Search

This work presents the Topology Optimisation of the Morphing Variable Span of Tapered Wing (MVSTW) using a finite element method. This topology optimisation aims to assess the feasibility of internal wing components such as ribs, spars and other structural components. This innovative approach is proposed for the telescopic mechanism of the MVSTW, which includes the sliding of the telescopically extended wing into the fixed wing segment. The optimisation is performed using the tools within ANSYS Mechanical, which allows the solving of topology optimisation problems. This study aims to minimise overall structural compliance and maximise stiffness to enhance structural performance, and thus to meet the structural integrity requirements of the MVSTW. The study evaluates the maximum displacements, stress and strain parameters of the optimised variable span morphing wing in comparison with those of the original wing. The optimised wing analyses are conducted on four wingspan extensions, that is, 0%, 25%, 50% and 75%, of the original wingspan, and for different flight speeds to include all flight phases (17, 34, 51 and 68m/s, respectively). Topology optimisation is carried out on the solid wing built with aluminium alloy 2024-T3 to distribute the wing components within the fixed and moving segments. The results show that the fixed and moving wing segments must be designed with two spar configurations, and seven ribs with their support elements in the high-strain area. The fixed and moving wing segments’ structural weight values were reduced to 16.3 and 10.3kg from 112 to 45kg, respectively. The optimised MVSTW was tested using different mechanical parameters such as strains, displacements and von Misses stresses. The results obtained from the optimised variable span morphing wing show the optimal mechanical behaviour and the structural wing integrity needed to achieve the multi-flight missions.

Disclosure—the sharing of personal information—has long been appreciated as a key element of strong, close relationships. But its exploration in educational contexts has been limited. In the present study, we examine whether disclosure in mentoring relationships between a college student and a school-based “adult” is associated with benefits, controlling for the student’s achievement and assessments of their potential. Using data from a selective university that includes both mentor and mentee reports (N=143 pairs), we find that mentoring relationships characterized by greater personal disclosure provide mentees with more resources and continue to a greater extent after mentees graduate.

Metabolic syndrome is a collection of disorders related to metabolisms such as obesity, lipid disorders, hyper/hypoglycemia, ... Metabolic syndrome can lead to cardiovascular diseases, strokes, and diabetes - the leading death causes in the world. In many cases, metabolic disorders are original by the redundant/ reduction of insulin- the most important hormone in metabolism regulation. Both of them are involved in beta-cells dysfunction. Many mechanisms related to this phenomenon has been approved, notably mitochondrial dysfunction and the Ubiquitin proteasome system impairment. UCH-L1 is a protein belonging to the Ubiquitin proteasome system and highly expressed in beta cells. Previous studies reported that decrease UCH-L1 function can alter metabolism and lead to b cell apoptosis under various nutritional conditions, however, the mechanism has not been clarified. In this study, we proposed a Drosophila melanogaster model that expresses many symptoms of metabolic syndrome, by knocking down dUCH (Drosophila homolog of UCH-L1) specifically in Insulin-producing cells. Our fruit fly model had abnormal metabolism, physiology, loss of insulinproducing cells, and mitochondria over-workload, similar to metabolic syndrome in humans. These results suggested that this model is suitable for further studies on the role of UCH-L1 in b cells, as well as a potential model in metabolism diseases' drug screening.

Studies have confirmed the benefits of using Third-party logistics (TPL) for efficient construction management, especially in large projects. Nevertheless, there is a dearth of decision-making mode...

