A nondominated ranked genetic algorithm for bi-objective single machine preemptive scheduling in just-in-time environment

Motivated by just-in-time (JIT) manufacturing, we study the bi-objective scheduling problem of minimizing the total weighted earliness and the number of tardy jobs on a single machine, in which machine idle time and preemption are allowed. The problem is known to be NP-hard. In this paper, we propose a new mathematical model, with nonlinear terms and integer variables which cannot be solved efficiently for medium- and large-sized problems. A method combining the new ranked-based roulette wheel selection algorithm with Pareto-based population ranking algorithm, named nondominated ranking genetic algorithm (NRGA), has been presented to find nondominated solutions in a reasonable time. Various operators and parameters of the proposed algorithm are reviewed to calibrate the algorithm by means of the Taguchi method. A number of numerical examples are solved to demonstrate the effectiveness of the proposed approach. The solutions obtained via NRGA are compared against solutions obtained via ε-constraint method in small-sized problems. Experimental results show that the proposed NRGA is competitive in terms of the quality and diversity of solutions in medium- and large-sized problems.     

Order‐reduction of parabolic PDEs with time‐varying domain using empirical eigenfunctions

A novel methodology for the order‐reduction of parabolic partial differential equation (PDE) systems with time‐varying domain is explored. In this method, a mapping functional is obtained, which relates the time‐evolution of the solution of a parabolic PDE with time‐varying domain to a fixed reference domain, while preserving space invariant properties of the initial solution ensemble. Subsequently, the Karhunen–Loève decomposition is applied to the solution ensemble on fixed spatial domain resulting in a set of optimal eigenfunctions. Further, the low dimensional set of empirical eigenfunctions is mapped on the original time‐varying domain by an appropriate mapping, resulting in the basis for the construction of the reduced‐order model of the parabolic PDE system with time‐varying domain. This methodology is used in three representative cases, one‐ and two‐dimensional (1‐D and 2‐D) models of nonlinear reaction‐diffusion systems with analytically defined domain evolutions, and the 2‐D model of the Czochralski crystal growth process with nontrivial geometry. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4142–4150, 2013     

An Efficient Finite Difference Method for The Time‐Delay Optimal Control Problems With Time‐Varying Delay

In this paper, an efficient finite difference method is presented for the solution of time‐delay optimal control problems with time‐varying delay in the state. By using the Pontryagin's maximum principle, the original time‐delay optimal control problem is first transformed into a system of coupled two‐point boundary value problems involving both delay and advance terms. Then the derived system is converted into a system of linear algebraic equations by using a second‐order finite difference formula and a Hermite interpolation polynomial for the first‐order derivatives and delay terms, respectively. The convergence analysis of the proposed approach is provided. The new scheme is also successful for the optimal control of time‐delay systems affected by external persistent disturbances. Numerical examples are included to demonstrate the validity and applicability of the new technique. Some comparative results are included to illustrate the effectiveness of the proposed method.     

Application of genetic algorithm to computer-aided process planning in preliminary and detailed planning

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in the computer integrated manufacturing (CIM) environment. A good process plan of a part is built up based on two elements: (1) optimized sequence of the operations of the part; and (2) optimized selection of the machine, cutting tool and tool access direction (TAD) for each operation. On the other hand, two levels of planning in the process planning is suggested: (1) preliminary and (2) secondary and detailed planning. In this paper for the preliminary stage, the feasible sequences of operations are generated based on the analysis of constraints and using a genetic algorithm (GA). Then in the detailed planning stage, using a genetic algorithm again which prunes the initial feasible sequences, the optimized operations sequence and the optimized selection of the machine, cutting tool, and TAD for each operation are obtained. By applying the proposed GA in two levels of planning, the CAPP system can generate optimal or near-optimal process plans based on a selected criterion. A number of case studies are carried out to demonstrate the feasibility and robustness of the proposed algorithm. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature for most problems. The main contribution of this work is to emerge the preliminary and detailed planning, implementation of compulsive and additive constraints, optimization sequence of the operations of the part, and optimization selection of machine, cutting tool and TAD for each operation using the proposed GA, simultaneously.     

Comparison of antireflection surfaces based on two-dimensional binary gratings and thin-film coatings

A comparison of antireflection surfaces based on the two-dimensional binary gratings and thin-film coatings is presented. First, a two-dimensional hybrid binary grating is proposed and analyzed by use of a vector-based implementation of the rigorous coupled-wave analysis method. The optimum parameters of the structure are determined and the effects that changing them have on spectral characteristics of the structure are studied. Then this structure is compared with multilayer thin-film antireflection filters. These filters are designed by genetic algorithm and needle methods, which are powerful methods for multilayer filter design. The comparison results show that the sensitivity of the grating to changes in the incident wavelength is high. However, a reflectance of the order of 10(-3)% at the design wavelength can be achieved. The sensitivity of designed antireflection thin-film filters to wavelength changes is lower, however, and the minimum achievable reflectance is higher.     

