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Introduction/purpose: Cutting is a typical material process. However, an appropriate solution for simultaneous optimization of material machinability and tool life in material cutting processes has not been obtained yet. In this article, probabilistic multi-objective robust design (PMORD) is expounded and the robust design problem in the simultaneous optimization of material machinability and tool life is analyzed by taking the machining of ferrite-bainite dual-phase steel as an example. Methods: According to PMORD, the arithmetic mean and its deviation of various performance and utility indexes of alternative schemes are evaluated as twin independent responses, which respectively contribute a part of the partial preferable probability to the performance indexes. In the evaluation, the arithmetic average of the utility index is taken as the representative of the utility attribute, and the evaluation is made in accordance with the function or preference of the utility index. However, the deviation term is generally used as an unbeneficial type of the index (that is, the smaller the better) to participate in the evaluation. The product of the two parts of partial preferable probability is the actual partial preferable probability of the corresponding performance index. The product of the partial preferable probability of all performance utility indexes gives the total preferable probability of the corresponding scheme, which is the only index for each scheme to participate in the competition in robust design. Results: The optimization result of this example is that the tool life is 1297.3333 s (standard deviation is 2.0817 s) and the surface roughness is 2.22mm (standard deviation is 0.2mm), while the corresponding working conditions are that the heat treatment temperature of the material is 790°C, the cutting speed is 150m/min, the feed speed is 0.15mm/rev, and the cutting depth is 0.2mm. Conclusion: The example of the parameter optimization of cutting of ferrite-bainite dual-phase steel by means of PMORD indicates the rationality of the appropriate solution.

Since the optimization results obtained by topology optimization depend on the size of the design domain, it is desirable to optimize the design domain as well during topology optimization. In this study, we propose a method to optimize the structural topology for solid structures by topology optimization based on the SIMP (Solid Isotropic Material with Penalization) method, while optimizing the design domain by shape optimization. In other words, we develop a simultaneous optimization method of shape and topology for structures. Under the constraints of volume and equilibrium equations, the compliance as the objective function is minimized. After formulating this design optimization problem, a sensitivity function is derived using the Lagrange multiplier method and adjoint method, which is applied to the H1 gradient method to determine the optimal shape and topology. The introduction of the H1 gradient method allows to optimize the shape and topology while simultaneously smoothing the outer shape and obtaining the topology without gray scale and checker board issues and also decreasing the objective function. The effectiveness of the method for topology optimization with the optimized design domain by shape optimization, or the simultaneous design optimization of solid structures is confirmed using numerical examples.

Additive manufacturing (AM) technology is used in sectors such as automotive and aerospace, due to its advantages in producing complex and lightweight structures. However, it is necessary to reduce the total manufacturing costs for AM technology to become widespread. The topology optimization (TO) studies in the literature typically optimize only the design without taking into account the manufacturing phase or sequentially optimize the topology first and then the process parameters. On the other hand, simultaneous optimization of topology together with process parameters provides more efficient and less costly solutions. This paper describes a strategy for simultaneous optimization of topology along with process parameters of the laser-powder bed fusion (L-PBF) process. The topology, laser power, scanning speed, energy density, and yield strength are controlled by integrating the overall process–property–structure–performance relationship of the L-PBF process into the optimization. The proposed simultaneous optimization method aims to minimize the total cost function including material, manufacturing, and energy costs. Moreover, the constraint functions of the optimization include the volume fraction, the strength of the structure, and the energy density calculated according to the process parameters. The proposed method is successfully applied to three different design problems as cantilever beam, MBB beam, and L bracket, respectively. The results of different TO methods including conventional TO (compliance minimization), structural TO (similar to stress-constrained TO), and sequential process parameters and topology optimization are compared with the results of the proposed method. It is found that the proposed method provided the minimum cost results, and the obtained designs met the structural requirements. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach that simultaneously integrates the layout design with the scalable reconfigurable manufacturing systems (RMS). In RMS, layout design is developed to cope with the need for reconfiguration between multiple periods. The reconfiguration concept is a basic feature of RMS that enables systems to change and cost-effectively rearrange their components. Machines in the RMS are reconfigurable machine tools (RMTs) that could change their capability (capacity scalability) or function (different operation) by adding or removing attached modules. The proposed approach is integrating simultaneously the RMT layout problem with the scalable RMS where, the arrangement of the resulting RMTs within each cell and the arrangement of cells are determined based on the actual dimensions of RMT. To illustrate the applicability and the efficiency of the proposed approach, numerical examples are generated and used to test the model's effect of considering machine reconfiguration. The results show that incorporating the reconfiguration concept and layout design decreases the total cost and the machines required in the system. Finally, a MATLAB solver based on the GUROBI optimization program is used to solve the proposed methodology optimally. [ABSTRACT FROM AUTHOR]

Technical indicators and mining sequence are often optimized separately when optimizing metal mine production. This approach ignores the mutual influence between indicators and mining sequence, and the optimization results may not reach the global optimum. This study focuses on the entire production process of a metal mine and establishes a model and creates an algorithm for the simultaneous dynamic optimization of the technical indicators and mining sequence of a metal mine. A model of the dynamic relationships between technical indicators is initially created in order to derive an optimization model that dynamically optimizes both the technical indicators and the mining sequence of a metal mine. A hybrid coding AADE algorithm is developed to solve the optimization equation. A test case of the Huogeqi copper mine is presented to demonstrate the use of the model and the algorithm. The simultaneous dynamic optimization of technical indicators and mining sequence produced greater NPV in the test case than the optimization of technical indicators alone; NPV increased by 1161.01 104 CNY. In addition, the hybrid coding AADE algorithm was compared with hybrid coding GA, DE and ADE algorithms. The hybrid coding AADE algorithm performed searches significantly better than the other three hybrid coding algorithms in solving the simultaneous dynamic optimization for technical indicators and mining sequence.

