32% less accurate than the other scenario. Performance of the both optimized networks was equally similar to the initial network in terms of developing precipitation rasters. [ABSTRACT FROM AUTHOR]

In this paper, a new responsive-green-cold vaccine supply chain network during the COVID-19 pandemic is developed for the first time. According to the proposed network, a new multi-objective, multi-period, multi-echelon mathematical model for the distribution-allocation-location problem is designed. Another important novelty in this paper is that it considers an Internet-of-Things application in the COVID-19 condition in the suggested model to enhance the accuracy, speed, and justice of vaccine injection with existing priorities. Waste management, environmental effects, coverage demand, and delivery time of COVID-19 vaccine simultaneously are therefore considered for the first time. The LP-metric method and meta-heuristic algorithms called Gray Wolf Optimization (GWO), and Variable Neighborhood Search (VNS) algorithms are then used to solve the developed model. The other significant contribution, based on two presented meta-heuristic algorithms, is a new heuristic method called modified GWO (MGWO), and is developed for the first time to solve the model. Therefore, a set of test problems in different sizes is provided. Hence, to evaluate the proposed algorithms, assessment metrics including (1) percentage of domination, (2) the number of Pareto solutions, (3) data envelopment analysis, and (4) diversification metrics and the performance of the convergence are considered. Moreover, the Taguchi method is used to tune the algorithm's parameters. Accordingly, to illustrate the efficiency of the model developed, a real case study in Iran is suggested. Finally, the results of this research show MGO offers higher quality and better performance than other proposed algorithms based on assessment metrics, computational time, and convergence. [ABSTRACT FROM AUTHOR]

Unsupervised clustering methods are used as data-driven models for mapping of mineral prospectivity (MPM). One of the challenging problems for modeling high potential mineral prospects using these algorithms is to select proper cluster centroids in order to locate the same geospatial objects into reasonable clusters. To overcome this kind of problems arise from traditional clustering algorithms (e.g. K-means), the combination of harmony search (HS) and artificial bee colony (ABC) metaheuristic optimization algorithms with K-means have been firstly developed in this study which can be used for selection of optimum cluster centroids for highlighting favorable targets in prospecting stage of mineral explorations. This stage is critically important for exploration of undiscovered mineral deposits, because the time and cost are two substantial factors in regional-scale exploration programs. This study stated that the applying hybrid data-driven clustering models (e.g., HS-KM and ABC-KM) are operational tools for identifying high-favorable areas linked to porphyry Cu systems in prospecting stage. Moreover, the results demonstrated that the properly selection of geospatial exploration features has strong impact on the modeling of mineral prospectivity in Kerman porphyry belt, Iran and cause significant decrease in systemic uncertainty linked to MPM. [ABSTRACT FROM AUTHOR]

Precise prediction of water quality parameters plays a significant role in making an early alert of water pollution and making better decisions for the management of water resources. As one of the influential indicative parameters, electrical conductivity (EC) has a crucial role in calculating the proportion of mineralization. In this study, the integration of an adaptive hybrid of differential evolution and particle swarm optimization (A-DEPSO) with adaptive neuro fuzzy inference system (ANFIS) model is adopted for EC prediction. The A-DEPSO method uses unique mutation and crossover processes to correspondingly boost global and local search mechanisms. It also uses a refreshing operator to prevent the solution from being caught inside the local optimal solutions. This study uses A-DEPSO optimizer for ANFIS training phase to eliminate defects and predict accurately the EC water quality parameter every month at the Maroon River in the southwest of Iran. Accordingly, the recorded dataset originated from the Tange-Takab station from 1980 to 2016 was operated to develop the ANFIS-A-DEPSO model. Besides, the wavelet analysis was jointed to the proposed algorithm in which the original time series of EC was disintegrated into the sub-time series through two mother wavelets to boost the prediction certainty. In the following, the comparison between statistical metrics of the standalone ANFIS, least-square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), generalized regression neural network (GRNN), wavelet-LSSVM (WLSSVM), wavelet-MARS (W-MARS), wavelet-ANFIS (W-ANFIS) and wavelet-GRNN (W-GRNN) models was implemented. As a result, it was apparent that not only was the W-ANFIS-A-DEPSO model able to rise remarkably the EC prediction certainty, but W-ANFIS-A-DEPSO (R = 0.988, RMSE = 53.841, and PI = 0.485) also had the edge over other models with Dmey mother in terms of EC prediction. Moreover, the W-ANFIS-A-DEPSO can improve the RMSE compared to the standalone ANFIS-DEPSO model, accounting for 80%. Hence, this model can create a closer approximation of EC value through W-ANFIS-A-DEPSO model, which is likely to act as a promising procedure to simulate the prediction of EC data. [ABSTRACT FROM AUTHOR]