A graph mining approach for detecting unknown malwares

Nowadays malware is one of the serious problems in the modern societies. Although the signature based malicious code detection is the standard technique in all commercial antivirus softwares, it can only achieve detection once the virus has already caused damage and it is registered. Therefore, it fails to detect new malwares (unknown malwares). Since most of malwares have similar behavior, a behavior based method can detect unknown malwares. The behavior of a program can be represented by a set of called API's (application programming interface). Therefore, a classifier can be employed to construct a learning model with a set of programs' API calls. Finally, an intelligent malware detection system is developed to detect unknown malwares automatically. On the other hand, we have an appealing representation model to visualize the executable files structure which is control flow graph (CFG). This model represents another semantic aspect of programs. This paper presents a robust semantic based method to detect unknown malwares based on combination of a visualize model (CFG) and called API's. The main contribution of this paper is extracting CFG from programs and combining it with extracted API calls to have more information about executable files. This new representation model is called API-CFG. In addition, to have fast learning and classification process, the control flow graphs are converted to a set of feature vectors by a nice trick. Our approach is capable of classifying unseen benign and malicious code with high accuracy. The results show a statistically significant improvement over n-grams based detection method.     

Cube-octameric silsesquioxane (POSS)-capped magnetic iron oxide nanoparticles for the efficient removal of methylene blue

Octavinyl polyhedral oligomeric silsesquioxane (POSS) was polymerized on the surface of Fe3O4 nanoparticles (NPs) and then the NPs were functionalized with carboxylic acid groups using thiol-ene click reactions with thioglycolic acid.The as-prepared Fe3O4@POSS-COOH magnetic hybrid NPs had mesoporous structures with an average particle diameter of 15 nm and a relatively high specific surface area of 447 m^2· g^-1.Experimental results showed that 4 mg of Fe3O4@POSS-COOH NPs efficiently adsorbed and removed methylene blue from water at 5 min.This is due to the presence of both carboxylic acid and pendant vinyl groups on the Fe3O4@POSS-COOH NPs.These NPs could be easily withdrawn from water within a few seconds under moderate magnetic field and showed high stability in acid and alkaline aqueous mediums.     

Adaptive robust control of uncertain systems with state and input delay

In this paper, adaptive robust control of uncertain systems with multiple time delays in states and input is considered. It is assumed that the parameter uncertainties are time varying norm-bounded whose bounds are unknown but their functional properties are known. To overcome the effect of input delay on the closed loop system stability, new Lyapunov Krasovskii functional will be introduced. It is shown that the proposed adaptive robust controller guarantees globally uniformly exponentially convergence of all system solutions to a ball with any certain convergence rate. Moreover, if there is no disturbance in the system, asymptotic stability of the closed loop system will be established. The proposed design condition is formulated in terms of linear matrix inequality (LMI) which can be easily solved by LMI Toolbox in Matlab. Finally, an illustrative example is included to show the effectiveness of results developed in this paper.     

Miscibility and tack of blends of poly (vinylpyrrolidone)/acrylic pressure-sensitive adhesive

In this study, miscibility and tack of blends of poly (vinylpyrrolidone) (PVP)/acrylic pressure-sensitive adhesive (PSA) were evaluated. For this purpose, appropriate amounts of PVP (2–30% w/w) were added to an acrylic PSA to obtain visually homogeneous solution. The resulting solution was evenly applied on a polyethylene terephthalate (PET) film with final specific thicknesses of 10, 40, and 70 μm by using a film applicator and miscibility as well as tack values were evaluated. With the addition of 2% (w/w) PVP the tack value decreased and increased in 5% (w/w) PVP and then continuously decreased up to 30%(w/w). It was found that the tack value was related to miscibility as well as to viscosity and the free functional group such as hydroxyl group of the blend. By the morphological analysis performed through scanning electron microscopy (SEM) and also by the study of thermal analysis using the differential scanning calorimetry (DSC) behavior of blends, it was found that the two distinct phases constituted after adding 5% (w/w) of PVP. This resulted in the acrylic PSA forming the continuous phase, and by increasing the concentration of PVP a dispersed phase was developed. The dispersed phase has a higher viscosity than the continuous phase and therefore cannot wet the adherent and hence result in lowering the tack values.