This work presents a comparative study of design and development, in addition, of analyses of variable span morphing of the tapered wing (VSMTW) for the unmanned aerial vehicle (UAV). The proposed concept consists in the sliding of the inner section into the fixed part along the wing with varying the angle of the inner section inside the fixed part (parallel with the leading edge and the moving-wing axis is coincident to the fixed-wing axis) within two configurations. The wing design is based on a NACA 4412 aerofoil with the root chord of 0.675m and the tip chord of 0.367m for the fixed segment and 0.320m for the moving segment. Morphing wing analysis occurs at three selected locations that have been specified for extending and modifying span length by (25%, 50%, and 75%) of its original length to fulfill various flight mission requirements. The main objective of this paper is to compare the aerodynamic characteristics for several span lengths and sweep angles and to find their most efficient combinations. The wing is optimised for different velocities during all phases of flight (min speed, loiter, cruise, and max speed) which are 17, 34, 51, and 68m/s, respectively. The analyses are performed by computing forces (drag and lift) and moments at various altitudes, such as at the sea level, at 5,000 and 10,000ft. Two-dimensional aerodynamic analyses are carried out using XFLR5 code, and the ANSYS Fluent solver is used for investigating the flow field on the three-dimensional wing structure. It has been observed that a variable span morphing of tapered wing technology with a variable sweep angle can deliver up to 32.93% improved aerodynamic efficiency. This concept design can also be used for the aircraft roll motion technique instead of conventional control devices. Furthermore, the range flight mission increases up to 46.89% when the wing is placed at its full length compared to an original position. Finally, it has been concluded from this study that the wing design is more sensitive to the changing angle of the inner section and more efficient in terms of aerodynamic characteristics.

In this study, we propose an integrated model for collaborative Construction Supply Chain (CSC) planning that deals with the joint project scheduling and material ordering decisions. The main objective is to achieve more coordination and, therefore, to reduce the total CSC cost. More specifically, we consider a two-echelon Supply Chain (SC) composed of a manufacturer, a warehouse, and multiple construction sites where multiple independent construction projects are planned. The projects require different materials that are provided by the same manufacturer with a limited production capacity. The starting time of each activity is subject to materials availability in construction sites. A mixed-integer linear programming (MILP) model is developed to reduce the total costs while collaboration between contractors is possible. The model is implemented using the IBM ILOG® CPLEX® Optimization Studio and used to analyze the collaboration process through a numerical study to demonstrate the benefits of collaborative planning in construction project management. The decision model help also in finding practical construction projects’ sequences as well as suitable materials ordering, manufacturing, and inventories plans for SC participants.

PurposeThis paper aims to propose an innovative building information modelling (BIM)-based framework for multi-objective and dynamic temporary construction site layout design (SLD), which uses a hybrid approach of systematic layout planning (SLP) and mathematical modelling.Design/methodology/approachThe hybrid approach, which follows a step-by-step process for site layout planning, is designed to facilitate both qualitative and quantitative data collection and processing. BIM platform is usedto facilitate the determination of the required quantitative data, while the qualitative data are generated through knowledge-based rules.FindingsThe multi-objective layout model represents two important aspects: layout cost and adjacency score. The result shows that the model meets construction managers’ requirements in not only saving cost but also assuring the preferences of temporary facility relationships. This implies that the integration of SLP and mathematical layout modelling is an appropriate approach to deliver practical multi-objective SLD solutions.Research limitations/implicationsThe proposed framework is expected to serve as a solution, for practical application, which takes the advantage of technologies in data collection and processing. Besides, this paper demonstrates, by using numerical experimentation and applying Microsoft Excel Solver for site layout optimisation, how to reduce the complexity in mathematical programming for construction managers.Originality/valueThe original contribution of this paper is the attempt of developing a framework in which all data used for the site layout modelling are collected and processed using a systematic approach, instead of being predetermined, as in many previous studies.

A cellular manufacturing system is a structured system based on the group concept. One of the advantages of this concept is being able to reduce the production time. The optimization problem of the cellular manufacturing systems is classified as NP-hard where the calculation time increases exponentially depending on the size of the problem. The use of meta-heuristic algorithms will be a great solution to solve the NP-hard problem in reasonable time. In this work, we proposed a new hybrid approach to optimize the sequence of parts in each cell as well as exceptional elements. This hybrid methodology based on an RC-Filter algorithm and Extended Great Deluge algorithm is proposed to optimize the sequence of parts including exceptional elements in each cell in order to minimize the makespan. The proposed approach was validated using 13 problems and the results were compared with that given by other algorithms.

