جستجوی مقالات مرتبط با کلیدواژه "multi-objective optimization" در نشریات گروه "مواد و متالورژی"

Dredged sediments from dredging operations pose environmental hazards and disposal challenges. The geopolymerization method for treating these sediments offers an eco-friendly alternative to Portland cement. This study develops geopolymer materials using uncalcined dredged sediment, fly ash, and an alkali activator (sodium hydroxide and sodium silicate). The compressive strength of the geopolymer was tested after 7 days curing under two conditions (ambient and at 60°C for the first 24 hours) and analyzed using Box-Behnken Design and Response Surface Method. The study examined three variables: sodium hydroxide molarity (6M to 12M), sediment-to-total solids ratio (0.3 to 0.9), and sodium silicate solution-to-sodium hydroxide solution ratio (1 to 3). High-accuracy prediction models were established, and the desirability function was used to optimize the mixture proportions for the two curing conditions. The multi-objective optimization aimed to meet the strength requirement of TCVN 6477:2016 standards for concrete bricks, maximize dredged sediment content, and minimize sodium silicate usage. The optimal mixture achieved a compressive strength of 7.5 MPa at 7 days, with 37.53% dredged sediment for ambient curing and 45.59% for drying curing. Compared to ambient curing, drying curing enables a higher sediment content and a reduced use of NaOH. Furthermore, the geopolymer reactions and gel matrix formation of the optimal mixture were confirmed by FTIR spectra and SEM observations.

Mill optimization has many economic benefits. Semi autogenous grinding mills are complex multi-input and multi-output systems that are difficult to optimize. The purpose of this study is to examine the functions of the wear of lifters, power draw and product size distribution. The design variables are mill speed, ball filling, slurry concentration and slurry filling. To achieve this aim, a pilot mill was carried out. The experimental results used to create training cases for the artificial neural network and then the optimization of the design variables is conducted by multi-objective genetic algorithm. Level diagrams are then used to select the best solution from the Pareto front. Finally, the response surface methodology has been used to study the interaction between the design parameters. The results showed that the best grinding occurs at 70-80% of the critical speed and ball filling of 15-20%. Optimized grinding was observed when the slurry volume was 1-1.5 times of the ball bed voidage volume and the slurry concentration was 60-70%. Additionally, variables with the largest effect on the process are mill speed and ball filling.

This study presents the optimized shape and thickness of thin continuous concrete shell structures, minimizing their weight, deflection, and elastic energy change while meeting the performance requirements and minimizing material usage. Unlike previous studies that focused on single-objective optimization, this research focuses on multi-objective optimization (MOO) by considering three objective functions. This combination of objective functions has not been reflected in previous research, distinguishing this study. The computational design workflow incorporates a parametric model, multiple components for measuring objective functions in the grasshopper of Rhino, and a metaheuristic algorithm, the non-dominated sorting multi-objective genetic algorithm (NSGA-II), as the search tool, which was coded in Python. This workflow allows us to perform form-finding and optimization simultaneously. To demonstrate the effectiveness of this metaheuristic algorithm in structural optimization, we applied it in a case study of a well-known shell designed using the physical prototyping hanging model technique. Interpretations of samples of optimized results indicate that although solution 1 weighs nearly the same as solution 2, it has less deflection and strain energy. Solution 3, with a three-fold mass, has significantly less deflection and strain energy than solution 1 and solution 2, with deflection reductions of over 50 and 17%, respectively. Solutions 3 and 4 show better deflection and strain energy performance. Furthermore, a comparison of the MOO results with the Isler shell revealed that this method found a solution with less weight and deflection while being stiffer, confirming its practicality. The study found that MOO is a reliable method for form-finding and optimization, generating accurate and reasonable results.