Introduction/purpose: New robust design by means of probability-based multi-objective optimization takes the arithmetic mean value of the performance indicator and its deviation as twin independent responses of the performance indicator. The aim of this article is to check the applicability of new robust design in optimizing machining process parameters. To conduct the examination in detail, the robust design for optimal cutting parameters to minimize energy consumption during the turning of AISI 1018 steel at a constant material removal rate is applied as well as the concurrent optimization of the machining process parameters and the tolerance allocation of a spheroidal graphite cast iron piston. Methods: In the spirit of the probability-based method for multi-objective optimization, the arithmetic mean value of the performance indicator and its deviation are taken as two independent responses of the performance indicator to implement robust design. Each of the above twin responses contributes one part of the partial preferable probabilities to the performance indicator of the alternatives in the treatment. The arithmetic mean value of the performance indicator should be assessed as a representative of the performance indicator according to the function or the preference of the performance indicator, and the deviation is the other index of the performance indicator, which has the characteristic of the 99 smaller-the-better in general. Furthermore, the square root of the product of the above two parts of the partial preferable probability forms the actual preferable probability of the performance indicator. Moreover, the product of partial preferable probabilities gives the total preferable probability of each alternative, which is the overall and unique index of each alternative in the robust optimum. Results: The paper gives the rational optimum cutting parameters for minimizing energy consumption during the turning of AISI 1018 steel at a constant material removal rate and the concurrent optimization of the machining process parameters and the tolerance allocation of a spheroidal graphite cast iron piston. Conclusion: The application study indicates its rationality and convenience of new robust optimization in the optimization of machining process parameters.

The Bi 2 Te 3 -based alloys with atomically layered structure are the best thermoelectric performance at the vicinity of room temperature. However, the inferior mechanical properties due to weak van der Waals interactions between layers are the obstacle to further promote the widespread commercialized application. Here, the grain refinement BiSbTe alloys prepared by ball milling and spark plasma sintering (grain boundary engineering) is reported. The microstructure characterization results indicate that ball milling can significantly reduce the grain size. The grain boundary engineering can significantly improve Seebeck coefficient about 23 %, maintain large hall mobility and reduce the carrier thermal conductivity about 48 %, and the lattice thermal conductivity is slightly decreased about 15 % by enhanced grain boundary phonon scattering. The highest average ZT reaches 1.2 in the range of 300–400 K for grain refinement sample, which is beneficial to improve the conversion efficiency of thermoelectric devices, and the Vickers hardness of the corresponding sample is increased by 58 %. The easy-to-operate strategy of grain refinement caused by ball milling provides a way to mass produce high performance and robust mechanical properties Bi 2 Te 3 -based thermoelectric devices. • The grain boundary engineering can significantly reduce thermal conductivity. • The coordinated regulation of electro-thermal-mechanical properties was achieved. • Our work provides a method to the large-scale production of Bi 2 Te 3 alloys. • The high Vickers hardness can promote the development of thermoelectric devices. [ABSTRACT FROM AUTHOR]

Screening is a key unit in the coal cleaning process. One of the most widely used screening technologies for solving the technical problems associated with coal accumulation is the multi-stage variable-inclination equal-thickness screening (MSVIETS) method. In this work, the Box-Behnken response surface methodology (BBRSM) was used to explore the synergistic mechanism of multiple operating parameters in the screening process. The simultaneous optimization of multiple parameters was conducted to obtain the optimal operating conditions and the significance order of the parameters. The significance order of the parameters affecting the screening efficiency η was β > f> Δθ. The parameter conditions for the model optimization experiments were set as β = 59.8°, f = 11.54 Hz, and Δθ = 4.16°. The screening experiments with different percentages of coarse and fine particles were carried out. The screening efficiency was higher than 86%, the separation accuracy was less than 1.1 mm, and the difference between the experimental results and the model prediction results was small. This verified the accuracy and the reliability of the prediction results of the response surface model. [ABSTRACT FROM AUTHOR]

