Parametric Modeling and Optimization of Dimensional Error and Surface Roughness of Fused Deposition Modeling Printed Polyethylene Terephthalate Glycol Parts

Polyethylene Terephthalate Glycol (PETG) is a fused deposition modeling (FDM)-compatible material gaining popularity due to its high strength and durability, lower shrinkage with less warping, better recyclability and safer and easier printing. FDM, however, suffers from the drawbacks of limited dimensional accuracy and a poor surface finish. This study describes a first effort to identify printing settings that will overcome these limitations for PETG printing. It aims to understand the influence of print speed, layer thickness, extrusion temperature and raster width on the dimensional errors and surface finish of FDM-printed PETG parts and perform multi-objective parametric optimization to identify optimal settings for high-quality printing. The experiments were performed as per the central composite rotatable design and statistical models were developed using response surface methodology (RSM), whose adequacy was verified using the analysis of variance (ANOVA) technique. Adaptive neuro fuzzy inference system (ANFIS) models were also developed for response prediction, having a root mean square error of not more than 0.83. For the minimization of surface roughness and dimensional errors, multi-objective optimization using a hybrid RSM and NSGA-II algorithm suggested the following optimal input parameters: print speed = 50 mm/s, layer thickness = 0.1 mm, extrusion temperature = 230 °C and raster width = 0.6 mm. After experimental validation, the predictive performance of the ANFIS (mean percentage error of 9.33%) was found to be superior to that of RSM (mean percentage error of 12.31%).

Modeling and Optimization of Sensitivity and Creep for Multi-Component Sensing Materials

Pressure sensors urgently need high-performance sensing materials in order to be developed further. Sensitivity and creep are regarded as two key indices for assessing a sensor's performance. For the design and optimization of sensing materials, an accurate estimation of the impact of several parameters on sensitivity and creep is essential. In this study, sensitivity and creep were predicted using the response surface methodology (RSM) and support vector regression (SVR), respectively. The input parameters were the concentrations of nickel (Ni) particles, multiwalled carbon nanotubes (MWCNTs), and multilayer graphene (MLG), as well as the magnetic field intensity (B). According to statistical measures, the SVR model exhibited a greater level of predictability and accuracy. The non-dominated sorting genetic-II algorithm (NSGA-II) was used to generate the Pareto-optimal fronts, and decision-making was used to determine the final optimal solution. With these conditions, the optimized results revealed an improved performance compared to the earlier study, with an average sensitivity of 0.059 kPa^-1 in the pressure range of 0-16 kPa and a creep of 0.0325, which showed better sensitivity in a wider range compared to previous work. The theoretical sensitivity and creep were relatively similar to the actual values, with relative deviations of 0.317% and 0.307% after simulation and experimental verification. Future research for transducer performance optimization can make use of the provided methodology because it is representative.

Multi-objective robust optimization using adaptive surrogate models for problems with mixed continuous-categorical parameters

Explicitly accounting for uncertainties is paramount to the safety of engineering structures. Optimization which is often carried out at the early stage of the structural design offers an ideal framework for this task. When the uncertainties are mainly affecting the objective function, robust design optimization is traditionally considered. This work further assumes the existence of multiple and competing objective functions that need to be dealt with simultaneously. The optimization problem is formulated by considering quantiles of the objective functions which allows for the combination of both optimality and robustness in a single metric. By introducing the concept of common random numbers, the resulting nested optimization problem may be solved using a general-purpose solver, herein the non-dominated sorting genetic algorithm (NSGA-II). The computational cost of such an approach is however a serious hurdle to its application in real-world problems. We therefore propose a surrogate-assisted approach using Kriging as an inexpensive approximation of the associated computational model. The proposed approach consists of sequentially carrying out NSGA-II while using an adaptively built Kriging model to estimate the quantiles. Finally, the methodology is adapted to account for mixed categorical-continuous parameters as the applications involve the selection of qualitative design parameters as well. The methodology is first applied to two analytical examples showing its efficiency. The third application relates to the selection of optimal renovation scenarios of a building considering both its life cycle cost and environmental impact. It shows that when it comes to renovation, the heating system replacement should be the priority.