In today's dynamic and unpredictable world, the planning and management of humanitarian supply chains hold paramount importance. Efficient logistics management is crucial for effectively delivering essential aid and resources to affected areas during disasters and emergencies, ensuring timely support and relief to vulnerable populations. In this research, we addressed a novel humanitarian supply chain network design problem that considers product differentiation and demand uncertainty. Specifically, we simultaneously incorporate non-perishable, perishable, and blood products as critical components of the network. The problem is formulated as a multi-objective mixed-integer linear programming model aiming to minimize the total cost and total traveled distance of products by making location, allocation, and production decisions. To enhance realism, we account for demand uncertainty in affected areas. To tackle this challenging problem, we proposed a two-phase solution methodology. Firstly, we employed a robust optimization approach to establish a deterministic counterpart for the stochastic model. Subsequently, an efficient fuzzy programming-based approach reformulates the model into a single-objective form, effectively accommodating decision-makers' preferences. Numerical instances are solved to investigate the performance of the model and solution methodologies. The results demonstrate the effectiveness of our fuzzy approach in finding non-dominated solutions, enabling decision-makers to explore trade-offs. Also, sensitivity analyses were conducted to provide more insights. Finally, some suggestions are presented to extend the current work by feature researchers.

Adjusting the operating parameters to optimize the performance of the scroll expander has been a hot research topic among scholars. This paper innovatively combines the response surface method and NSGA2 algorithm for parameter optimization. This novel method can accurately predict the optimal operating parameters of the scroll expander and improve the overall efficiency of the scroll expander. Initially, a three-dimensional transient simulation model of the scroll expander was established, and the effects of three key operating parameters (suction pressure, exhaust pressure, and rotational speed) on the output power and isentropic efficiency of the scroll expander were analyzed through numerical simulation. On this basis, the response surface model between the input parameters and the objective function  was established by using the response surface methodology. Consequently, three different optimization algorithms  were compared, and it was found that NSGA-II had a better performance both in terms of convergence and solution performance,. Threfore, the NSGA-II algorithm was used for the multi-objective optimization. Under the premise of considering the maximum output power and isentropic efficiency, based on the established response surface model, the Pareto optimal solution was used to determine the optimal combination of its operating parameters: suction pressure of 1.62 MPa, exhaust pressure of 0.45 MPa, and rotational speed of 2,099.58 rpm.Finally, the numerical model is verified by the laboratory-built test bed of the Organic Rankine cycle low-temperature waste heat oil-free power generation system. The experimental results match well with the numerical simulation results and verify the model accuracy. The results from this pioneering and thorough thr study will provide a solid benchmark for the development and refinement of upcoming scroll machines.

Allocating renewable energy systems (RESs) in an electrical distribution system (EDS) is crucial to achieving various objectives. However, their intermittency presents several challenges. In this connection, an efficient meta-heuristic pathfinder algorithm (PFA) is employed to determine the optimal location and size of photovoltaic (PV) and wind turbine (WT) systems, along with energy storage systems (ESS) and capacitor banks (CB) for both grid and islanding modes of operations. An objective function was formulated for loss reduction, greenhouse gas (GHG) emissions, and voltage profile improvement. The simulation results for the IEEE 33-bus EDS system are shown for two cases: grid-connected and islanding. The computational effectiveness of the PFA was compared with that reported in the literature. The PFA results showed an outstanding ability to resolve difficult optimisation problems. In addition, the optimal size of the RES when the network operates in the grid-connected mode can significantly improve the performance. The real power losses and GHG emissions were reduced by 48.49 % and 67.75% with PV systems and the other, respectively, whereas WT systems they are reduced to 69.68 % and 67.85 %, respectively. However, a combination of ESS, CB, and PV/WT can render the EDN sustainable for the islanding mode of operations.

