A linear optimization approach to the combined production planning model

Two fundamental processes usually arise in the production planning of many industries. The first one consists of deciding how many final products of each type have to be produced in each period of a planning horizon, the well-known lot sizing problem. The other process consists of cutting raw materials in stock in order to produce smaller parts used in the assembly of final products, the well-studied cutting stock problem. In this paper the decision variables of these two problems are dependent of each other in order to obtain a global optimum solution. Setups that are typically present in lot sizing problems are relaxed together with integer frequencies of cutting patterns in the cutting problem. Therefore, a large scale linear optimizations problem arises, which is exactly solved by a column generated technique. It is worth noting that this new combined problem still takes the trade-off between storage costs (for final products and the parts) and trim losses (in the cutting process). We present some sets of computational tests, analyzed over three different scenarios. These results show that, by combining the problems and using an exact method, it is possible to obtain significant gains when compared to the usual industrial practice, which solve them in sequence. (C) 2010 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Fault Coverage-Driven Incremental Test Generation

In this paper, we consider a classical problem of complete test generation for deterministic finite-state machines (FSMs) in a more general setting. The first generalization is that the number of states in implementation FSMs can even be smaller than that of the specification FSM. Previous work deals only with the case when the implementation FSMs are allowed to have the same number of states as the specification FSM. This generalization provides more options to the test designer: when traditional methods trigger a test explosion for large specification machines, tests with a lower, but yet guaranteed, fault coverage can still be generated. The second generalization is that tests can be generated starting with a user-defined test suite, by incrementally extending it until the desired fault coverage is achieved. Solving the generalized test derivation problem, we formulate sufficient conditions for test suite completeness weaker than the existing ones and use them to elaborate an algorithm that can be used both for extending user-defined test suites to achieve the desired fault coverage and for test generation. We present the experimental results that indicate that the proposed algorithm allows obtaining a trade-off between the length and fault coverage of test suites.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.

MEH-PPV photobleaching control by femtosecond pulse shaping

In the work reported here we were able to control the photobleaching of poly[2-methoxy-5-(2`-ethyl-hexyloxy)-1,4-phenylene vinylene] (MEH-PPV), excited by two-photon absorption, using femtosecond pulse shaping. By applying a cosine-like spectral phase mask, we observe a reduction of three times in the photobleaching rate, while the fluorescence intensity decreases by 20%, in comparison to the values obtained with a Fourier-transform-limited pulse. These results demonstrate an interesting trade-off between photobleaching rate and nonlinear fluorescence intensity. The possible mechanism behind this process is discussed in terms of the pulse spectral profile and the absorbance band of MEH-PPV. (C) 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Mice and larvae tracking using a particle filter with an auto-adjustable observation model

This paper proposes a novel way to combine different observation models in a particle filter framework. This, so called, auto-adjustable observation model, enhance the particle filter accuracy when the tracked objects overlap without infringing a great runtime penalty to the whole tracking system. The approach has been tested under two important real world situations related to animal behavior: mice and larvae tracking. The proposal was compared to some state-of-art approaches and the results show, under the datasets tested, that a good trade-off between accuracy and runtime can be achieved using an auto-adjustable observation model. (C) 2009 Elsevier B.V. All rights reserved.

OZONATION OF SEDIMENTS FROM AN URBAN LAKE: AN EXPLORATORY INVESTIGATION

An exploratory investigation was conducted on the effects of application of ozone on the removal of organic and inorganic contaminants and the reduction of settleable solids in urban lake sediments. Homogenized sediment samples were treated in a batch reactor with an external recirculation loop and ozone feed from a Venturi injector. The ozone generating system was fed with ambient air with small footprint and operational simplicity. Ozone mass application (g/h) and contact time (min) were varied over wide ranges during testing. The effects of the ozone mass applied per unit time and the contact time on contaminant removal efficiencies were analyzed and a trade-off between the costs of ozonation and of solids treatment and disposal was proposed. The minimum ozone mass application required for total contaminant removal apparently depended on the type of organic contaminant present. An apparent influence of inorganic contaminant speciation on the removal efficiency was found and discussed.

Extensions of discrete triangular distributions and boundary bias in kernel estimation for discrete functions

Asymmetric discrete triangular distributions are introduced in order to extend the symmetric ones serving for discrete associated kernels in the nonparametric estimation for discrete functions. The extension from one to two orders around the mode provides a large family of discrete distributions having a finite support. Establishing a bridge between Dirac and discrete uniform distributions, some different shapes are also obtained and their properties are investigated. In particular, the mean and variance are pointed out. Applications to discrete kernel estimators are given with a solution to a boundary bias problem. (C) 2010 Elsevier B.V. All rights reserved.

Bias-corrected Pearson estimating functions for Taylor`s power law applied to benthic macrofauna data

Estimation of Taylor`s power law for species abundance data may be performed by linear regression of the log empirical variances on the log means, but this method suffers from a problem of bias for sparse data. We show that the bias may be reduced by using a bias-corrected Pearson estimating function. Furthermore, we investigate a more general regression model allowing for site-specific covariates. This method may be efficiently implemented using a Newton scoring algorithm, with standard errors calculated from the inverse Godambe information matrix. The method is applied to a set of biomass data for benthic macrofauna from two Danish estuaries. (C) 2011 Elsevier B.V. All rights reserved.

What aspects of shiftwork influence off-shift well-being of healthcare workers?

Characteristics of shiftwork schedules have implications for off-shift well-being. We examined the extent to which several shift characteristics (e.g., shift length, working sundays) are associated with three aspects of off-shift well-being: work-to-family conflict, physical well-being, and mental wellbeing. We also investigated whether these relationships differed in four nations. The Survey of Work and Time was completed by 906 healthcare professionals located in Australia, Brazil, Croatia, and the USA. Hierarchical multiple regression analyses supported the hypothesis that shiftwork characteristics account for significant unique variance in all three measures of well-being beyond that accounted for by work and family demands and personal characteristics. The patterns of regression weights indicated that particular shiftwork characteristics have differential relevance to indices of work-to-family conflict, physical well-being, and mental well-being. Our findings suggest that healthcare organizations should carefully consider the implications of shiftwork characteristics for off-shift well-being. Furthermore, although our findings did not indicate national differences in the nature of relationships between shift characteristics and well-being, shiftwork characteristics and demographics for healthcare professionals differ in systematic ways among nations; as such, effective solutions may be context-specific. (c) 2008 Elsevier Ltd. All rights reserved.