This paper presents a numerical investigation of the effects of chamfered and sharp cemented carbide tools using finite element method-based DEFORM-2D software and cutting parameters on different machining characteristics during the orthogonal cutting of AISI 1045 steel. The objective is to study the interactions between chamfer width, chamfer angle, sharp angle and the cutting speed and feed rate on the cutting temperature, effective stress and wear depth. These effects were investigated statistically using the analysis of variance (ANOVA) test. The obtained numerical results showed that for the chamfer tool, high values of temperature, stress and wear depth were obtained for chamfer widths of 0.35 mm and 0.45 mm. In terms of combined influences, for the cutting temperature and stress, a strong interaction between the cutting speed and chamfer width was obtained. For the sharp tool design, and in terms of temperature, strong interactions are mostly observed between cutting speeds and feed rates. The ANOVA showed that for both chamfer and sharp tools, the feed rate, the cutting speed and their interactions are the most significant parameters that influence temperature and stress.

Many combinatorial optimization problems are hard to solve within the polynomial computational time or NP-hard problems. Therefore, developing new optimization techniques or improving existing ones still grab attention. This paper presents an improved variant of the Ant Colony Optimization meta-heuristic called Enhanced Hyper Cube Framework ACO (EHCFACO). This variant has an enhanced exploitation feature that works through two added local search movements of insertion and bit flip. In order to examine the performance of the improved meta-heuristic, a well-known structural optimization problem of laminate Stacking Sequence Design (SSD) for maximizing critical buckling load has been used. Furthermore, five different ACO variants were concisely presented and implemented to solve the same optimization problem. The performance assessment results reveal that EHCFACO outperforms the other ACO variants and produces a cost-effective solution with considerable quality.

This article proposes the integration of structural sizing, topology, and aerodynamic optimization for a morphing variable span of tapered wing (MVSTW) with the aim to minimize its weight. In order to evaluate the feasibility of the morphing wing optimization, this work creates a numerical environment by incorporating simultaneous structural sizing and topology optimization based on its aerodynamic analysis. This novel approach is proposed for an MVSTW. A problem-specific optimization approach to determine the minimum weight structure of the wing components for its fixed and moving segments is firstly presented. The optimization was performed using the OptiStruct solver inside HyperMesh. This investigation seeks to minimize total structure compliance while maximizing stiffness in order to satisfy the structural integrity requirements of the MVSTW. The aerodynamic load distribution along the wingspan at full wingspan extension and maximum speed were considered in the optimization processes. The wing components were optimized for size and topology, and all of them were built from aluminum alloy 2024-T3. The optimization results show that weight savings of up to 51.2% and 55.7% were obtained for fixed and moving wing segments, respectively. Based on these results, the optimized variable-span morphing wing can perform certain flight missions perfectly without experiencing any mechanical failures.

This paper presents the development of a numerical model for predicting and studying the effects of tool nose geometries and its interactions with cutting parameters during orthogonal cutting of AISI 1045 steel. The process performance characteristics studied were cutting temperature, effective stress, cutting forces and tool wear. The cutting simulations were done using the commercial DEFORM-2D R V 11.3 software, based on the finite element method (FEM). The cutting tool used had a round nose with various nose radii (0.01–0.9 mm), while the machining parameters tested were the feed rate (0.1–0.3 mm/rev), the cutting speed (100–500 m/min) and the rake angle (–5° to +10°). The interactions between the tool nose radius and the cutting parameters (speed, feed) were found to affect mostly the cutting stress and, slightly, the tool wear rate. These interactions did not much influence the cutting temperature, that was found to be high when the tool nose radius and/or the cutting speed were high. The maximum temperature was found to occur at the middle of the tool-chip contact length and at the interaction of nose radius and flank face of the tool. Except for some fluctuations, there was no significant difference in tool wear rate between small and large nose radius scales.

This paper utilizes a systematic literature review methodology to identify the present focuses and discuss the future directions of decision-making in construction supply chain management (CSCM). T...