Cloud manufacturing (CMfg) is a new advanced manucatring model developed with the help of enterprise information technologies under the support of cloud computing, Internet of Things and service-based technologies. CMfg compose multiple manufacturing resources to provide efficient and valuable services. CMfg has a highly dynamic environment. In this environment, many disruptions or events may occur that lead the system to unplanned situations. In CMfg, a series of service providers are scheduled for production. During the production operation, some of them may be damaged, stopped, and out of service. Therefore, rescheduling is necessary for the continuation of the production process according to the concluded contracts and initial schedule. When any disruptions or other events occurred, the rescheduling techniques used to updating the inital schedule. In this paper, the dynamic rescheduling problem in CMfg is analyzed. Then the multi-objective rescheduling in CMfg is modeled and defined as a multi-objective optimization problem. Defining this problem as a multi-objective optimization problem provides the possibility of applying, checking and comparing different algorithms. For solving this problem, previous optimization methods have improved and a multi-objective and elitist algorithm based on the Jaya algorithm, called advanced multi-objective elitist Jaya algorithm (AMEJ) is proposed. Several experiments have been conducted to verify the performance of the proposed algorithm. Computational results showed that the proposed algorithm performs better compared to other multi-objective optimization algorithms.

Nowadays, the notion of plug-in electric vehicle (PEV) as a valuable tool of energy management has been extensively employed in smart distribution grids. The main advantage of clean energy as well as elastic behaviour of operation in both electrical load/generation modes can sufficiently justify the utilization of such emerging technology. Moreover, the specific capability of renewable energy sources (RESs) in terms of contribution in PEV smart charging/discharging scheme would cause to remarkable techno-economic benefits in smart grids. However, the load demand, RES generation and also the electrical energy price encounter with uncertainty in practice required to be properly handled. Hence, a non-deterministic optimization model based on information gap decision theory (IGDT) is proposed in this paper to specify a robust PEV smart charging pattern. To solve the multi-objective proposed IGDT-based PEV smart charging (IGDT-PSC) model, the multi-objective version of particle swarm optimization (MOPSO) is utilized to define a set of Pareto optimal solutions. Furthermore, the final solution among the Pareto solutions is selected by means of a linear fuzzy satisfaction rule. The simulation results for a test smart microgrid comprising a PEV, a set of RES units and a load demand verify the effectiveness of the proposed IGDT-PSC model.

The important objective of a building must be to provide a comfortable environment for people. Heating ventilation and air conditioning systems provide a comfortable environment but they have high energy consumption. Therefore, designing an energy-efficient building that balances energy performance and thermal comfort is necessary. Choosing effective parameters for energy performance is an important factor in achieving this goal. This research aims to produce a methodology for multi-objective optimization of daylight and thermal comfort in order to study the effect of wall material and shading of an office building (Tehran a basic-location). The building simulation was developed and validated by comparing predicted daylight and thermal comfort hours based on tests and training in Jupiter Notebook. The sensitivity analysis uses a multiple linear regression method. Secondly, optimization is based on a genetic algorithm with effective parameters to optimize daylight and thermal comfort performance. For this, we developed a parametric model using the Grasshopper plugin for Rhino and then used Honeybee and Ladybug plugins to simulate thermal comfort and daylight, and finally used Octopus engine to find an optimization solution. The result of this paper is essential as a preliminary analysis for building optimization in the open-plan office.

The vehicle routing problem as a challenging decision problem has been studied extensively. More specifically, solving it for a mixed fleet requires realistic calculation of the performance of electric and combustion vehicles. This study addresses a new variant of the vehicle routing problem for a mixed fleet of electric and combustion vehicles under the presence of time windows and charging stations. A bi-objective mixed-integer programming model is developed which aims at minimizing cost and pollution level concurrently. To accurately quantify travel quantities, such as fuel consumption, emission, and battery charge level, a set of realistic mathematical formulas are used. The model is first converted to a single-objective counterpart using the epsilon-constraint method and a simulated annealing algorithm is tailored to obtain Pareto optimal solutions. A discussion is also made on how the final solution can be selected from the Pareto frontier according to the design objectives. The presented framework can find a set of Pareto optimal solutions as a trade-off between cost and pollution objectives by considering different combinations of electric and combustion vehicles. It was shown that those solutions that involve more electric fleet than combustion fleet, lead to higher total costs and smaller emissions and vice versa.