Due to the lightly damped resonance and intrinsic nonlinearities, it is difficult for the piezoactuated nanopositioning stage to realize high-bandwidth and high-accuracy control. To handle these limitations, in this work, a dual-loop control scheme based on state-feedback-based modal method is designed to both actively damp and stiffen the resonant mode and to suppress the effects of nonlinearities of the piezoactuated nanopositioning stage. In this scheme, the state-feedback-based modal controller is first designed in the inner loop to enlarge both the damping ratio and natural frequency of the first resonant mode. Then, a proportional–integral (PI) controller is utilized in the outer loop for eliminating the tracking errors caused by other disturbances and nonlinearities including hysteresis and creep. To maximize the control bandwidth of system under the proposed dual-loop scheme, an optimization method is thus proposed for simultaneously tuning the parameters of the inner and the outer loop controllers. Finally, to validate the proposed dual-loop control scheme, comparative experiments are carried out on a piezoactuated nanopositioning stage. Results demonstrate that the proposed control scheme improves the bandwidth of the system from 497 Hz (with PI control) and 1543 Hz (with a commonly used positive acceleration, velocity, and position damping control and a PI controller) to 6546 Hz, which is 664 Hz larger than the first resonant frequency of the original system, validating the effectiveness of the proposed dual-loop scheme on high-bandwidth control. Note to Practitioners—The demand of high-bandwidth and high-accuracy piezoactuated nanopositioning stages increases rapidly. However, the lightly damped resonance of the mechanism and the intrinsic nonlinearities of the piezoelectric actuator limit the tracking performance of the stage. A dual-loop control structure is adopted in this work to improve the tracking performance of the nanopositioning stage. Different from most of the vibration control methods proposed in the literature which aimed only at improving the damping ratio, a state-feedback-based modal controller is designed in the inner-loop for improving both the damping ratio and the stiffness of the system. This task is realized by re-placing the resonant poles of the system to the optimized location. The outer-loop controller adopts the high-gain PI control for eliminating the tracking errors. More importantly, in order to realize the high-bandwidth and high-accuracy control, a numerical optimization method is proposed for simultaneously tuning the parameters of the controllers in inner and outer loops. The controller design is simple, and it can be applied to other systems with second or higher order in which the first resonant mode dominates the system dynamics. [ABSTRACT FROM AUTHOR]

This study proposes a simultaneous optimization model that considers flow assignment and vehicle capacity for the problem of transit network design to determine the route structure and frequencies simultaneously. The problem is focused on reducing the total travel time and the number of transfers. A heuristic algorithm is developed to solve this problem. In the proposed algorithm, the initial routes are generated according to a changing demand matrix, which can reflect the real-time demand with transfers and ensure that the direction of route generation maximizes the percentage of direct service. A regulating method for a sequence of stops is used during route generation to guarantee the shortest trip time for a formed route. Vehicles are allocated to each route according to the flow share. The concept of vehicle difference is introduced to evaluate the distinction between actual allocated vehicles and required vehicles for each route. The optimization process of frequencies based on vehicle difference can ensure that the solution meets the constraints. Two scale networks are used to illustrate the performances of the proposed method. Results show that route structure and frequencies can be optimized simultaneously through the proposed method. Different scenarios are created to test the algorithm properties via various parameter values. The test result indicates that the upper bound is a key parameter to balance the proportion of direct service and average in-vehicle travel time (AIVTT), and the increased number of planning routes can improve the proportion of direct service. [ABSTRACT FROM AUTHOR]

Traditionally, the shell and tube heat exchanger (STHE) design minimizes the total annual cost subject to allowable pressure drop. However, it is often insufficient when considering equipment limitations and uncertain disturbance factors. Design inefficiency or improper operation of overall process systems could potentially occur. A rigorous design strategy of STHE based on the flexibility index analysis method is proposed to address the above issues. The genetic algorithm with rigorous constraints is considered for the STHE design optimization. Simultaneously, the flexibility index (FI) is incorporated to quantify the uncertain factors of the STHE operation. The STHE load capacity and the corresponding uncertain disturbance factors affecting the STHE operability are evaluated. In addition, design workflows incorporating flexibility index are also discussed. Simultaneous consideration of the flexibility index in the optimization procedure can improve the STHE design operability under the expected range of disturbance factors and lower the total cost.

According to the requirements of the aerospace industry for high strength, high stiffness, and lightweight structural parts, topology optimization has been proved to be an effective product design method. As one of the most conceptual and prospective structural optimization design methods, topology optimization intends to seek the optimal layout of materials in an allowed design region under a given load and boundary conditions. Thus, the object of study in the article is the method of topological optimization of aircraft structures. The goal of this article is to analyze the existing approaches, algorithms, as well as application of the method of topological optimization in the aerospace field in applied problems. The tasks are to describe the existing various approaches methods, features, and research directions of topological optimization as well as to study the possibility of application in the manufacturing process of composite structures. The following results were obtained. The optimization methods are briefly explained and compared, and the advantages and limitations of each approach are discussed. The various ways of simultaneous optimization of fiber orientation and structural topology were described and analyzed. The features of different methods of continuous fiber orientation optimization method were reviewed. The discrete fiber orientation optimization methods were represented. The possibility of multi-scale concurrent topological optimization was described. The combination of topology optimization and additive manufacturing was considered. Finally, the topology optimization of FRC structures which have been resolved in literature are reviewed and the potential research fields requiring more investigation are pointed out. Conclusions. In the article, a comprehensive review of the topology optimization design of FRC structures was presented. The promising way is to combine topology optimization with additive manufacturing techniques. However, these proposed methods may not suitable for other more complex problems, such as bucking stability and natural frequency. Hence, the topology optimization design of complex FRC components under complicated conditions is the main challenge in the future. This can be a new trend in the topology design of FRC structures.

