Flexible Decision Rules Research Articles

Dynamic opportunistic maintenance planning for multi-component redundant systems with various types of opportunities

Opportunistic maintenance (OM) is a key solution to reduce the maintenance costs or/and to improve the system dependability/performance. However, the existing OM models are mainly developed to specific classes of systems (series structures) with specific types of maintenance opportunities (MOs) such as component failure. The objective of this paper is to develop a dynamic OM approach for multi-component redundant systems such as parallel, parallel-series, series-parallel, and k-out-of-n, etc. Various types of MOs are also considered. To this purpose, a generalized MO model is firstly proposed for the modeling and formulation of existing MOs and new ones. A dynamic OM model with flexible decision rules allowing to consider various types of MOs in maintenance decision-making is then introduced. In addition, a multi-mode logistic support model is also proposed to better incorporate different types of MOs with associated logistic support requirements. To find the best OM scenario, an efficient optimization algorithm using Genetic algorithm with memory is developed. The proposed optimization algorithm allows also updating online an OM maintenance plan in presence of new MOs which may occur with time. The uses and advantages of the proposed OM approach are illustrated through a six-component redundant system.

3.1.2 Design Catalogues: An Efficient Search Approach for Improved Flexibility in Engineering Systems Design

AbstractThis paper proposes and demonstrates design catalogues as a computationally efficient method for identifying improved designs for complex technological systems. This new process significantly speeds up the analysis of systems that will operate and will be managed under various uncertainty scenarios. It enables analysts to explore the design space more fully, taking into account a greater number of design parameters and variables. It can lead to design solutions with greatly improved lifecycle performance. The design catalogue consists of a small subset of designs that collectively perform reasonably well over a range of possible scenarios. The catalogue approach contrasts with the usual approach that optimizes designs for each scenario, and thus can only afford to examine a limited number of situations. Each design in the catalogue consists of combinations of design variables, parameters, and flexibility decision rules. The set of designs in the catalogue is determined using adaptive One‐Factor‐At‐a‐Time (aOFAT) analysis. An example demonstrates the use and how it leads to improved lifecycle performance compared to a standard benchmark design.

Superchain procedures in clinical trials with multiple objectives

This paper introduces a new class of multiple testing procedures for addressing multiplicity problems arising in clinical trials with multiple objectives grouped into families. The families may correspond to equally important sets of objectives (co-primary endpoints) or ordered sets of objectives (primary and secondary endpoints). The procedures, termed superchain procedures, serve as an extension of several classes of other multiple testing procedures, including chain procedures and parallel gatekeeping procedures. Superchain procedures exhibit several desirable features, including flexible decision rules that can be tailored to a broad class of win criteria in confirmatory clinical trials. Additionally, superchain procedures enable the trial's sponsor to efficiently incorporate available distributional information to improve overall power. Finally, superchain procedures rely on straightforward testing algorithms, which facilitates their development. We illustrate the new methodology by using clinical trial examples with two and three families of objectives.

Automated sleep–wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules

AbstractThe classification of sleep–wake stages suffers from poor standardization in scoring criteria and heterogeneous conditioning of polysomnographic signals. To improve applicability of fully automated sleep staging, we have designed a formal classification framework to rigorously (1) select robust candidate features, (2) emulate artificial neural network classifiers, and (3) assign sleep–wake stages using flexible decision rules. An extensive database of 48 PSG records scored in 20 s epochs by two independent clinicians was used. A small subset of 2 s elementary epochs representative of each stages with unequivocal expert scores was selected to form a limited set of learning exemplars. From 16 statistical, spectral and non‐linear candidate features extracted in 2 s epochs from EEG and EMG signals, a sequential forward search selected an optimal set of five features with a 22% error rate. Multiple layer perceptrons were trained from this optimal feature set while classification accuracy was assessed using the unequivocal instance subset. A simple majority vote among 10 consecutive classifier outputs ensured a final scoring resolution comparable to that of the experts. Poor classification performance was obtained for movement time, wakefulness, and intermediate sleep stages with a 36±15% error rate (Cohen's kappa 0.48±0.18). In contrast, deep and paradoxical sleep was classified with an 82% accuracy not far from inter‐expert expert agreement (83±3%). Significant improvements should be expected using a larger learning set compensating for a high inter‐individual variability, and decision rules incorporating more domain‐knowledge. Copyright © 2009 John Wiley & Sons, Ltd.