Studies have confirmed the benefits of using Third-party logistics (TPL) for efficient construction management, especially in large projects. Nevertheless, there is a dearth of decision-making models evaluating the exact role of TPL providers as drivers for supply chain (SC) integration and optimisation. This study aims to develop a decision-making model for construction supply chain (CSC) optimisation, with possible TPL integration. The proposed model considers two types of purchased materials (type-1 and type-2) and assists the main contractor in determining construction supply chain management (CSCM) strategies, including supplier selection, order quantity determination, and TPL use evaluation. Using the model, the main contractor can take advantage of the TPL provider’s warehouse and order larger quantities, if necessary, to obtain lower prices offered by suppliers. Through case examples in Canada, we find that the proposed model performs better in optimising total SC cost, as compared to the model without TPL. Model validations also show that TPL can be conditionally used to improve construction logistics performance and to meet practical requirements targeting issues in the construction industry.

The popularity of electronic devices has sparked research to implement components that can achieve better performance and scalability. However, companies face significant challenges when they use systems with a long-life cycle, such as in avionics, which leads to obsolescence problems. Obsolescence can be driven by many factors, primary among which could be the rapid development of technologies that lead to a short life cycle of parts. Moreover, obsolescence problems can prove costly in terms of intermittent stock availability and unmet demand. Therefore, obsolescence forecasting appears to be one of the most efficient solutions. This paper presents a review of gaps in the actual approaches and proposes a method that can better forecast the product life cycle. The proposed approach will help companies to improve obsolescence forecasting and reduce its impact in the supply chain. The method introduces a stochastic approach to estimate the obsolescence life cycle through simulation of demand data using Markov chain and homogeneous compound Poisson process. This approach uses multiple states of the life cycle curve based on the change in demand rate and introduces hidden Markov theory to estimate the model parameters. Numerical results are provided to validate the proposed method. To examine the accuracy of this approach, the standard deviation (STD) of obsolescence time is calculated. The results showed that the life cycle curves of parts can be predicted with high accuracy.

This paper presents the performance comparison of five meta-heuristic algorithms to solve a discrete optimization problem. The comparison is undertaken for a case of simply supported plate subjected to biaxial loading conditions. Furthermore, the optimization objective is to determine the optimal stacking sequence design of a laminate that maximizes the critical buckling load factor (λcb). The chosen meta-heuristics have been implemented using MATLAB with the same convergence criteria and the same maximum number of iterations to ensure a fair comparison. The implemented assessment criterion has performance measures of average CPU time, solution price, reliability, and normalized price. The results have demonstrated the outperformance of the Ant Colony Optimization Algorithm (ACOA) over other algorithms, which confirms the findings of previous studies. Moreover, the Tabu search algorithm (TS) and the Discrete Particle Swarm Optimization algorithm (DPSO) performed poorly due to their limited exploration capability. Additionally, the Genetic Algorithm (GA) and the Simulated Annealing algorithm (SA) exhibited a high level of reliability but showed an expensive solution cost. This study presents an adequate comparison approach of meta-heuristics, where it extends the comparison scope to cover the performance analysis of meta-heuristics more than that previously done in the domain of stacking sequence design optimization.

Neural Machine Translation recently has become the state-of-the-art approach in Machine Translation. One of the more advanced techniques concerning this approach, the attention model, tends to not use alignments from past translation steps and selects the context word purely using the devised attention score. Unfortunately, this sometimes leads to repetition and omission of important words in translations. To solve this problem, we propose a simple approach using coverage techniques that can be used in conjunction with a diverse number of attention models. Our experiments show that our improved technique increases the quality of translation on both English -Vietnamese and Japanese - Vietnamese language pairings.

This paper aims at presenting one novel quantitative strategy of optimizing the design of Lean supply chain using Meta-Heuristics. While classifying Lean Manufacturing tools in two categories, namely Functional and Tier Lean tools, we propose a new framework to design the Lean supply chain by implementing the former into a 5-echelon Fat supply chain. As the following step, we investigate the effect of the latter on the mentioned Lean supply chain model. Then, we utilize the tight correlation of Tier Lean tools and priority-based Genetic Algorithm Meta-Heuristics in order to optimize the configuration of the Lean supply chain. Finally, these ideas are illustrated step by step in one numeral example.