Abstract Optimization of the placement and operational conditions of oil wells plays an important role in the development of the oilfields. Several automatic optimization algorithms have been used by different authors in recent years. However, different optimizers give different results depending on the nature of the problem. In the current study, a comparison between the genetic algorithm and particle swarm optimization algorithms was made to optimize the operational conditions of the injection and production wells and also to optimize the location of the injection wells in a southern Iranian oilfield. The current study was carried out with the principal purpose of evaluating and comparing the performance of the two most used optimization algorithms for field development optimization on real-field data. Also, a comparison was made between the results of sequential and simultaneous optimization of the decision variables. Net present value of the project was used as the objective function, and the two algorithms were compared in terms of the profitability incremental added to the project over twelve years. First, the production rate of the producers was optimized, and then water alternating gas injection wells were added to the field at locations determined by engineering judgment. Afterward, the location, injection rate, and water alternating gas ratio of the injectors were optimized sequentially using the two algorithms. Next, the production rate of the producers was optimized again. Finally, a simultaneous optimization was done in two manners to evaluate its effect on the optimization results: simultaneous optimization of the last two steps and simultaneous optimization of all decision variables. Results showed the positive effect of the algorithms on the profitability of the project and superiority of the particle swarm optimization over the genetic algorithm at every stage. Also, simultaneous optimization was beneficial at finiding better results compared to sequential optimization approach. In the end, a sensitivity analysis was made to specify the most influencing decision variable on the project’s profitability.

The use of carbon fiber reinforced plastic (CFRP) is increasing in engineering applications such as aerospace, automobiles, defense, and construction. Excellent strength-to-weight ratio, high impact toughness, and corrosion resistance make CFRP highly suitable for aerospace applications. Curing temperature, curing time, and autoclave pressure are among the most important curing parameters affecting the properties of CFRP. Tensile strength, impact toughness, and hardness of CFRP were selected as desirable properties for optimization. A 23 full factorial design of experiment (DOE) was employed by varying curing temperature (120 and 140 °C), curing time (90 and 120 min), and autoclave pressure (3 and 7 bar) while keeping the number of experiments to a minimum level. The cured samples were subjected to tensile strength, impact toughness, and hardness tests at room temperature as per relevant ASTM standards. Analysis of variance (ANOVA) was used, and it was found that tensile strength, impact toughness, and hardness were influenced most significantly by temperature and time. The maximum tensile strength and hardness were achieved for curing cycle parameters of 140 °C, 120 min, and 7 bar, and impact toughness was maximized for 140 °C, 120 min, and 3 bar. A concept of composite desirability function was used to achieve simultaneous optimization of conflicting tensile strength and impact toughness properties for the specific application of aircraft skin. [ABSTRACT FROM AUTHOR]

Iodine value (IV) is an indicator to evaluate the degree of unsaturation (DU) of biodiesel. It reflects the biodiesel degradation and oxidation stability (OS) and also has an effect on viscosity, low-temperature flow properties (LTFP), and the combustion performance. To construct a theoretical system for the simultaneous optimization of LTFP and OS of biodiesel using IV, 52 measured experimental data are used to investigate the qualitative and quantitative relationship between IV and biodiesel composition. The relationships between biodiesel physicochemical properties and IV are investigated in this work. The qualitative analysis shows that the poly-unsaturated fatty acid methyl esters (FAMEs) contribute to an increase in IV, whereas saturated and mono-unsaturated FAMEs decrease IV. Multiple linear regression (MLR) and artificial neural network (ANN) are used to estimate IV from FAMEs. The correlation coefficient, root mean squared error (RMSE), and mean absolute percentage error (MAPE) are respectively 0.976, 2.45, and 1.76% for the MLR model and 0.983, 2.14, and 1.57% for the back propagation neural network (BPNN) model; these values indicate the high accuracy of these methods. The performances of the proposed models were compared with three existing IV prediction models and validated by another databank. The results indicate that the application of the developed BPNN model is better and more comprehensive. Additionally, a preliminary conclusion is that biodiesel with a low percentage of both long-chain saturated and poly-unsaturated FAMEs can have solidifying point (SP) and OS in the proper range. Biodiesel with a low IV is generally more combustible and efficient. [ABSTRACT FROM AUTHOR]

Nickel Titanium (NiTi) alloys are the class of smart materials classified under shape memory alloys. The traditional machining of these alloys is hard because of various inherent mechanical characteristics of these alloys. Therefore, non-traditional machining process such as wire electro discharge machining (WEDM) has been employed for machining of such alloys. The present study is focused on multi-performance characteristic simultaneous optimization of WEDM process parameters, in which three system control factors, namely, pulse on time (TON), pulse off time (TOFF) and wire feed (WF) are considered for simultaneously maximizing material removal rate (MRR), while minimizing surface roughness (SR) and tool wear rate (TWR). The simultaneous optimization is performed using Taguchi’s Quality Loss Function. Analysis of means and analysis of variance have been carried out to identify the significance level of each system control factor. The different levels of settings and the optimized setting have been analysed using scanning electron microscope images for surface morphological studies. The multi-response optimization investigations revealed that TON is the major contributing factor and optimal performance values were obtained at TON of 125μs, TOFF of 25μs and at WF of 4 m/min.