Stable Configurations of DOXH Interacting with Graphene: Heuristic Algorithm Approach Using NSGA-II and U-NSGA-III

Nanoparticles in drug delivery have been widely studied and have become a potential technique for cancer treatment. Doxorubicin (DOX) and carbon graphene are candidates as a drug and a nanocarrier, respectively, and they can be modified or decorated by other molecular functions to obtain more controllable and stable systems. A number of researchers focus on investigating the energy, atomic distance, bond length, system formation and their properties using density function theory and molecular dynamic simulation. In this study, we propose metaheuristic optimization algorithms, NSGA-II and U-NSGA-III, to find the interaction energy between DOXH molecules and pristine graphene in three systems: (i) interacting between two DOXHs, (ii) one DOXH interacting with graphene and (iii) two DOXHs interacting with graphene. The result shows that the position of the carbon ring plane of DOXH is noticeably a key factor of stability. In the first system, there are three possible, stable configurations where their carbon ring planes are oppositely parallel, overlapping and perpendicular. In the second system, the most stable configuration is the parallel form between the DOXH carbon ring plane and graphene, and the spacing distance from the closest atom on the DOXH to the graphene is 2.57 Å. In the last system, two stable configurations are formed, where carbon ring planes from the two DOXHs lie either in the opposite direction or in the same direction and are parallel to the graphene sheet. All numerical results show good agreement with other studies.

Pareto multi-objective optimization of tandem cold rolling settings for reductions and inter stand tensions using NSGA-II

In this paper, multi-objective optimization of tandem cold rolling settings for reductions and inter-stand tensions using NSGA-II and Pareto-optimal front are investigated. In this multi-objective optimization, the total power consumption and uniform power distribution are suggested as objective functions, and reduction thicknesses in each stand and inter stand tensions were selected as problem decision variables. Analytical formulations are introduced to determine the rolling forces and power based on the Stone approach. Then, the main variables of the optimization problem, objective functions, linear and nonlinear constraints, are defined. Moreover, some empirical constraints are introduced regarding the practical limitations of cold rolling equipment and the mechanical properties of the material. At first, considering the conditions of a practical tandem rolling line, single-objective optimization is performed separately, and finally, NSGA-II was used for multi-objective optimization. Compared to the initial setting of the rolling line, the obtained single objective schedules have better performance. Moreover, the multi-objective results based on the Pareto-optimal front are investigated, and an optimized setting for rolling schedule has been suggested. Using this proposed schedule the total power consumption is reduced by more than 11% comparing to the initial setting and more uniform power distribution has been obtained in rolling stands. The normalized reductions calculated from this investigation are compared with numerical and experimental results found in the literature and the similarity was observed in the pattern of thickness reduction distribution.

Enhancing FBG Sensing in the Industrial Application by Optimizing the Grating Parameters Based on NSGA-II

Fiber Bragg grating (FBG) technology has shown a mutation in developing fiber optic-based sensors because of their tiny size, high dielectric strength, distributed sensing, and immunity to high voltage and magnetic field interference. Therefore, FBG sensors significantly improve performance and accuracy in the world of measurements. The reflectivity and bandwidth are the main parameters that can dramatically affect the sensing performance and accuracy. Each industrial application has its requirements regarding the reflectivity and bandwidth of the reflected wavelength. Optimizing such problems with multi-objective functions that might t with each other based on applications' needs is a big challenge. Therefore, this paper presents an optimization method based on the nondominated sorting genetic algorithm II (NSGA-II), aiming at determining the optimum grating parameters to suit applications' needs. To sum up, the optimization process aims to convert industrial applications' requirements, including bandwidth and reflectivity, into the manufacturing setting of FBG sensors, including grating length and modulation refractive index. The method has been implemented using MATLAB and validated with other research work in the literature. Results proved the capability of the new way to determine the optimum grating parameters for fulfilling application requirements.