A morphing wing model is used to improve aircraft performance. To obtain the desired airfoils, electrical actuators are used, which are installed inside of the wing to morph its upper surface in order to obtain its desired shape. In order to achieve this objective, a robust position controller is needed. In this research, a design and test validation of a controller based on neural networks is presented. This controller was composed by a position controller and a current controller to manage the current consumed by the electrical actuators to obtain its desired displacement. The model was tested and validated using simulation and experimental tests. The results obtained with the proposed controller were compared to the results given by the PID controller. Wind tunnel tests were conducted in the Price-Paidoussis Wind Tunnel at the LARCASE laboratory in order to calculate the pressure coefficient distribution on an ATR-42 morphing wing model for different flow conditions. The pressure coefficients obtained experimentally were compared with their numerical values given by XFoil software.

The fast determination of aerodynamic parameters such as pressure distributions, lift, drag and moment coefficients from the known airflow conditions (angles of attack, Mach and Reynolds numbers) in real time is still not easily achievable by numerical analysis methods in aerodynamics and aeroelasticity. A flight parameters control system is proposed to solve this problem. This control system is based on new optimisation methodologies using Neural Networks (NNs) and Extended Great Deluge (EGD) algorithms. Validation of these new methodologies is realised by experimental tests using a wing model installed in a wind tunnel and three different transducer systems (a FlowKinetics transducer, an AEROLAB PTA transducer and multitube manometer tubes) to determine the pressure distribution. For lift, drag and moment coefficients, the results of our approach are compared to the XFoil aerodynamics software and the experimental results for different angles of attack and Mach numbers. The main purpose of this new proposed control system is to improve, in this paper, wing aerodynamic performance, and in future to apply it to improve aircraft aerodynamic performance.

The asymmetrical three-roll bending process is commonly used to manufacture cylindrical and conical sections from flat plates. The roll bending forces, the residual stresses, and the required roll bending power are influenced by material properties and process parameters, such as yield stress, plate thickness, curvature, and conicity for a truncated cone. In the present paper, an analytical model is developed to predict roll bending force, residual stresses, and power of roll bending process. To verify the developed model, asymmetrical three-roll bending experiments are performed. The results given by analytical model are in agreement with the experimental results. The analytical model has shown that an increase of the yield stress of material, or plate thickness or conicity requires more roll bending forces, roll bending power during roll bending process and results in residual stresses in the final shape. On other hand, an increase of the radius of bent shape requires less roll bending forces, less roll bending power, and results in less residual stresses in the final shape. Geometric verification of bent shapes has shown that when the yield stress, the thickness, or the conicity of the bent shape increases, the bent shape has less geometric defects. On the contrary, it was found that when the radius of curvature increases, the final shape has more geometric defects.

Driven by the frequent technological changes and innovation, obsolescence has become a major challenge that cannot be ignored in which the life cycle of the components is often shorter than that of their systems. Basically, obsolescence problems are often sudden and not planned which causes delays and extra costs. On the other side forecasting appears to be one of the most efficient solutions to solve this problem. This paper aims to provide new light and help industries to generate different solutions to the problems of obsolescence. Specifically it presents a framework for forecasting the obsolescence based on random forest (RF) algorithm which has proven as the best predictor for forecasting obsolescence risk based on a previous comparative study with a high degree of accuracy.