In this study, the design of a nanofluid driven shell and tube heat exchanger is optimized, for the first time, by use of three multi objective algorithms. Two different operating conditions are investigated to compare the performance of the algorithms based on an economic model (cost function). Based on the obtained results, the Genetic, Particle Swarm and Jaya optimization algorithms can all improve the design. The amount of design improvement by each method is 9.66%, 10.63% and 10.9% respectively. Also from the view point of optimization time, Jaya optimization algorithm has relatively less CPU time than the other two algorithms, which in fact, reduces computational costs in complicated computations. Finally, due to the good performance of Jaya optimization algorithm in comparison with other considered algorithms, the performance of the heat exchangers is evaluated for using Ag, TiO2 and Al2O3 nanofluids of 0.5% to 5 vol.% by this algorithm. A performance evaluation factor (PE) is introduced as the criterion for simultaneous investigation of thermal and hydraulic performance of nanofluids. The results show that silver nanofluid, among other ones has better performance.

This study investigates the effect of friction stir back extrusion (FSBE) input parameters such as traverse speed, rotational speed, and wire diameter on the mechanical and microstructural properties of the produced wire. Numerous experiments were performed with different input parameters, and the grain size, hardness, and ultimate pressure strength (UPS) of each of the produced wires were investigated. In addition, to better understand the effect of input parameters, the process was simulated using the finite element method (FEM) model, and the temperature, material flow, and strain distributions in the wires were investigated. Then, using the artificial neural network (ANN), a relationship was obtained between the input parameters of the process, such as traverse speed, rotational speed, and wire diameter, with the mechanical and microstructural properties of the produced wires. This relationship was then used in a hybrid multi-objective optimization to find the optimal process parameters. Due to the higher importance of UPS in comparison to the grain size and microhardness, the weighting of 0.6, 0.2, and 0.2 were used in the TOPSIS model, and the optimum input parameters were achieved as 6 mm, 36.35 mm/min, and 456 rpm, for the traverse speed, rotational speed, and wire diameter, respectively.

Multi-hole orifices have better performance than single-hole orifices. In this paper, multi-objective optimization of multi-hole orifices is performed using a Fluid-Solid Interaction (FSI) analysis and multi-objective genetic algorithm (NSGA II). In all numerical analysis, the governing equations of the solid and the governing equations of the fluid are carried out for orifice and fluid around orifice respectively. All calculations are made for a 16-hole orifice with circular holes. The design variable in the optimization process is the distance between the holes of the orifice and thus the amount of shrinkage or expansion of the orifice geometry. The objective functions are the pressure drop created on the sides of the orifice, the deformation and tension created in the orifice structure, which should be maximized, minimized and minimized respectively. In the results section, the Pareto front are presented which represent useful information for designing the multi-hole orifices geometry, and five orifices are also introduced as final design options that have better performance. The results of the sensitivity analysis of the various parameters are also presented and discussed in detail in the multi-hole orifices.

Heaters are one of the central parts of natural gas reduction stations using turboexpanders to prevent the formation of hydrate and corrosion failure. This study intends to design a fired heater by applying a combustion sub-model to derive an optimal model for this kind of application. This model is developed to accurately consider all subsections of the fired heater namely radiation, convection, and shield sections, as well as flue gas composition, and its volume. Within this context, a multi-objective optimization is employed to identify the optimal design of the gas-fired heater in the natural gas reduction station for the Ramin power plant case study. The total economic and environmental costs, together with modified exergy efficiency, are selected as objective functions. Multi-criteria-decision-making-method is employed on Pareto frontiers optimal curve to suggest the optimal solution. Results show that the developed model can outperform previous models in thermal efficiency with relatively similar costs. Besides, the optimal point in Pareto suggested by the decision-making-method accounts for a higher modified exergy efficiency (1.3%) than the counterpart, which thermal efficiency is regarded as an objective function. At the same time, its total cost remained almost constant. The effects of changes in each of the design parameters on the objective functions are also evaluated.

