Algorithms for no-wait flowshops with total completion time subject to makespan

The m-machine no-wait flowshop scheduling problem, with the objective of minimizing total completion time subject to the constraint that makespan has to be less than or equal to a certain value, is addressed in this paper. First, an algorithm is proposed to find an upper bound on the makespan in case the upper bound is not given or unknown. Given the upper bound on makespan, a proposed algorithm (PAL) with five versions L (1, 5, 10, 15, and 20) and a genetic algorithm (GA) are utilized for solving the problem. Furthermore, a dominance relation is established for the case of four machines. The five versions of PAL and GA are evaluated on randomly generated problems with different number of jobs and number of machines. Computational experiments show that the errors of PA1 0, PA15, and PA20 are much smaller than that of GA while the CPU times of PA10, PA15, and PA20 are significantly smaller than that of GA. Therefore, the algorithms PA10, PA15, and PA20 are superior to the GA algorithm.     

Permutation flow shops with exact time lags to minimise maximum lateness

In this paper we investigate the m-machine permutation flow shop scheduling problem where exact time lags are defined between consecutive operations of every job. This generic model can be used for the study and analysis of various real situations that may arise, for instance, in the food-producing, pharmaceutical and steel industries. The objective is to minimise the maximum lateness. We study polynomial special cases and provide a dominance relation. We derive lower and upper bounds that are integrated in a branch-and-bound procedure to solve the problem. Three branching schemes are proposed and compared. We perform a computational analysis to evaluate the efficiency of the developed method.     

The Tricriteria Two-Machine Flowshop Scheduling Problem

The literature on the two-machine flowshop scheduling problem reveals that the problem has been addressed with bicriteria of either makespan and mean flowtime or makespan and maximum tardiness (lateness). This paper extends the problem to all the three criteria (tricriteria) where the objective is to minimize a weighted sum of makespan, mean flowtime, and maximum lateness. A dominance relation and a lower bound are established. The dominance relation and the lower bound are used to develop a branch-and-bound algorithm. The dominance relation is also used to develop several heuristics. An extensive computational analysis is conducted to evaluate the performance of the dominance relation and the heuristics. The analysis shows that the dominance relation is effective. The analysis also shows that the heuristics are quite efficient, and some heuristics have an error of less than 1%. Moreover, these heuristics have the desirable property that the error does not increase by the number of jobs.     

Melt-extracted oxide ceramic fibres — the fundamentals

A melt-extraction technique, using a sharpened molybdenum wheel, has been used to produce fine oxide ceramic fibres. Wetting of the molybdenum wheel by molten ceramic is a key parameter in the melt-extraction process. Two types of fibre are generally obtained, depending on the extraction speed. At very low wheel speed, fine and uniform fibres of high quality are produced. However, when the wheel speed exceeds a critical velocity, Rayleigh waves are formed on the free surface of the fibres. Moreover, the average fibre thickness first increases with the wheel velocity, then passes through a maximum, decreasing at high velocity. This thickness variation is discussed in terms of both surface tension and viscosity of the liquid ceramics.     

A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application

The assembly flowshop scheduling problem has been addressed recently in the literature. There are many problems that can be modeled as assembly flowshop scheduling problems including queries scheduling on distributed database systems and computer manufacturing. The problem has been addressed with respect to either makespan or total completion time criterion in the literature. In this paper, we address the problem with respect to a due date-based performance measure, i.e., maximum lateness. We formulate the problem and obtain a dominance relation. Moreover, we propose three heuristics for the problem: particle swarm optimization (PSO), Tabu search, and EDD. PSO has been used in the areas of function optimization, artificial neural network training, and fuzzy system control in the literature. In this paper, we show how it can be used for scheduling problems. We have conducted extensive computational experiments to compare the three heuristics along with a random solution. The computational analysis indicates that Tabu outperforms the others for the case when the due dates range is relatively wide. It also indicates that the PSO significantly outperforms the others for difficult problems, i.e., tight due dates. Moreover, for difficult problems, the developed dominance relation helps reduce error by 65%.     

Platelet survival in childhood idiopathic thrombocytopenic purpura in remission

Mean platelet survival was significantly decreased (P less than 0.01) in three children with chronic and five children with acute ITP in remission. Sera from these patients sensitized normal platelets, as demonstrated by increased phagocytosis by autologous granulocytes. These results may suggest that the remission in some patients with ITP is obtained by compensatory platelet production.     

Interferometric investigation and simulation of refractive index in glass matrixes containing nanoparticles of varying sizes

The relationship between refractive index and nanoparticle radii of cadmium selenide (CdSe) nanoparticles embedded within glass matrixes was investigated experimentally and by simulations. A homemade automated Michelson interferometer arrangement employing a rotating table and a He-Ne laser source at a wavelength of 632.8 nm determined the refractive index versus nanoparticle radii of embedded cadmium selenide (CdSe) nanoparticles. The refractive index was found to decrease linearly with nanoparticle radius increase. However, one sample showed a step increase in refractive index; on spectroscopic analysis, it was found that its resonant wavelength matched that of the He-Ne source wavelength. The simulations showed that two conditions caused the step increase in refractive index: low plasma frequency and matched sample and source resonances. This simple interferometer setup defines a new method of determining the radii of nanoparticles embedded in substrates and enables refractive index tailoring by modification of exact annealing conditions.     

Exergetic and environmental performance improvement in cement production process by driving force distribution

This paper presents an investigation of the effects of temperature gradient distribution by the aid of a secondary burner on exergetic and environmental functions of the cement production process. For this reason, the burning system of the cement production (kiln &; preheater) process was simulated in four thermal areas. Three lines of cement production with 2,000, 2,300 and 2,600 ton/day were investigated. Fuel injection ratio into the secondary burner, from 10 to 40 percent was studied for each line. The obtained results show that, for cyclone preheaters, fuel injection into the secondary burner up to a proportion resulting in the minimum temperature required for alite formation (2,200 °C) in the kiln burning zone is suitable. For shaft preheaters, however, according to percent calcinations, there exists an optimum proportion for 15 to 20 percent injection fuel into secondary burner. Finally, it was shown that the secondary burner application can reduce the exergy losses about 25 percent, which leads to a reduction of the green house gases of about 35000 cubic meters per year for each ton per day of clinker production.     

Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine

This study is deals with artificial neural network (ANN) and fuzzy expert system (FES) modelling of a gasoline engine to predict engine power, torque, specific fuel consumption and hydrocarbon emission. In this study, experimental data, which were obtained from experimental studies in a laboratory environment, have been used. Using some of the experimental data for training and testing an ANN for the engine was developed. Also the FES has been developed and realized. In this systems output parameters power, torque, specific fuel consumption and hydrocarbon emission have been determined using input parameters intake valve opening advance and engine speed. When experimental data and results obtained from ANN and FES were compared by t-test in SPSS and regression analysis in Matlab, it was determined that both groups of data are consistent with each other for p > 0.05 confidence interval and differences were statistically not significant. As a result, it has been shown that developed ANN and FES can be used reliably in automotive industry and engineering instead of experimental work.