The main objective of this paper is to optimize series-parallel system reliability using Genetic Algorithm (GA) and statistical analysis; considering system reliability constraints which involve the redundant numbers of selected components, total cost, and total weight. To perform this work, firstly the mathematical model which maximizes system reliability subject to maximum system cost and maximum system weight constraints is presented; secondly, a statistical analysis is used to optimize GA parameters, and thirdly GA is used to optimize series-parallel systems reliability. The objective is to determine the strategy choosing the redundancy level for each subsystem to maximize the overall system reliability subject to total cost and total weight constraints. Finally, the series-parallel system case study reliability optimization results are showed, and comparisons with the other previous results are presented to demonstrate the performance of our GA., {"references":["Boyabatli, O., & Sabuncuoglu, I. (2004). Parameter selection in genetic algorithms. Journal of Systemics, Cybernetics and Informatics, 4(2), 78.","Castillo-Valdivieso, P. A., Merelo, J. J., Prieto, A., Rojas, I., & Romero, G. (2002). Statistical analysis of the parameters of a neuro-genetic algorithm. IEEE Transactions on Neural Networks, 13(6), 1374-1394.","Chambari, A., Rahmati, S. H. A., & Najafi, A. A. (2012). A bi-objective model to optimize reliability and cost of system with a choice of redundancy strategies. Computers & Industrial Engineering, 63(1), 109-119.","Chern, M. S. (1992). On the computational complexity of reliability redundancy allocation in a series system. Operations research letters, 11(5), 309-315.","Coit, D. W., & Smith, A. E. (1996a). Reliability optimization of series-parallel systems using a genetic algorithm. Reliability, IEEE Transactions on, 45(2), 254-260.","Coit, D. W., & Smith, A. E. (1996b). Penalty guided genetic search for reliability design optimization. Computers & industrial engineering, 30(4), 895-904.","François, O., & Lavergne, C. (2001). Design of evolutionary algorithms-A statistical perspective. IEEE Transactions on Evolutionary Computation, 5(2), 129-148.","Khorshidi, H. A., & Nikfalazar, S. (2015). Comparing Two Meta-Heuristic Approaches for Solving Complex System Reliability Optimization. Applied and Computational Mathematics, 4(2-1), 1-6.","Kuo, W., & Prasad, V. R. (2000). An annotated overview of system-reliability optimization. Reliability, IEEE Transactions on, 49(2),176-187.\n[10]\tKuri-Morales, A. F., & Gutiérrez-García, J. (2002, April). Penalty function methods for constrained optimization with genetic algorithms: A statistical analysis. In Mexican International Conference on Artificial Intelligence (pp. 108-117). Springer Berlin Heidelberg.\n[11]\tLiang, Y. C., & Chen, Y. C. (2007). Redundancy allocation of series- parallel systems using a variable neighborhood search algorithm. Reliability Engineering & System Safety, 92(3), 323-331.\n[12]\tMills, K. L., Filliben, J. J., & Haines, A. L. (2015). Determining relative importance and effective settings for genetic algorithm control parameters. Evolutionary computation, 23(2), 309-342.\n[13]\tPetrovski, A., Brownlee, A. E. I., & McCall, J. (2005). Statistical optimisation and tuning of GA factors. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005), Volume 1. IEEE.\n[14]\tSharifi, M., & Yaghoubizadeh, M. (2015). Reliability Modelling of the Redundancy Allocation Problem in the Series-parallel Systems and Determining the System Optimal Parameters. Journal of Optimization in Industrial Engineering, 8(17), 67-77.\n[15]\tSmith, A. E., & Coit, D. W. (1997). Penalty functions. Handbook on Evolutionary Computation, pages C, 5, 1-6.\n[16]\tTillman, F. A., Hwang, C. L., & Kuo, W. (1977). Optimization Techniques for System Reliability with Redundancy A Review. Reliability, IEEE Transactions on, 26(3), 148-155.\n[17]\tYeniay, Ö. (2005). Penalty function methods for constrained optimization with genetic algorithms. Mathematical and Computational Applications, 10(1), 45-56.\n[18]\tZhao, J. H., Liu, Z., & Dao, M. T. (2007). Reliability optimization using multi-objective ant colony system approaches. Reliability Engineering & System Safety, 92(1), 109-120."]}

Because it is influenced by various processing parameters, predicting the bending force and improving the accuracy of the final shape are significant challenges in the roll bending process when the part to be produced is large and made of high-strength steel. In this paper, a 3D dynamic finite element (FE) model of an asymmetrical roll bending process is developed using the ANSYS/LS-DYNA software. This model investigates the parameters that affect the accuracy of the final shape, the bending forces, and the residual strains left in the formed plate. The simulation results are then compared with experiments performed on an instrumented roll bending machine. Strain measurements are also performed during forming with strain gauges fixed onto plate blanks. A good agreement between the experiments and simulations has been obtained.