Design Space Exploration of Clustered Sparsely Connected MPSoC Platforms

Heterogeneous multiprocessor platforms are the foundation of systems that require high computational power combined with low energy consumption, like the IoT and mobile robotics. In this paper, we present five new algorithms for the design space exploration of platforms with elements grouped in clusters with very few connections in between, while these platforms have favorable electric properties and lower production costs, the limited interconnectivity and inability of heterogeneous platform elements to execute all types of tasks, significantly decrease the chance of finding a feasible mapping of application to the platform. We base the new algorithms on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) meta-heuristic and the previously published SDSE mapping algorithm designed for fully interconnected multiprocessor platforms. With the aim to improve the chance of finding feasible solutions for sparsely connected platforms, we have modified the parts of the search process concerning the penalization of infeasible solutions, chromosome decoding, and mapping strategy. Due to the lack of adequate existing benchmarks, we propose our own synthetic benchmark with multiple application and platform models, which we believe can be easily extended and reused by other researchers for further studying this type of platform. The experiments show that four proposed algorithms can find feasible solutions in 100% of test cases for platforms with dedicated clusters. In the case of tile-like platforms, the same four algorithms show an average success rate of 60%, with one algorithm going up to 84%.

Application of the Non-dominated Sorting Genetic Algorithm II in Multi-objective Optimization of Orally Disintegrating Tablet Formulation

In the context of increasing application of modelling methods in the field of pharmaceutics, this study aims to reduce the weight of sildenafil orally disintegrating tablets (ODTs) and optimize their formulation through modelling methods. To achieve the goal, the back-propagation neural network (BPNN)-based non-dominated sorting genetic algorithm II (NSGA-II) was introduced to establish the models and to optimize the percentage of magnesium stearate (MgSt), crospovidone (PVPP), and croscarmellose sodium (CCNa) to obtain satisfactory candidate ODTs. Ultimately, the bioequivalence trial was conducted to verify the effectiveness of the formulation. With the support of the neural network, the model showed satisfactory results in the prediction of hardness and disintegration time of ODTs, and the pareto front obtained by the NSGA-II suggested that there was a strong "competition" between disintegration time and hardness. Since disintegration time should be given the priority, the optimal formulation was determined as 1% MgSt, 6% CCNa, and 2.6% PVPP. The bioequivalence trial results indicated a bioequivalence between the test and the reference formulations of sildenafil, and better medication experience for the test formulation. A bioequivalent formulation with better medication experience is successfully prepared using the NSGA-II. It proves that the NSGA-II is applicable to multi-objective optimization of the drug formulation.

A novel cascaded fractional fuzzy approach for control of fermentation process

In this work, a cascaded control strategy based on fractional-order fuzzy PD/PI (FOFPD/PI) is proposed for temperature control of the bioreactor. The FOFPI is used to control the ethanol concentration in the inner loop, while the FOFPD is used for temperature control of the bioreactor in the outer loop. The integer order fuzzy PD/PI (IOFPD/PI), 2DOF FOPID, 2DOF PID, and PID are also designed for comparison purposes. The design parameter of FOFPD/PI and IOFPD/PI are estimated using non-dominated sorting genetic algorithm II (NSGA-II). Results revealed that the proposed cascaded control scheme reduced the IAE by 33.5 %, 40.5%, 47%, and 64% compared to IOFPD/PI, 2DOF FOPID, 2DOF PID, and PID, respectively. Hence, it can be concluded that the proposed FOFPD/PI controller provides precise control of reactor temperature in different operating conditions compared to other controllers.

COVID-19 vaccine distribution planning using a congested queuing system-A real case from Australia

Crisis-induced vaccine supply chain management has recently drawn attention to the importance of immediate responses to a crisis (e.g., the COVID-19 pandemic). This study develops a queuing model for a crisis-induced vaccine supply chain to ensure efficient coordination and distribution of different COVID-19 vaccine types to people with various levels of vulnerability. We define a utility function for queues to study the changes in arrival rates related to the inventory level of vaccines, the efficiency of vaccines, and a risk aversion coefficient for vaccinees. A multi-period queuing model considering congestion in the vaccination process is proposed to minimise two contradictory objectives: (i) the expected average wait time of vaccinees and (ii) the total investment in the holding and ordering of vaccines. To develop the bi-objective non-linear programming model, the goal attainment algorithm and the non-dominated sorting genetic algorithm (NSGA-II) are employed for small- to large-scale problems. Several solution repairs are also implemented in the classic NSGA-II algorithm to improve its efficiency. Four standard performance metrics are used to investigate the algorithm. The non-parametric Friedman and Wilcoxon signed-rank tests are applied on several numerical examples to ensure the privilege of the improved algorithm. The NSGA-II algorithm surveys an authentic case study in Australia, and several scenarios are created to provide insights for an efficient vaccination program.

