Scheduling batches with sequential job processing for two-machine flow and open shops

In this paper, we study a problem of scheduling and batching on two machines in a flow-shop and open-shop environment. Each machine processes operations in batches, and the processing time of a batch is the sum of the processing times of the operations in that batch. A setup time, which depends only on the machine, is required before a batch is processed on a machine, and all jobs in a batch remain at the machine until the entire batch is processed. The aim is to make batching and sequencing decisions, which specify a partition of the jobs into batches on each machine, and a processing order of the batches on each machine, respectively, so that the makespan is minimized. The flow-shop problem is shown to be strongly NP-hard. We demonstrate that there is an optimal solution with the same batches on the two machines; we refer to these as consistent batches. A heuristic is developed that selects the best schedule among several with one, two, or three consistent batches, and is shown to have a worst-case performance ratio of 4/3. For the open-shop, we show that the problem is NP-hard in the ordinary sense. By proving the existence of an optimal solution with one, two or three consistent batches, a close relationship is established with the problem of scheduling two or three identical parallel machines to minimize the makespan. This allows a pseudo-polynomial algorithm to be derived, and various heuristic methods to be suggested.

Low temperature crystal structures of two rhodanine derivatives, 3-amino rhodanine and 3-methyl rhodanine: geometry of the rhodanine ring

Rhodanines (2-thio-4-oxothiazolidines) are synthetic small molecular weight organic molecules with diverse applications in biochemistry, medicinal chemistry, photochemistry, coordination chemistry and industry. The X-ray crystal structure determination of two rhodanine derivatives, namely (I), 3-aminorhodanine [3-amino-2-thio-4-oxothiazolidine], C3H4N2OS2, and (II) 3-methylrhodanine [3-methyl-2-thio-4-oxothiazolidine], C4H5NOS2, have been conducted at 100 K. I crystallizes in the monoclinic space group P2(1)/n with unit cell parameters a = 9.662(2), b = 9.234(2), c = 13.384(2) angstrom, beta = 105.425(3)degrees, V = 1151.1(3) angstrom(3), Z = 8 (2 independent molecules per asymmetric unit), density (calculated) = 1.710 mg/m(3), absorption coefficient = 0.815 mm(-1). II crystallizes in the orthorhombic space group Iba2 with unit cell a = 20.117(4), b = 23.449(5), c = 7.852(2) angstrom, V = 3703.9(12) angstrom(3), Z = 24 (three independent molecules per asymmetric unit), density (calculated) = 1.584 mg/m(3), absorption coefficient 0.755 mm(-1). For I in the final refinement cycle the data/restraints/parameter ratios were 2639/0/161, goodness-of-fit on F-2 = 0.934, final R indices [I > 2sigma(I)] were R1 = 0.0299, wR2 = 0.0545 and R indices (all data) R1 = 0.0399, wR2 = 0.0568. The largest difference peak and hole were 0.402 and -0.259 e angstrom(-3). For II in the final refinement cycle the data/restraints/parameter ratios were 3372/1/221, goodness-of-fit on F(2) = 0.950, final R indices [I > 2sigma(I)] were R1 = 0.0407, wR2 = 0.1048 and R indices (all data) R1 = 0.0450, wR2 = 0.1088. The absolute structure parameter = 0.19(9) and largest difference peak and hole 0.934 and -0.301 e angstrom(-3). Details of the geometry of the five molecules (two for I and three for II) and the crystal structures are fully discussed. Corresponding features of the molecular geometry are highly consistent and firmly establish the geometry of the rhodanine

Levitated liquid droplets in AC and DC magnetic field

Electromagnetic levitation of liquid metal droplets can be used to measure the properties of highly reactive liquid materials. Two independent numerical models, the commercial COMSOL and the spectral-collocation based free surface code SPHINX, have been applied to solve the transient electromagnetic, fluid flow and thermodynamic equations, which describe the levitated liquid motion and heating processes. The SPHINX model incorporates free surface deformation to accurately model the oscillations that result from the interaction between the electromagnetic and gravity forces, temperature dependent surface tension, magnetically controlled turbulent momentum transport. The models are adapted to incorporate periodic laser heating at the top of the droplet, which is used to measure the thermal conductivity of the material. Novel effects in the levitated droplet of magnetically damped turbulence and nonlinear growth of velocities in high DC magnetic field are analysed.

In vitro-in vivo correlations of self-emulsifying drug delivery systems combining the dynamic lipolysis model and neuro-fuzzy networks

The aim of the current study was to evaluate the potential of the dynamic lipolysis model to simulate the absorption of a poorly soluble model drug compound, probucol, from three lipid-based formulations and to predict the in vitro-in vivo correlation (IVIVC) using neuro-fuzzy networks. An oil solution and two self-micro and nano-emulsifying drug delivery systems were tested in the lipolysis model. The release of probucol to the aqueous (micellar) phase was monitored during the progress of lipolysis. These release profiles compared with plasma profiles obtained in a previous bioavailability study conducted in mini-pigs at the same conditions. The release rate and extent of release from the oil formulation were found to be significantly lower than from SMEDDS and SNEDDS. The rank order of probucol released (SMEDDS approximately SNEDDS > oil formulation) was similar to the rank order of bioavailability from the in vivo study. The employed neuro-fuzzy model (AFM-IVIVC) achieved significantly high prediction ability for different data formations (correlation greater than 0.91 and prediction error close to zero), without employing complex configurations. These preliminary results suggest that the dynamic lipolysis model combined with the AFM-IVIVC can be a useful tool in the prediction of the in vivo behavior of lipid-based formulations.