Design optimization of structure and control combined systems are coupled problems. The conventional sequential design strategy (structure design sequentially followed by control design) usually neglects the coupling effects between structure and control and can only produce sub-optimal designs of the complex systems. Nevertheless, simultaneous design strategy with a reasonable and explicit optimization iteration management can ensure global optimization designs of the coupled system. In this study, a surrogate based co-design is explored to achieve the simultaneous optimization, which transforms continuous optimal control problems into discrete nonlinear programming problems. The discrete control parameters combining with the structure parameters can be simultaneously optimized and the co-design strategy can be achieved by introducing necessary and reasonable dynamic constraints. Here, the proposed surrogate based co-design is tested using a practical engineering case study, a heavy-load face-shovel excavator (FSE). The case study is built upon existing FSE design problems by incorporating detailed structure and control design variables while satisfying the system dynamic equations. The proposed surrogate based co-design is implemented and compared with the conventional sequential design strategy and an existing co-design strategy. Results indicate that the surrogate based co-design is accurate and efficient enough to achieve better performances than the conventional sequential design strategy by making FSE's structure more compact with relatively small vibrations.

Two non-linear programming (NLP) models with rigorous stage-by-stage MESH calculations are proposed based on the concept of bypass efficiency. The models can be used to simultaneously optimize the number of stages, feed stage location and operating parameters such as the reflux ratio and column pressure. They can also be used for simultaneous flowsheet optimization with distillation columns and heat integration, as demonstrated in a case study. The first model removes an over-specification where the enthalpy of the outlet stream is calculated using an equation-of-state. A degrees of freedom (DOF) analysis shows that the calculation is not required since the enthalpy of the outlet stream is computed from the inlet stream and equilibrium stream and the outlet temperature does not need to be known for subsequent stage calculations. The second model reduces the number of free variables by grouping the bypass efficiencies in a systematic manner. This modification reduces the model size and solution time without sacrificing accuracy or the number of unique solutions. Other than that, the pressure drop equations were improved. The improvement proved to be significant as the original equations caused all stages to be active in a case study, while the same problem was not observed with the new set of pressure drop constraints. Both proposed models had shorter solution times, smaller model size and they yielded minimized objective values 1.5–5.4% lower compared to the original bypass efficiency model.

This letter presents a rotation-based optimization method for pattern synthesis of subarrayed phased arrays. In order to keep a good beam scanning ability at different steering angles, the optimization problem is reasonably defined where the objective is taken as minimizing the maximum sidelobe level at a series of steering angles, and both the arrangement variables and the scanning variables are optimized simultaneously. The mutual coupling of the iteratively rotated arrays is rigorously considered in the synthesis. In order to solve the synthesis problem effectively, the design sensitivities are derived analytically and the synthesis is actualized by using a gradient-based algorithm. Typical examples, with considering different regular subarray configurations and the state-of-art irregular subarray configurations, are provided to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Hand–eye calibration is one of the key technologies of robot hand and eye coordination operation. The calibration accuracy directly affects the measurement accuracy of hand–eye system. Due to the influence of environmental noise and disturbance, the results of hand–eye calibration usually contain errors. The hand–eye parameters with errors are used for vision measurement, which will lead to the calibration error into the measurement results. This error transfer phenomenon will inevitably affect the accuracy of visual measurement. In this article, a simultaneous optimization method of calibration and measurement is proposed. Based on the analysis of the motion characteristics, the measurement and calibration model with nonlinear constraints is established. An iterative method is proposed along with the closed-form solution to improve the calibration accuracy. Furthermore, the advantages of the proposed method over traditional methods are discussed. Simulation and experiment are carried out to analyze the calibration and measurement accuracy, error propagation, and solution conditions. The results demonstrate that the proposed method realizes the simultaneous optimization of calibration and measurement and can effectively suppress the influence of calibration error on measurement accuracy. [ABSTRACT FROM AUTHOR]