Multi-Objective Optimization of the Basic and Regenerative ORC Integrated with Working Fluid Selection

A multi-objective optimization based on the non-dominated sorting genetic algorithm (NSGA-II) is carried out in the present work for the basic organic Rankine cycle (BORC) and regenerative ORC (RORC) systems. The selection of working fluids is integrated into multi-objective optimization by parameterizing the pure working fluids into a two-dimensional array. Two sets of decision indicators, exergy efficiency vs. thermal efficiency and exergy efficiency vs. levelized energy cost (LEC), are adopted and examined. Five decision variables including the turbine inlet temperature, vapor superheat degree, the evaporator and condenser pinch temperature differences, and the mass fraction of the mixture are optimized. It is found that the turbine inlet temperature is the most effective factor for both the BORC and RORC systems. Compared to the reverse variation of exergy efficiency and thermal efficiency, only a weak conflict exists between the exergy efficiency and LEC which tends to make the binary objective optimization be a single objective optimization. The RORC provides higher thermal efficiency than BORC at the same exergy efficiency while the LEC of RORC also becomes higher because the bare module cost of buying one more heat exchange is higher than the cost reduction due to the reduced heat transfer area. Under the heat source temperature of 423.15 K, the final obtained exergy and thermal efficiencies are 45.6% and 16.6% for BORC, and 38.6% and 20.7% for RORC, respectively.

Green two-echelon closed and open location-routing problem: application of NSGA-II and MOGWO metaheuristic approaches

Nowadays organizations outsource transportation of goods or services to reduce cost which leads to a particular type of problem called open location-routing. Also, each logistic organization possesses a limited number of specific vehicles that may not be enough in certain circumstances. This issue indicates the importance of simultaneously considering both open and closed routs. On the other hand, the growing concerns about the detrimental environmental impacts of human activities reveal the necessity of paying attention to environmental issues in logistics. In this study, a bi-objective mathematical programming model is proposed for two-echelon close and open location-routing problem (2E-COLRP) including two echelons of factories, depots and customers to minimize costs and CO2 emissions. The proposed model finds the optimal routs, optimal number of vehicles and facilities as well as the locations of facilities. The augmented epsilon constraint method is used as an exact method to solve the small-sized problems. Due to complexity of model, two metaheuristic algorithms named MOGWO and NSGA-II are utilized to tackle the problems. The efficiency of two aforementioned algorithms is evaluated in terms of several indices considering 22 problem instances with various sizes. The results show that MOGWO performs better than NSGA-II.

Investigation of the Effects of Roller Spreading Parameters on Powder Bed Quality in Selective Laser Sintering

Powder spreading is one of crucial steps in selective laser sintering (SLS), which controls the quality of the powder bed and affects the quality of the printed parts. It is not advisable to use empirical methods or trial-and-error methods that consume lots of manpower and material resources to match the powder property parameters and powder laying process parameters. In this paper, powder spreading in realistic SLS settings was simulated using a discrete element method (DEM) to investigate the effects of the powder's physical properties and operating conditions on the bed quality, characterized by the density characteristics, density uniformity, and flatness of the powder layer. A regression model of the powdering quality was established based on the response surface methodology (RSM). The relationship between the proposed powdering quality index and the research variables was well expressed. An improved multi-objective optimization algorithm of the non-dominated sorting genetic algorithm II (NSGA-II) was used to optimize the powder laying quality of nylon powder in the SLS process. We provided different optimization schemes according to the different process requirements. The reliability of the multi-objective optimization results for powdering quality was verified via experiments.