A new algorithm is presented in order to search for the optimal solution of the manufacturing and fractional cell formation problem. In addition, this paper introduces a new toolkit, which is used to search for the various candidate solutions in a periodic and a waving (diversified) manner. The toolkit consists of 15 tools that play a major role in speeding up the obtainment of the final solution as well as in increasing its efficiency. The application of the binary digit grouping algorithm leads to the creation of manufacturing cells according to the concept of group technology. The nonzero entries, which remain outside the manufacturing cells, are called exceptional elements. When a lot of such elements is obtained, an additional cell called fractional (or remainder) cell may be formed; the aim of which is to reduce their number. This algorithm was tested by using illustrative examples taken from the literature and succeeded to give better or at least similar results when compared to those of other well-known algorithms.

One of the hardest tasks involving wind tunnel characterization is to determine the air-flow condition inside the test section. The Log-Tchebycheff method and the Equal Area method allow calculation of local velocities from measured differential pressures on rectangular and circular ducts. However, these two standard methods for air flow measurement are limited by the number of accurate pressure readings by the Pitot tube. In this paper, a new approach is presented for wind tunnel calibrations. This approach is based on a limited number of dynamic pressure measurements and a predictive technique using Neural Network (NN). To optimize the NN, the extended great deluge (EGD) algorithm is used. Wind tunnel testing involves a large number of variables such as wind direction, velocity, rate flow, turbulence characteristics, temperature variation and pressure distribution on airfoils. NN has the advantage that multilayer perceptron neural networks can describe a 3D flow area with a small amount of experimental data, fewer numbers of iterations and less computation time per iteration. The Fluent results are used to train and optimize the proposed NN approach. The validation of this new approach is achieved by experimental tests using the wind tunnel Price-Paidoussis of LARCASE laboratory. This wind tunnel has two test chambers. The main chamber with a section Height, Width and Length equal to 0.62 x 0.91 x 1.83 meter respectively that provides a speed ranging from 0 to 30 m/s and a second chamber test with a section Height, Width and Length equal to 0.31 x 0.61 x 1.22 meters respectively that provides a speed from 0 to 60 m/s.

This paper aims at presenting the new 2-stage framework of Robust Optimization for Lean Supply Chain design under uncertainty by using the so-called Dual Lean Filter. First, we formulate one quantitative model of Fat Supply Chain in stable circumstance based on the six-performance drivers of Chopra and Meindl, (2013). Then, we propose one novel procedure called Forward Lean Filter in order to transform Fat Supply Chain into Lean Supply Chain. Afterwards, in second phase, we investigate the Lean Supply Chain model under disruptive risk, in which Reverse Lean Filter is introduced to prevent the Lean system from returning Fat form under threats. Both stages are optimized by one meta-heuristics namely priority-based Genetic Algorithm. All aforementioned processes are illustrated in one numerical supply chain under the risk of disruption its key distribution center.

Article history: Received 1 May 2010 Received in revised form 6 June 2010 Accepted 7 June 2010 Available online 11 June 2010 This paper examines the optimization of production involving a tandem two-machine system producing a single part type, with each machine being subject to random breakdowns and repairs. An analytical model is formulated with a view to solving an optimal stochastic production problem of the system with machines having up-downtime non-exponential distributions. The model developed is obtained by using a dynamic programming approach and a semi-Markov process. The control problem aims to find the production rates needed by the machines to meet the demand rate, through a minimization of the inventory/shortage cost. Using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-JacobiBellman equation, which depends on time and system states, and ultimately, leads to a feedback control. Consequently, the new model enables us to improve the coefficient of variation (CVup/down) to be less than one while it is equal to one in Markov model. Heuristics methods are used to involve the problem because of the difficulty of the analytical model using several states, and to show what control law should be used in each system state (i.e., including Kanban, feedback and CONWIP control). Numerical methods are used to solve the optimality conditions and to show how a machine should produce. © 2010 Growing Science Ltd. All rights reserved.