Prey discrimination by a generalist coccinellid predator: effect of prey age or parasitism?

Summary1. Patterns of prey discrimination by the generalist predatory coccinellid Coleomegilla maculata lengi Timb. (Coleoptera: Coccinellidae) were studied in relation to prey quality, under laboratory conditions.2. Choice experiments were performed in which second‐ and fourth‐instar coccinellids had the choice between Trichoplusia ni (Lepidoptera: Noctuidae) eggs that were young or old, unparasitised or parasitised by Trichogramma evanescens Westwood (Hymenoptera: Trichogrammatidae).3. Coleomegilla maculata larvae did not exhibit any preference for parasitised or unparasitised eggs at a similar stage of development but always preferred younger eggs, regardless of whether or not they were parasitised. Furthermore, the percentage of rejection was higher and handling time longer on the less preferred egg type. Pre‐imaginal development time, food intake to reach adulthood, and survival of coccinellid immatures were altered when coccinellid larvae were fed with parasitised and old unparasitised eggs.4. These results indicate that C. maculata larvae select eggs based on their stage of development regardless of parasitism, and that prey quality of aged prey is lower.5. Fourth‐instar coccinellid larvae spent less time in patches containing solely parasitised old eggs, and their level of exploitation was greatly reduced, compared with homogeneous patches containing unparasitised young eggs. This suggests that C. maculata larvae respond to variable patch quality by using flexible decision rules that reflect the payoff of the patch.

Flexible patch time allocation by the leafminer parasitoid, Opius dimidiatus

Abstract. The ability to use flexible decision rules can be an advantage to parasitoid females searching for patchily‐distributed hosts. In a series of laboratory experiments the hypothesis that Opius dimidiatus, a solitary parasitoid of the chrysanthemum leafminer (Liriomyza trifolii), adjusts the time she allocates to searching for her larval hosts in response to both patch qualities and experiences with hosts was tested by varying such patch parameters as area, presence of host mines and density of host mines, and by allowing ovipositions and encounters with parasitized hosts. Though leaf area was not a factor, the presence of host mines in a leaf did increase the time a female O.dimidiatus spent searching, over time spent on unmined leaves. When host mine density was increased, females responded by increasing their search period in a density‐dependent manner, suggesting a perception of patch quality. Ovipositions in hosts caused females to reset their‘giving‐up time’(GUT), or increase search intensity, by adding an amount of search time that increased with each successive oviposition. Conversely, encounters with parasitized (unsuitable) hosts incremented the GUT, but by an amount that decreased with each successive encounter.

THE VALUES OF ABILITY TESTING: IMPLICATIONS OF MULTIPLE PERSPECTIVES ABOUT CRITERIA AND STANDARDS*

ABSTRACTIn response to an invitation from the National Council on Measurement in Education to review the National Academy of Sciences report on Ability Testing, this paper explores the ideological consequences of the report's tacit acceptance of a pervasive institutional perspective. By focussing on selection and classification as the major functions of ability testing in both employment and education and by embracing job performance and grades as primary criteria, the report puts implicit priority on productivity and efficiency as the primary social values to be served by testing — tempered, of course, by the legal requirements of equal opportunity. As a consequence, the conflict between productivity and parity is given central status in the report along with a call for flexible decision rules to accommodate both, but at the expense of simultaneously downplaying other values important in the workplace and especially in education. This paper examines the utility of systematically contrasting multiple value perspectives as a means of reducing the likelihood that any single perspective will dominate our assumptions and methodologies.