Multi-objective optimal model for sustainable management of groundwater resources in an arid and semiarid area using a coupled optimization-simulation modeling

Excessive exploitation of groundwater resources can increase the concentration of pollutants in addition to the progressive drawdown of groundwater table. In this research, to achieve aquifer quantitative and qualitative (QQ) sustainable development, an optimal scenario for withdrawing from operation wells is proposed. At the first step, the aquifer QQ simulation was carried out with the GMS model. The developed code in MATLAB2018b in the second step provides the link between the simulation and the NSGA-II optimization tools. In the third step, a multi-objective coupled optimization-simulation model based on GMS and NSGA-II developed. Finally, optimal scenario was chosen based on applying the multiple criteria decision-making (MCDM) and Berda Aggregation Method (BAM). The results show that reducing the current withdrawal rate to 51.55% can establish the QQ stability of the aquifer. This decrease in groundwater abstraction has led to a 4.6 m increase in groundwater level (GWL) over 3 years (average 19 cm per month). The spatial and temporal distribution of nitrate concentration after applying the optimal discharge of wells shows the nitrate concentration in central and eastern parts of the aquifer has greatly reduced. Developed sustainable management model can be used to provide a real operation planning of wells to improvement of the QQ status of groundwater in each unconfined aquifer.

Multi-objective optimization of technology solutions in municipal solid waste treatment system coupled with pollutants cross-media metabolism issues

The environmental impact, energy conservation, and economic cost are prominent decision criteria in municipal solid waste (MSW) management, among which trade-off relationships widely exist because of different features of pollutant treatment technologies. These three objectives should thereby be simultaneously considered in the design of technology combinations in MSW treatment system (MSWTS). In addition, comprehensive characterization of environmental impact of the whole MSWTS should cover the complex pollutants cross-media metabolism in the treatment of both MSW and subsequent secondary pollution. This study developed a multi-objective optimization model to select optimal technology solutions in MSWTS. Three objectives, the minimizations of total environmental impact calculated from pollutants cross-media metabolism perspective, net energy consumption, and total cost are optimized through the second generation of the Non-dominated Sorting Genetic Algorithm (NSGA-II). Final MSW management schemes under environment, energy, and cost preferences are obtained through Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. This paper uses China's MSWTS as a case study and finds that Pareto optimal solutions can reduce the total environmental impact and the net energy consumption by 24.2% and 7.4% respectively, while increase the total cost by 18.2% in average, compared with the baseline scenario. The promotion of MSW biological treatment technologies, especially anaerobic digestion (AD), can effectively improve the environmental performance of MSWTS, while the current vigorous promotion of MSW incineration in China is not recommended. Sludge co-processing in cement kiln is highly promoted under all three types of management preferences. In summary, the proposed methodology can provide decision support for the optimal design of technology solutions in MSWTS.

A mathematical formulation and an NSGA-II algorithm for minimizing the makespan and energy cost under time-of-use electricity price in an unrelated parallel machine scheduling

In many countries, there is an energy pricing policy that varies according to the time-of-use. In this context, it is financially advantageous for the industries to plan their production considering this policy. This article introduces a new bi-objective unrelated parallel machine scheduling problem with sequence-dependent setup times, in which the objectives are to minimize the makespan and the total energy cost. We propose a mixed-integer linear programming formulation based on the weighted sum method to obtain the Pareto front. We also developed an NSGA-II method to address large instances of the problem since the formulation cannot solve it in an acceptable computational time for decision-making. The results showed that the proposed NSGA-II is able to find a good approximation for the Pareto front when compared with the weighted sum method in small instances. Besides, in large instances, NSGA-II outperforms, with 95% confidence level, the MOGA and NSGA-I multi-objective techniques concerning the hypervolume and hierarchical cluster counting metrics. Thus, the proposed algorithm finds non-dominated solutions with good convergence, diversity, uniformity, and amplitude.