The use of neural networks in the design of cellular manufacturing system is not new. This paper presents an application of modified Hopfield neural networks in order to solve cell formation problems: the quantized and fluctuated Hopfield neural networks (QFHN). This kind of Hopfield network combined with the “tabu search” approach were primarily used in a hybrid procedure in order to solve the cell formation for big sizes industrial data set. The problem is formulated as a 0/1 linear and integer programming model in order to minimize the dissimilarities between machines and/or parts. Our hybrid approach allows us to obtain optimal or nearly optimal solutions very frequently and much more quickly than traditional Hopfield networks. It is also illustrated that the fluctuation associated with this quantization may enable the network to escape from local minima, to converge to global minima, and consequently to obtain optimal solutions very frequently and much more quickly than pure quantized Hopfield networks (QHN). The effectiveness of the proposed approach is flexibility it gives us, for example, in time problem-solving for large-scale and speed of execution when we apply it.

A hybrid Neural Networks have been developed and the simulation techniques have been used to solve complex group scheduling problems. The objectives are to find the best performance criterion optimum for a given number of jobs and machines in order to satisfy different production constraints. The solution methods proposed in this paper, have their roots in both operational research and artificial intelligence. The use of combining Hopfield Neural Networks (HNN) and Tabu (local search) approach to define optimal groups of operations which will facilitate the generation of the route sheet has been discussed and presented in this paper. For each one of the operations, the (HNN) will be used to generate the feasible manufacturing alternatives, and the Tabu search technique will be applied to identify the best schedule. The proposed approach, Hybrid Hopfield Neural Networks (HHNN) and the simulation techniques, save time and minimise unbalanced workloads among the operations. The potential ability of the proposed approach is shown using a numerical case example of a big size of group scheduling problem.

This article proposes a new approach to optimize a milk-run delivery in supply chain (SC) which was introduced by Zhou and Kelin (2011). These authors particularly formulated the mathematical model of SC total cost at operational level for milk-run delivery and used Ant Colony Optimization (ACO) to find out the results in one specific case study. Yet, the solution found by this Meta- Heuristics did not obtain optimal values. Hence, to improve the solution, the paper presents one new method namely Hybrid Ant Colony Optimization and Tabu Search (HAT). To qualify new method, Tabu Search (TS) is taken into account in testing stage. With the same data in the case study and random data, the new method outperforms the original and TS regarding SC total cost and milk-run distance.

The main objective of the paper is to propose a mathematical method, based on New Insertion technique and meta-heuristics to solve forest transportation routing problem. To perform this work, firstly a mathematical model is proposed; secondly a New Insertion algorithm is used to build an initial solution and thirdly the extended great deluge and reactive tabu search are used to improve this solution. The objective is to minimize the total cost by respecting the time window of all customers, which is sometimes important in this field. Finally, the experimental results obtained with the extended great deluge for the named vehicle routing problem are showed, discussed and compared to its reactive tabu search results obtained using the same initial solution. The reactive tabu search is quicker than the extended great deluge; but instead of only one parameter to control in the extended great deluge, we have to control six parameters in reactive tabu search.

The determination of aerodynamic coefficients such as pressure distributions and aerodynamic coefficients (lift, drag and moment) from the known parameters (angle of attack, Mach number …) in real time is still not achievable easily by methods of numerical analysis in aerodynamics and aeroelasticity domains. For this reason, we propose a flight parameters control system. This approach is based on new optimization methodologies with neural networks (NN) and extended great deluge (EGD). The validation of this new method is realized by experimental tests using a model installed in the wind tunnel to determine the pressure distribution. For lift, drag and moment coefficient, the results of our approach are compared to the XFoil results for different angles of attack and Mach number. The main purpose of this control system is to improve the aircraft aerodynamic performance.Copyright © 2014 by ASME