The researches on environmental and sustainability are an active topic, especially in the waste management. As such, the hazardous waste optimization is an active research topic in developing countries which may be integrated with carbon emissions and green subjects. This grand challenge motivates the current research to contribute a new multi-objective optimization model to address the green hazardous waste location-routing problem. The proposed multi-objective optimization model establishes four objectives simultaneously for the first time. In addition to the total cost and the greenhouse gas emissions of the transportation systems as the two main objectives, another objective function aims to minimize the risk of transportation of the hazardous waste alongside the waste residue associated with the people’s exposure around transportation paths. Furthermore, the total risk linked with the population in a certain radius around the treatment and disposal centers is minimized. As the proposed model is complex with conflicting objectives, several multi-objective decision making (MODM) tools are employed and compared with each other based on different test problems associated with an industrial example. Based on the solution quality and the computational time, the technique for the order of preference by similarity to the ideal solution (TOPSIS) is selected as the strongest technique to assess the performances of all five MODM methodologies.

In this paper, the energy absorption features of tri-layer explosive-welded deep-drawn cups subjected to quasi-static axial compressive loading are investigated numerically and experimentally. To produce the cups, tri-layer blanks composed of aluminum and stainless steel alloys were fabricated by an explosive-welding process and formed by a deep drawing setup. The quasi-static tests were carried out at a rate of 2 mm/min. Based on the structure of the tri-layer cups and to calculate the energy absorption features of these structures, a numerical model was established and validated by experimental findings. Moreover, based on a surrogate model and using non-domain sorting genetic algorithm II, multi-objective optimizations were performed on specific energy absorption and initial peak load. The results indicated that the total absorbed energy and mean crush force of the pure stainless steel tri-layer cup were about 5.8 and 5.7 times the values of those for the pure aluminum specimen, respectively.

In this paper, for the first time, a comprehensive experimental study is performed on hydroforming process of metallic bellows. For this purpose, the effects of the main process parameters and their interactions on the characteristics of hydroformed metallic bellows are investigated using Response Surface Methodology (RSM). The selected parameters as input variables are internal pressure, die stroke and die fillet. The measured characteristics of metallic bellows are convolution height and thickness of the top point of bellows congress. A set of experiments are carried out and the convolution height and thickness of the top point of bellows congress are measured. Then a mathematical model is developed according to the second-order linear regression equations to maximize the convolution height and thickness of the top point of bellows congress. The results show that the increase in the convolution height and decrease in the thickness of the top point of bellows congress will occur by increasing the internal pressure and die stroke. Also, the convolution height and thickness of the top point of bellows congress are increased with an increase in the die fillet.

This paper presented a new two-stage green supply chain network, in which includes two innovations. Firstly, it presents a new multi-objective model for a two-stage green supply chain problem that considers the amount of shortage in the network, reworking, and carbon-trading cost produced in the green supply chain. Secondly, because of the complexity of this model, it uses a new multi-objective interior search algorithm (MOISA) to solve the presented model. The obtained results of the proposed algorithm were compared with the results of other multi-objective meta-heuristics, namely MOPSO, SPEA2, and NSGA-II. The outcomes demonstrate that the proposed MOISA gives better Pareto solutions and indicates the superiority of the proposed algorithm in most cases. This paper presented a new two-stage green supply chain network, in which includes two innovations. Firstly, it presents a new multi-objective model for a two-stage green supply chain problem that considers the amount of shortage in the network, reworking, and carbon-trading cost produced in the green supply chain. Secondly, because of the complexity of this model, it uses a new multi-objective interior search algorithm (MOISA) to solve the presented model. The obtained results of the proposed algorithm were compared with the results of other multi-objective meta-heuristics, namely MOPSO, SPEA2, and NSGA-II. The outcomes demonstrate that the proposed MOISA gives better Pareto solutions and indicates the superiority of the proposed algorithm in most cases.