An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions

The prognostic is the key to the state-based maintenance of Francis turbine units (FTUs), which consists of performance state evaluation and degradation trend prediction. In practical engineering environments, there are three significant difficulties: low data quality, complex variable operation conditions, and prediction model parameter optimization. In order to effectively solve the above three problems, an ensemble prognostic method of FTUs using low-quality data under variable operation conditions is proposed in this study. Firstly, to consider the operation condition parameters, the running data set of the FTU is constructed by the water head, active power, and vibration amplitude of the top cover. Then, to improve the robustness of the proposed model against anomaly data, the density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean outliers and singularities in the raw running data set. Next, considering the randomness of the monitoring data, the healthy state model based on the Gaussian mixture model is constructed, and the negative log-likelihood probability is calculated as the performance degradation indicator (PDI). Furthermore, to predict the trend of PDIs with confidence interval and automatically optimize the prediction model on both accuracy and certainty, the multiobjective prediction model is proposed based on the non-dominated sorting genetic algorithm and Gaussian process regression. Finally, monitoring data from an actual large FTU was used for effectiveness verification. The stability and smoothness of the PDI curve are improved by 3.2 times and 1.9 times, respectively, by DBSCAN compared with 3-sigma. The root-mean-squared error, the prediction interval normalized average, the prediction interval coverage probability, the mean absolute percentage error, and the R2 score of the proposed method achieved 0.223, 0.289, 1.000, 0.641%, and 0.974, respectively. The comparison experiments demonstrate that the proposed method is more robust to low-quality data and has better accuracy, certainty, and reliability for the prognostic of the FTU under complex operating conditions.

A multiobjective model for the green capacitated location-routing problem considering drivers' satisfaction and time window with uncertain demand

Location-routing problem is a combination of facility location problem and vehicle routing problem. Numerous logistics problems have been extended to investigate greenhouse issues and costs related to the environmental impact of transportation activities. The green capacitated locating-routing problem (LRP) seeks to find the best places to establish facilities and simultaneously design routes to satisfy customers' stochastic demand with minimum total operating costs and total emitted carbon dioxide. In this paper, features that make the problem more practical are: considering time windows for customers and drivers, assuming city traffic congestion to calculate travel time along the edges, and dealing with capacitated warehouses and vehicles. The main novelty of this study is to combine the mentioned features and consider the problem closer to the real-world case uses. A mixed-integer programming model has been developed and scenario production method is used to solve this stochastic model. Since the problem belongs to the class of NP-hard problems, a combination of the progressive hedging algorithm (PHA) and genetic algorithm (GA) is considered to solve large-scale problems. It is the first time, as per our knowledge, that this combination is implemented on a green capacitated location routing problem (G-CLPR) and resulted in satisfactory solutions. Nondominating sorting genetic algorithm II (NSGA-II) and epsilon constraints methods are used to face with the bi-objective problem. Finally, sensitivity analysis is performed on the problem's input parameters and the efficiency of the proposed method is measured. Comparing the results of the proposed solution approach with those of the exact method indicates that the solution approach is computationally efficient in finding promising solutions.

Sim-DRS: a similarity-based dynamic resource scheduling algorithm for microservice-based web systems

Microservice-based Web Systems (MWS), which provide a fundamental infrastructure for constructing large-scale cloud-based Web applications, are designed as a set of independent, small and modular microservices implementing individual tasks and communicating with messages. This microservice-based architecture offers great application scalability, but meanwhile incurs complex and reactive autoscaling actions that are performed dynamically and periodically based on current workloads. However, this problem has thus far remained largely unexplored. In this paper, we formulate a problem of Dynamic Resource Scheduling for Microservice-based Web Systems (DRS-MWS) and propose a similarity-based heuristic scheduling algorithm that aims to quickly find viable scheduling schemes by utilizing solutions to similar problems. The performance superiority of the proposed scheduling solution in comparison with three state-of-the-art algorithms is illustrated by experimental results generated through a well-known microservice benchmark on disparate computing nodes in public clouds.

Convalescent plasma bank facility location-allocation problem for COVID-19

With convalescent plasma being recognized as an eminent treatment option for COVID-19, this paper addresses the location-allocation problem for convalescent plasma bank facilities. This is a critical topic, since limited supply and overtly increasing cases demand a well-established supply chain. We present a novel plasma supply chain model considering stochastic parameters affecting plasma demand and the unique features of the plasma supply chain. The primary objective is to first determine the optimal location of the plasma banks and to then allocate the plasma collection facilities so as to maintain proper plasma flow within the network. In addition, recognizing the perishable nature of plasma, we integrate a deteriorating rate with the objective that as little plasma as possible is lost. We formulate a robust mixed-integer linear programming (MILP) model by considering two conflicting objective functions, namely the minimization of overall plasma transportation time and total plasma supply chain network cost, with the latter also capturing inventory costs to reduce wastage. We then propose a CPLEX-based optimization approach for solving the MILP functions. The feasibility of our results is validated by a comparison study using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and a proposed modified NSGA-III. The application of the proposed model is evaluated by implementing it in a real-world case study within the context of India. The optimized numerical results, together with their sensitivity analysis, provide valuable decision support for policymakers.