[Display omitted] • A surrogate-based optimization method is proposed for simultaneous synthesis. • An ANN-based surrogate model is established for process unit modeling. • An enhanced MINLP model is introduced to address the variable HEN. • Two examples are studied to illustrate the effectiveness of the proposed method. Heat-integrated process synthesis is fundamental to achieve higher energy efficiency. The well-known sequential-conceptual methods have been widely adopted to solve the synthesis problem in a hierarchical manner. However, the natural hierarchy fails to consider complex interactions between the unit operation and the heat integration. To address this issue, a surrogate-based optimization framework is proposed for simultaneous synthesis of chemical process and heat exchanger network. An artificial neural network (ANN)-based surrogate model, derived from the simulation data generated via rigorous mechanism modelling approach, is established for process units to replace their complex realistic models. With surrogate model formulation incorporated into heat integration, an enhanced transshipment-based mixed integer nonlinear programming model is introduced to synthesize heat exchanger network with variable flowrates and temperatures, aiming at the maximized annual profit. Finally, two example studies are investigated to demonstrate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Sustainable forest management requires tools to guide optimal silvicultural treatments. Timing and intensity of thinnings and timing of final harvest are key decisions. An Optimized Reforestation Management (herein, an ORM) model was developed to reach the optimal combination of thinning times, thinning intensities and rotation periods for Ecuadorian forest plantations of Alnus acuminata, Kunth and Pinus patula Schltdl. & Cham. The ORM model uses non-linear programing to simultaneously optimize the number of trees to harvest in a certain period in order to maximize the plantations' annualized net present values (annuities). Results obtained for optimal silvicultural interventions were compared to current management practices in Ecuador, one with no interventions, and a second with a treatment schedule promoted by the Ecuadorian Ministry of Agriculture. ORM model delivered better financial performance for both species, compared to both alternative scenarios, in terms of annuities and payback periods. Thinnings scheduled by ORM tend to be later than recommended by existing schedules. For Alnus, only one thinning was optimal, independent of the discount rate, while for Pinus two thinnings were optimal for higher levels of the discount rate considered. It is recommended for private and public actors to consider these findings for future management of reforestation areas. [ABSTRACT FROM AUTHOR]

Optimization of the placement and operational conditions of oil wells plays an important role in the development of the oilfields. Several automatic optimization algorithms have been used by different authors in recent years. However, different optimizers give different results depending on the nature of the problem. In the current study, a comparison between the genetic algorithm and particle swarm optimization algorithms was made to optimize the operational conditions of the injection and production wells and also to optimize the location of the injection wells in a southern Iranian oilfield. The current study was carried out with the principal purpose of evaluating and comparing the performance of the two most used optimization algorithms for field development optimization on real-field data. Also, a comparison was made between the results of sequential and simultaneous optimization of the decision variables. Net present value of the project was used as the objective function, and the two algorithms were compared in terms of the profitability incremental added to the project over twelve years. First, the production rate of the producers was optimized, and then water alternating gas injection wells were added to the field at locations determined by engineering judgment. Afterward, the location, injection rate, and water alternating gas ratio of the injectors were optimized sequentially using the two algorithms. Next, the production rate of the producers was optimized again. Finally, a simultaneous optimization was done in two manners to evaluate its effect on the optimization results: simultaneous optimization of the last two steps and simultaneous optimization of all decision variables. Results showed the positive effect of the algorithms on the profitability of the project and superiority of the particle swarm optimization over the genetic algorithm at every stage. Also, simultaneous optimization was beneficial at finiding better results compared to sequential optimization approach. In the end, a sensitivity analysis was made to specify the most influencing decision variable on the project's profitability. [ABSTRACT FROM AUTHOR]

Neural Networks (NN) structure is commonly determined before the training of its weights using a trial-and-error-based approach by an expert of the problem under consideration since there are no clear guidelines for selecting an optimal NN structure. The trial-and-error-based method can be very time-consuming and can also overlook possible optimal combinations due to the large amount of NN structure combinations available. To cope with these issues, the definitions developed by Zou et al. are used to identify the NN structure and weight optimization problem as a hierarchically coupled constrained optimization problem, and an improved version of the algorithm is developed and applied to find the optimal NN structure and weight values simultaneously. The proposed approach is then employed to build NN prediction models (referred to as NN-HCCOP) for a variety of case studies to test its validity. The results of the NN-HCCOP model are compared with five other prediction methods i.e., adaptive neuro-fuzzy inference system (ANFIS), ANFIS-firefly algorithm (ANFIS-FA), classical NN, regression analysis, and gaussian process regression analysis. The comparison results show that NN-HCCOP model can provide higher prediction accuracy in the majority of the testing scenarios compared to models built by other techniques. [ABSTRACT FROM AUTHOR]

Although controller devices highly improve the seismic performance of civil structures, they usually incur enormous financial costs. A variety of sequential methods for minimizing the cost function have been proposed in the literature. In most of these methods, the control system is optimally designed after the best structural configuration is provided. However, such sequential procedures are unable to yield the best overall design. This paper discusses a combined structural and control optimization approach in which the structural mass and controlled system energy are simultaneously minimized. To this end, a twofold objective function is defined by linearly combining the structural mass and linear quadratic regulator (LQR) performance index. It is shown that the optimal value of this objective function depends on the initial condition, and to circumvent this problem, an alternative objective function is introduced which allows for solving the problem of initial condition dependency. Given the complexity of the problem, a variable neighborhood search (VNS) metaheuristic method is developed. The performance of the developed method is demonstrated through case studies involving the determination of structural responses for shear-type frames. The case studies are presented for evaluating and comparing the effectiveness of the proposed simultaneous technique and the conventional sequential method. The responses of the structures are determined under an array of strong ground motions. The results of the comparative analyses revealed that the simultaneously optimized cases using the proposed methodology had superior dynamic performance compared to the sequentially optimized cases. [ABSTRACT FROM AUTHOR]

