An efficient heuristic for scheduling batches of parts in a flexible flow system

Flexible manufacturing systems (FMSs) are a class of automated systems that can be used to improve productivity in batch manufacturing. Four stages of decision making have been defined for an FMS—the design, planning, scheduling, and control stages. This research focuses on the planning stage, and specifically in the area of scheduling batches of parts through the system.The literature to date on the FMS planning stage has mostly focused on the machine grouping, tool loading, and parttype selection problems. Our research carries the literature a step further by addressing the problem of scheduling batches of parts. Due to the use of serial-access material-handling systems in many FMSs, the batch-scheduling problem is modeled for a flexible flow system (FFS). This model explicitly accounts for setup times between batches that are dependent on their processing sequence.A heuristic procedure is developed for this batch-scheduling problem—the Maximum Savings (MS) heuristic. The MS heuristic is based upon the savings in time associated with a particular sequence and selecting the one with the maximum savings. It uses a two-phase method, with the savings being calculated in phase I, while a branch-and-bound procedure is employed to seek the best heuristic solution in phase II. Extensive computational results are provided for a wide variety of problems. The results show that the MS heuristic provides good-quality solutions.     

Preliminary model of the Ribeira Grande geothermal field (Azores Islands)

On the basis of a geological - volcanological - geophysical survey, integrated with petrological and geochronological data, a structural model, including a shallow magmatic body, was defined for the Ribeira Grande area, Sao Miguel island (Azores). Two series of production tests were carried out in the Pico Vermelho 1 and Ribeira Grande 1 wells, which led to a preliminary definition of the deep fluid circulation. The data obtained from these studies were used to reconstruct a preliminary model of the Ribeira Grande geothermal field.     

Effect of cationic surfactant and inorganic anions on the electrochemical behavior of carbon steel in formation water

Corrosion behavior of carbon steel in formation water associated crude oil from Egyptian western desert was studied at various concentrations of dodecyl dimethyl ammonium chloride (CS) as a cationic surfactant. Polarization curves indicate that CS has a good inhibition efficiency for carbon steel in formation water and behaves as cathodic inhibitor. The inhibition efficiency was found to increase with increase in CS concentration until reaches a maximum constant value corresponding to the critical micelle concentration of CS and decrease with increase in solution temperature. The adsorption of CS follows the kinetic thermodynamic model and Flory-Huggins isotherm. Thermodynamic parameters obtained indicate that that the presence of the CS increases the activation energy. Polarization and Impedance measurements indicate that the addition of KI has a significant synergistic effect with CS and results in increase the inhibition efficiency of CS in formation water.     

A pattern recognition-based approach for phylogenetic network construction with constrained recombination

The tree representation of evolutionary relationship oversimplifies the view of the process of evolution as it cannot take into account the events such as horizontal gene transfer, hybridization, homoplasy and genetic recombination. Several algorithms exist for constructing phylogenetic networks which result from events such as horizontal gene transfer, hybridization and homoplasy. Very little work has been published on the algorithmic detail of phylogenetic networks with constrained recombination. The problem of minimizing the number of recombinations in a phylogenetic network, constructed using binary DNA sequences, is NP-hard. In this paper, we propose a pattern recognition-based O(n²) time approach for constructing the phylogenetic network, where n is the number of nodes or sequences in the input data. The network is constructed with the restriction that no two cycles in the network share a common node.