Multi-objective optimization of quantitative-qualitative operation of water resources systems with approach of supplying environmental demands of Shadegan Wetland

Irregular withdrawals from water resources followed by the increase of the cultivation lands and the construction of Marun and Jarahi Dams on upstream rivers of the Shadegan Wetland have led to severe hydrological changes as well as increased salinity of the wetland inflow in some periods. The aim of this study is to develop a simulator-optimizer coupling model for proper planning and management of resource allocation to the upstream of Shadegan Wetland. In addition to maximizing the supply of basin demands during the operation period, this model aims to reduce the salinity of the inflow to Shadegan Wetland. Due to the importance of the wetland as a seasonal habitat for birds and the importance of protecting its ecosystem, the development of a quantitative-qualitative optimization model for optimal use of available water resources is the aim of this study. First, based on current conditions, the prepared model is developed as a reference scenario for a future 30-year period(2021-2050). To achieve the best system efficiency in terms of quality and quantity, the optimization is performed by means of the NSGA-II algorithm. The results indicate that the optimizer model performs appropriately in supplying various demands and also decreasing the salinity of the inflow to Shadegan Wetland compared to the reference scenario so that in addition to supplying the demands with more than92% reliability in the whole system, it is expected that the salinity of the river at the entrance to Shadegan Wetland to be reduced by about50%., especially in low water months. The coupling model proposed in this research is applicable for other study areas with quantitative-qualitative operation approach and is able to detect critical points of rivers in terms of quantity and quality. This model has also the capability of providing optimal solutions for improving river conditions as well as downstream ecosystems.

Application of NSGA-II to Obtain the Charging Current-Time Tradeoff Curve in Battery Based Underwater Wireless Sensor Nodes

In this paper, a novel application of the Nondominated Sorting Genetic Algorithm II (NSGA II) is presented for obtaining the charging current–time tradeoff curve in battery based underwater wireless sensor nodes. The selection of the optimal charging current and times is a common optimization problem. A high charging current ensures a fast charging time. However, it increases the maximum power consumption and also the cost and complexity of the power supply sources. This research studies the tradeoff curve between charging currents and times in detail. The design exploration methodology is based on a two nested loop search strategy. The external loop determines the optimal design solutions which fulfill the designers’ requirements using parameters like the sensor node measurement period, power consumption, and battery voltages. The inner loop executes a local search within working ranges using an evolutionary multi-objective strategy. The experiments proposed are used to obtain the charging current–time tradeoff curve and to exhibit the accuracy of the optimal design solutions. The exploration methodology presented is compared with a bisection search strategy. From the results, it can be concluded that our approach is at least four times better in terms of computational effort than a bisection search strategy. In terms of power consumption, the presented methodology reduced the required power at least 3.3 dB in worst case scenarios tested.

Multi-objective optimization for optimal extraction of groundwater from a nitrate-contaminated aquifer considering economic-environmental issues: A case study