The optimal and smart design of nonlinear building structures with and without passive dampers subjected to earthquake loading is of great concern in the structural design of building structures. The research started around 1980 and many investigations have been conducted. A comprehensive review on this subject is made in this article. After the description of essential features of the optimal design problem of nonlinear building structures under earthquake ground motions, analysis types of optimization problems are explained and the significance of the dynamic pushover analysis is discussed from the viewpoint of analysis of limit states under earthquake ground motions of magnitude larger than the code-specified level. Then, the categorization by the response of frames and dampers was made. In this categorization, several subjects are discussed first: 1) Optimal design of bare nonlinear building frames under seismic loading, 2) Optimal design of nonlinear dampers for elastic building frames under seismic loading, 3) Optimal design of linear dampers for nonlinear building frames under seismic loading, 4) Optimal design of nonlinear building frames with specified nonlinear dampers under seismic loading, 5) Optimal design of nonlinear dampers for specified nonlinear building frames under seismic loading, 6) Simultaneous optimization of elastic-plastic building structures and passive dampers. Finally, the classification of researches in view of solution strategies is conducted for providing another viewpoint.

The transition from traditional fuel-based bus transportation towards electric bus systems is regarded as a beacon of hope for emission-free public transport. In this study, we focus on battery electric bus systems, in which charging is possible at a variety of locations distributed at terminal stations over the entire bus network. In such systems, two intertwined planning problems to be considered are charging location planning and electric vehicle scheduling. We account for the interdependent nature of both planning problems by adopting a simultaneous optimization perspective. Acknowledging the existence of parameter uncertainty in such complex planning situations, which is rooted in potential changes of values of several environmental factors, we analyze the solution sensitivity to several of these factors in order to derive methodological guidance for decision makers in public transportation organizations. Based on the formulation of a new mathematical model and the application of a variable neighborhood search metaheuristic, we conduct sensitivity analysis by means of numerical experiments drawing on real-world data. The experiments reveal that it is not possible to identify persistent structures for charging locations by an a priori analysis of the problem instances, so that rather a simultaneous optimization is necessary. Furthermore, the experiments show that the configuration of electric bus systems reacts sensitively to parameter changes.

The use of carbon fiber reinforced plastic (CFRP) is increasing in engineering applications such as aerospace, automobiles, defense, and construction. Excellent strength-to-weight ratio, high impact toughness, and corrosion resistance make CFRP highly suitable for aerospace applications. Curing temperature, curing time, and autoclave pressure are among the most important curing parameters affecting the properties of CFRP. Tensile strength, impact toughness, and hardness of CFRP were selected as desirable properties for optimization. A 23 full factorial design of experiment (DOE) was employed by varying curing temperature (120 and 140 °C), curing time (90 and 120 min), and autoclave pressure (3 and 7 bar) while keeping the number of experiments to a minimum level. The cured samples were subjected to tensile strength, impact toughness, and hardness tests at room temperature as per relevant ASTM standards. Analysis of variance (ANOVA) was used, and it was found that tensile strength, impact toughness, and hardness were influenced most significantly by temperature and time. The maximum tensile strength and hardness were achieved for curing cycle parameters of 140 °C, 120 min, and 7 bar, and impact toughness was maximized for 140 °C, 120 min, and 3 bar. A concept of composite desirability function was used to achieve simultaneous optimization of conflicting tensile strength and impact toughness properties for the specific application of aircraft skin.

Multi-objective optimal placement and sizing of Distributed Generation in a distribution system is a challenging endeavor. It is seen that in most cases DG placement is required for multiple criteria, however, placement is done using a single criterion. It is imperative to take into account all criteria and their importance for DG placement. Optimal placement of DG is portrayed as a multi-criteria decision-making (MCDM) problem in this paper. An optimal place is found by applying the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). Voltage stability has been studied in context with DG sizing, however, power stability has not been given due consideration. In this paper, maintaining power stability is one of the objectives for finding optimal DG size. It is observed that the addition of this objective restricts DG size. A robust Taguchi Desirability Function Analysis (TDFA) technique is applied for estimating the optimal size of DG. TDFA can optimize almost a thousand objectives simultaneously. Each objective can be added effortlessly. Moreover, the proposed approach offers a set of near-optimal solutions. IEEE 33-bus radial distribution system and restructured IEEE 118-bus radial distribution system are used for testing. [ABSTRACT FROM AUTHOR]

This paper presents an optimal sitting and sizing model of a lithium-ion battery energy storage system for distribution network employing for the scheduling plan. The main objective is to minimize the total power losses in the distribution network. To minimize the system, a newly developed version of cayote optimization algorithm has been introduced and validated based on different test functions and verified by some well-known meta-heuristics. The proposed algorithm is then applied to four different case studies in the presence of two photo voltaic (PVs) and without them. The optimization of the problems is applied based on two different trends including simultaneous and step-by-step optimization. Final results are compared with some meta-heuristics including cuckoo optimization algorithm, lion optimization algorithm, and cooperative random particle swarm optimization algorithm to show the algorithm higher efficiency. The results also showed that the presence of PVs reduces the total power losses and applying the simultaneous optimization gives higher efficiency with less total power losses. [ABSTRACT FROM AUTHOR]

