Scheduling open shops with parallel machines to minimize total completion time

This paper explores scheduling a realistic variant of open shops with parallel machines per working stage. Since real production floors seldom employ a single machine for each operation, the regular open shop problem is very often in practice extended with a set of parallel machines at each stage. The purpose of duplicating machines in parallel is to either eliminate or to reduce the impact of bottleneck stages on the overall shop efficiency. The objective is to find the sequence which minimizes total completion times of jobs. We first formulate the problem as an effective mixed integer linear programming model, and then we employ memetic algorithms to solve the problem. We employ Taguchi method to evaluate the effects of different operators and parameters on the performance of memetic algorithm. To further enhance the memetic algorithm, we hybridize it with a simple form of simulated annealing as its local search engine. To assess the performance of the model and algorithms, we establish two computational experiments. The first one is small-sized instances by which the model and general performance of the algorithms are evaluated. The second one consists of large-sized instances by which we further evaluate the algorithms.     

Coacervative extraction of phthalates from water and their determination by high performance liquid chromatography

A simple, rapid and low cost method for determination of phthalic acid esters (PAEs) including Dimethyl phthalate (DMP), Diethyl phthalate (DEP), Di-n-butyl phthalate (DBP) and Butylbenzyl phthalate (BBP) in water samples was investigated. The method is based on the extraction of PAEs with coacervate made up of decanoic acid reverse micelles and the subsequent determination by HPLC-UV. Effect of parameters such as concentration of tetrahydrofuran (THF) (2?C40% v/v) and decanoic acid (20?C400 mg in 40 ml total volume), ionic strength (0.0?C0.1 M NaCl), pH (1?C4) and stirring time (2?C60 min) on recoveries (Rs) and enrichment factors (EFs) were investigated and optimized. The optimum condition for extraction was the stirring of 36 ml of water sample with 4 ml of THF containing 100 mg of decanoic acid for 10 min and its centrifugation (10 min, 3500 rpm). Recoveries and enrichment factors of PAEs mainly depended on the amount of decanoic acid and THF making up the coacervate and were not affected by ionic strength of the sample solution (up to 0.1 M of NaCl), pH (1?C4), and stirring time (2?C60 min). Recoveries, enrichment factors, LODs and relative standard deviations (RSD%) for PAEs were between 87?C94%, 187?C202, 0.22?C0.30 ??g l^?1 and 2?C5%, respectively. This method was applied to determine PAEs in tap water, river water, and sea water samples. No PAEs were found in tap water. The amount of DMP and DEP in the Babolrood River was 0.87 and 0.67 ??g l^?1, while in the Caspian Sea was 0.49 and 0.52 ??g l^?1, respectively.     

A filter trust-region algorithm for unconstrained optimization with strong global convergence properties

We present a new filter trust-region approach for solving unconstrained nonlinear optimization problems making use of the filter technique introduced by Fletcher and Leyffer to generate non-monotone iterations. We also use the concept of a multidimensional filter used by Gould et?al. (SIAM J. Optim. 15(1):17?C38, 2004) and introduce a new filter criterion showing good properties. Moreover, we introduce a new technique for reducing the size of the filter. For the algorithm, we present two different convergence analyses. First, we show that at least one of the limit points of the sequence of the iterates is first-order critical. Second, we prove the stronger property that all the limit points are first-order critical for a modified version of our algorithm. We also show that, under suitable conditions, all the limit points are second-order critical. Finally, we compare our algorithm with a natural trust-region algorithm and the filter trust-region algorithm of Gould et al. on the CUTEr unconstrained test problems Gould et?al. in ACM Trans. Math. Softw. 29(4):373?C394, 2003. Numerical results demonstrate the efficiency and robustness of our proposed algorithms.     

Developing an alcoholic‐assisted dispersive liquid–liquid microextraction for extraction of pentachlorophenol in water

Optimization of alcoholic‐assisted dispersive liquid–liquid microextraction of pentachlorophenol (PCP) and determination of it with high‐performance liquid chromatography (UV‐Vis detection) was investigated. A Plackett‐Burman design and a central composite design were applied to evaluate the alcoholic‐assisted dispersive liquid–liquid microextraction procedure. The effect of seven parameters on extraction efficiency was investigated. The factor studied were type and volume of extraction and dispersive solvents, amount of salt, and agitation time. According to Plackett‐Burman design results, the effective parameters were type and volume of extraction solvent and agitation time. Next, a central composite design was applied to obtain optimal condition. The optimized conditions were obtained at 170‐μL 1‐octanol and 5‐min agitation time. The enrichment factor of PCP was 242 with limits of detection of 0.04 μg L^?1. The linearity was 0.1–100 μg L^?1 and the extraction recovery was 92.7%. RSD for intra and inter day of extraction of PCP were 4.2% and 7.8%, respectively for five measurements. The developed method was successfully applied for the determination of PCP in environmental water samples.     

A combined parabolic-integral equation approach to the acoustic simulation of vibro-acoustic imaging

This paper aims to model ultrasound vibro-acoustography to improve our understanding of the underlying physics of the technique thus facilitating the collection of better images. Ultrasound vibro-acoustography is a novel imaging technique combining the resolution of high-frequency imaging with the clean (speckle-free) images obtained with lower frequency techniques. The challenge in modeling such an experiment is in the variety of scales important to the final image. In contrast to other approaches for modeling such problems, we break the experiment into three parts: high-frequency propagation, non-linear interaction and the propagation of the low-frequency acoustic emission. We then apply different modeling strategies to each part. For the high-frequency propagation we choose a parabolic approximation as the field has a strong preferred direction and small propagation angles. The non-linear interaction is calculated directly with Fourier methods for computing derivatives. Because of the low-frequency omnidirectional nature of the acoustic emission field and the piecewise constant medium we model the low-frequency field with a surface integral approach. We use our model to compare with experimental data and to visualize the relevant fields at points in the experiment where laboratory data is difficult to collect, in particular the source of the low-frequency field. To simulate experimental conditions we perform the simulations with the two frequencies 3 and 3.05 MHz with an inclusion of varying velocity submerged in water.     

Nonlinear total variation based noise removal algorithms

A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lanrange multipliers. The solution is obtained using the gradient-projection method. This amounts to solving a time dependent partial differential equation on a manifold determined by the constraints. As t → ∞ the solution converges to a steady state which is the denoised image. The numerical algorithm is simple and relatively fast. The results appear to be state-of-the-art for very noisy images. The method is noninvasive, yielding sharp edges in the image. The technique could be interpreted as a first step of moving each level set of the image normal to itself with velocity equal to the curvature of the level set divided by the magnitude of the gradient of the image, and a second step which projects the image back onto the constraint set.