This paper focuses on the multi-objective optimization of the groundwater extraction scheme in the Bouein-Myandasht aquifer (Iran) in order to reduce the concentration of nitrate, originating from agricultural activities and wastewater absorbent wells. A simulation-optimization model coupling an artificial neural network (ANN) as the simulator with the non-dominated sorting genetic algorithm-type II (NSGA-II) as the optimizer, are employed. The simulator is trained by help of data generated by process-based simulation models for groundwater flow (MODFLOW) and solute transport (MT3D). The optimization objectives include (1) minimizing the contamination concentration and (2) maximizing the net benefit of the agricultural activities. The outcome of the simulation-optimization model is an optimized management strategy formed by the optimal values of the optimization parameters searched and obtained consisting of (1) seasonal groundwater extraction volume; (2) the ratio of the wastewater which should be treated before being leached into the groundwater through the absorbent wells; (3) the ratio of the fertilizers consumption; and (4) the cultivated area for each of the main crops in the study area. The results of the model suggest a groundwater extraction policy fulfilling the objectives of the optimization. The optimal operating policy also indicates that a partly conflicting relation exists between minimizing the risk of groundwater contamination and maximizing the net benefits of the agricultural activities. Hence, the focus of this paper is at finding the better and better Pareto-fronts in the objective space while dealing with the parts of the objective functions with less conflict to reach the optimal Pareto-front on which the full conflict between the objectives is held. Then, an entropy-based trade-off reflected in designating a couple of weights assigned to the couple of objectives calculated for each solution in the bi-objective space is held over the solutions lying on the optimal Pareto-front and finally, the favorite solution minimizing the weighted-distance to the ideal point in the objective space is achieved using the TOPSIS method. With this policy the regional nitrate concentration will be decreased by 36.7%, 20.45% and 21.6% in the first, second and third study sub-areas, respectively, as compared to those in the actual operation. Furthermore, the model suggests 15%, 12% and 9% wastewater treatment and also 9%, 6% and 7% decrease in the fertilizer use in the first, second, and third study sub-areas, respectively.

Site selection of straw collection and storage facilities considering carbon emission reduction

Straw recycling has generated high collection and transportation costs. Scientifically informed collection, storage, and transportation methods can reduce automobile exhaust emissions and high transportation costs. According to the relevant statistics, China's total theoretical straw resources reached 920 million tons in 2020. Due to such regional and seasonal straw surpluses, however, comprehensive utilization technologies need to be improved, and farmers' awareness of environmental protection needs to be strengthened. In some areas, open burning of straw is still practiced, causing environmental pollution and wasting resources. This study used cost and carbon emission metrics in a dual-objective planning model to plan the site selection of straw collection and storage facilities. Compared with the current manual calculation in various links in straw supply logistics, modeling can resolve the contradiction between cost and carbon emission considerations and can help meet the goal of Pareto optimum while ensuring supply, reducing costs for enterprises, and providing decision-making assistance for the government. This paper uses transportation theory and a dual-objective, mixed-integer model to study the field of biomass energy. Through the planning and design of the biomass raw material supply chain, the system efficiency is improved, and the studied company can obtain more profits. This article also explores the role of controlling carbon emissions in the field of biomass energy. It is believed that the government not only needs to guide corporate decision-making by charging carbon taxes but also needs to support enterprises in participating in the field of biomass power generation through active policy guidance.

NSGA-II as feature selection technique and AdaBoost classifier for COVID-19 prediction using patient's symptoms

Nowadays, humanity is facing one of the most dangerous pandemics known as COVID-19. Due to its high inter-person contagiousness, COVID-19 is rapidly spreading across the world. Positive patients are often suffering from different symptoms that can vary from mild to severe including cough, fever, sore throat, and body aches. In more dire cases, infected patients can experience severe symptoms that can cause breathing difficulties which lead to stern organ failure and die. The medical corps all over the world are overloaded because of the exponentially myriad number of contagions. Therefore, screening for the disease becomes overwrought with the limited tools of test. Additionally, test results may take a long time to acquire, leaving behind a higher potential for the prevalence of the virus among other individuals by the patients. To reduce the chances of infection, we suggest a prediction model that distinguishes the infected COVID-19 cases based on clinical symptoms and features. This model can be helpful for citizens to catch their infection without the need for visiting the hospital. Also, it helps the medical staff in triaging patients in case of a deficiency of medical amenities. In this paper, we use the non-dominated sorting genetic algorithm (NSGA-II) to select the interesting features by finding the best trade-offs between two conflicting objectives: minimizing the number of features and maximizing the weights of selected features. Then, a classification phase is conducted using an AdaBoost classifier. The proposed model is evaluated using two different datasets. To maximize results, we performed a natural selection of hyper-parameters of the classifier using the genetic algorithm. The obtained results prove the efficiency of NSGA-II as a feature selection algorithm combined with AdaBoost classifier. It exhibits higher classification results that outperformed the existing methods.