The adjustment and control of the water injection rate is a commonly used method for increasing the cumulative oil production of waterflooded reservoirs. This article studies the production optimization problem under the condition of a fixed total water injection rate. The production process is divided into several segments. Considering the correlation between the segment's time intervals and the well's injection rate distribution, a simultaneous optimization of both segmented time and injection rate is proposed for enhancing net present value. Both empirical simulations and field application demonstrate that the suggested methods produce the highest increase in net present value – of approximately 13% and 10%, respectively – and significantly improve water flooding efficiency compared to other conventional schemes, such as segmented oil production optimization, cumulative oil production optimization and Bang-Bang control. The proposed methods under a 2-segment division increase oil production efficiency and greatly reduce adjustment costs. [ABSTRACT FROM AUTHOR]

The search for novel non-thermal preservation technologies is continuously growing, as traditional thermal processing may have undesirable effects on sensory and nutritional properties of fruit juices. Therefore, the aims of this study were to find the ultrasound, pomegranate extract and geraniol levels that simultaneously optimize different quality parameters of strawberry juice after 14 days of refrigerated storage (5°C) and evaluate the performance of the optimal combination of these treatmentsto control a contamination with Escherichia coli O157:H7. Response surface methodology was applied for optimization study where geraniol was applied at 0, 0.15 and 0.30 μL/mL; pomegranate extract, at 0, 180 and 360 μg/mL; and ultrasound treatments were performed at 40 kHz during 0, 15, and 30 min. The optimum combination resulted in 0.15 μL/mL of geraniol, 360 μg/mL of pomegranate extract and 30 min of ultrasound. This treatment was able to significantly reduce microbial counts, with a low impact on sensory attributes, while improving the antioxidant capacity of the product compared tountreated juice samples. Also, it allows reducing the contamination with E. coli to undetectable values. Thus, the optimized treatment resulted adequate for extending the shelf-life as well as increasing the safety of strawberry juice. [ABSTRACT FROM AUTHOR]

Increasing the cutter size to enhance its stiffness is of great significance for improving the machining efficiency and precision of deep and narrow channel parts. In this paper, the concept of cutter size critical sphere (CSCS) is presented, which is a specified sphere and tangent to several geometric constraints of tool accessible regions. With this concept, the complex multi-constraints of the workpiece can be transformed into several simple but precise geometric conditions to optimize the size and tool orientation of a conical cutter. The conical cutter generated by CSCS can reach a critical state with multi-constraints simultaneously. Firstly, the mathematical principle of the conical cutter generated by a cylindrical cutter is deduced based on the motion envelope theory. Secondly, the concept of CSCS is proposed based on the deduced mathematical principle and the calculation method of the CSCS with the general situation is provided. Thirdly, the CSCS with special situations is discussed and the calculation method is proposed. Fourthly, the mathematical model and solving algorithm for the max-size CSCS is given. The solving algorithm is designed based on the 3D Medial Axis Transformation (3D MAT) theory. Finally, the proposed method is verified by three simulation experiments and a comparison experiment. The results show that the proposed method can efficiently and accurately calculate the max size and tool orientation of the conical cutter simultaneously. The optimized conical cutter can be tangent to the multi-constraints of the workpiece but do no interfer with them. [ABSTRACT FROM AUTHOR]

This paper presents a non-intrusive framework for integrating existing unsteady partial differential equation (PDE) solvers into a parallel-in-time simultaneous optimization algorithm. The time-parallelization is provided by the non-intrusive software library XBraid [Parallel multigrid in time, software available at ], which applies an iterative multigrid reduction technique to the time domain of existing time-marching schemes for solving unsteady PDEs. Its general user-interface has been extended in [S. Günther, N.R. Gauger, and J.B. Schroder, A non-intrusive parallel-in-time adjoint solver with the XBraid library, Comput. Vis. Sci. 19 (2018), pp. 85–95. Available at arXiv, math.OC/1705.00663] for computing adjoint sensitivities such that gradients of output quantities with respect to design changes can be computed parallel-in-time alongside with the primal PDE solution. In this paper, the primal and adjoint XBraid iterations are embedded into a simultaneous optimization framework, namely the One-shot method. In this method, design updates towards optimality are employed after each state and adjoint update such that optimality and feasibility of the design and the PDE solution are reached simultaneously. The time-parallel optimization method is validated on an advection-dominated flow control problem which shows significant speedup over a classical time-serial optimization algorithm. [ABSTRACT FROM AUTHOR]

We present a combined traffic control and route choice optimization (CTCRCO) system to quickly find new traffic equilibrium solutions in urban road networks with significant changes. The system iteratively executes a control module which combines a traffic control system (TCS) and a traffic simulation manager (TSM) and an assignment module which combines a route set manager (RSM) and a route choice system (RCS), thus can optimize both traffic flows and route choices for minimizing average travel time of vehicles. We evaluate CTCRCO by testing various traffic scenarios in real-world road networks with significant changes. The results show that TCS and RCS are both significant for achieving a quick and low-cost convergence to an approximate equilibrium in a road network with significant changes. Approximate simulations not only accelerate convergence processes for non-congested cases, but also bypass potential gridlock states for over-congestion cases. The role of different path-finding procedures is also examined. [ABSTRACT FROM AUTHOR]