Mechanical properties of carbonate rocks (elasticity modulus (E) and uniaxial compressive strength (UCS)) are important properties in tunneling, rock excavation and rock drilling blasting. Determination of these parameters using testing rock cores is almost difficult due to the discontinuities presence and it requires well-prepared cylindrical core samples. In addition, the testing procedure is expensive and time consuming. Thus, indirect tests are often utilized to evaluate the mechanical properties. In this research, a new technique for data processing called support vector regression (SVR) improved by metaheuristic algorithms (harmony search (HS), grey wolf optimizer (GWO), cuckoo search (CS), dolphin echolocation (DE) and genetic algorithm (GA)) to estimate of mechanical properties of carbonate rocks from physical properties and petrographic characteristics is applied. The techniques were employed in an open access literature. (case study: Koohrang's third tunnel path, Iran). In these techniques, petrographic characteristics (allochem percent, carbonate percent, dolomite percent and grain size) and physical properties (saturated unit weight (γsat), S-wave velocity (Vs), dry unit weight (γd), P wave velocity (Vp)) were used as the inputs, while the mechanical properties of carbonate rocks were the outputs. Different performance concepts were used to compare the prediction models performance. The outcomes obtained show that the SVR-HS technique has robust potential for the prediction of mechanical properties of carbonate rocks based on physical properties and petrographic characteristics with high accuracy. [ABSTRACT FROM AUTHOR]

The direct measurement of uniaxial compressive strength (UCS) as one of the main important rock engineering parameters is destructive, cumbersome, difficult and costly. Therefore, the prediction of this parameter using simpler, cheaper indirect methods is of interest. In this paper, the UCS was predicted using a developed hybrid intelligent model including generalized feedforward neural network (GFFN) incorporated with imperialist competitive algorithm (ICA). To find the optimum model, 197 sets including rock class, density, porosity, P-wave velocity, point load index and water absorption from almost all over quarries of Iran were compiled. The efficiency and performance of GFFN and hybrid ICA-GFFN models subjected to different error criteria and established confusion matrixes were compared to multilayer perceptron (MLP) and radial basis function (RBF) neural network models as well as conducted multivariate regression. The hybrid ICA-GFFN with 11.37%, 14.27% and 22.74% improvement in correct classification rate over than GFFN, RBF and MLP demonstrated superior predictability level. The results indicated that the developed ICA-GFFN model as a feasible and accurate enough tool can effectively be applied for UCS prediction purposes. Using the sensitivity analyses, the P-wave velocity and rock class were identified as the most and least influences factors on predicted UCS. [ABSTRACT FROM AUTHOR]

During the past few decades, many researchers have studied the issue of air travel demand in different countries. On the other hand, the development of airports requires considerable space in the vicinity of cities which needs planning and huge investment. However, development of air travel through different airports will be affected by various factors such as population growth and economic development. The purpose of this study is to predict air travel demand in Iran. Data were provided by the Civil Aviation Organization of Islamic Republic of Iran from 2011 to 2015. Collected information includes airports of the country and destination cities all across the country. For this purpose, the artificial neural network (ANN) is used to predict the air travel demand by considering income elasticity and population size in each zone. Evolutionary meta-heuristic algorithms have been implemented in order to improve the performance of ANN. Bat and Firefly algorithms are new meta-heuristic algorithms which have been examined in this study. The results show that the use of these algorithms increases adaptation rate of neural network (NN) prediction with real data. The coefficient of determination increases from 0.2 up to about 0.9 while using the meta-heuristics NN. This represents the high rate of efficiency using this new method. [ABSTRACT FROM AUTHOR]