85,216 research outputs found
Uncertainty propagation in multi-agent systems for multidisciplinary optimization problems
International audienceBecause of uncertainties on models and variables, deterministic multidisciplinary optimization may achieve under-sizing (without design margins) or over-sizing (with arbitrary design margins). Thus, it is necessary to implement multidisciplinary optimization methods that take into account the uncertainties in order to design systems that are both robust and reliable. Probabilistic methods such as reliability-based design optimization (RBDO) or robust design methods, provide designers with powerful decision-making tools but may involve very time-consuming calculations. New optimization approaches have been developed to deal with such complex problems. Auto-adaptive Multi-Agent Systems (AMAS) is a new approach developed recently, allowing to take into account the various aspects of a multidisciplinary optimization problem (multi-level, computation burden etc.). This approach was suggested for solving complex deterministic optimization problem. Now, the question of the integration of uncertainties in this multi-agent based optimization arises. The aim of this paper is to propose a new methodology for integrating the treatment of uncertainties in an adaptive multi-agent system for sequential optimization. The developed method employs a single loop process in which cycles of deterministic optimization alternate with evaluations of the system reliability. For each cycle, the optimization and the reliability analysis are decoupled from each other. The reliability analysis is carried out at agent level and only after the resolution of the deterministic optimization, to verify the feasibility of the constraints under uncertainties. Following the probabilistic study, the constraints violated (with low reliability) are shifted to the area of feasibility by integrating adaptive safety coeficients whose calculations are based on the agent-level reliability information. The method developed is applied to a conceptual aircraft design problem
Optimal treatment allocations in space and time for on-line control of an emerging infectious disease
A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulation–optimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America
Foraging as an evidence accumulation process
A canonical foraging task is the patch-leaving problem, in which a forager
must decide to leave a current resource in search for another. Theoretical work
has derived optimal strategies for when to leave a patch, and experiments have
tested for conditions where animals do or do not follow an optimal strategy.
Nevertheless, models of patch-leaving decisions do not consider the imperfect
and noisy sampling process through which an animal gathers information, and how
this process is constrained by neurobiological mechanisms. In this theoretical
study, we formulate an evidence accumulation model of patch-leaving decisions
where the animal averages over noisy measurements to estimate the state of the
current patch and the overall environment. Evidence accumulation models belong
to the class of drift diffusion processes and have been used to model decision
making in different contexts. We solve the model for conditions where foraging
decisions are optimal and equivalent to the marginal value theorem, and perform
simulations to analyze deviations from optimal when these conditions are not
met. By adjusting the drift rate and decision threshold, the model can
represent different strategies, for example an increment-decrement or counting
strategy. These strategies yield identical decisions in the limiting case but
differ in how patch residence times adapt when the foraging environment is
uncertain. To account for sub-optimal decisions, we introduce an
energy-dependent utility function that predicts longer than optimal patch
residence times when food is plentiful. Our model provides a quantitative
connection between ecological models of foraging behavior and evidence
accumulation models of decision making. Moreover, it provides a theoretical
framework for potential experiments which seek to identify neural circuits
underlying patch leaving decisions
Dynamic Robust Transmission Expansion Planning
Recent breakthroughs in Transmission Network Expansion Planning (TNEP) have
demonstrated that the use of robust optimization, as opposed to stochastic
programming methods, renders the expansion planning problem considering
uncertainties computationally tractable for real systems. However, there is
still a yet unresolved and challenging problem as regards the resolution of the
dynamic TNEP problem (DTNEP), which considers the year-by-year representation
of uncertainties and investment decisions in an integrated way. This problem
has been considered to be a highly complex and computationally intractable
problem, and most research related to this topic focuses on very small case
studies or used heuristic methods and has lead most studies about TNEP in the
technical literature to take a wide spectrum of simplifying assumptions. In
this paper an adaptive robust transmission network expansion planning
formulation is proposed for keeping the full dynamic complexity of the problem.
The method overcomes the problem size limitations and computational
intractability associated with dynamic TNEP for realistic cases. Numerical
results from an illustrative example and the IEEE 118-bus system are presented
and discussed, demonstrating the benefits of this dynamic TNEP approach with
respect to classical methods.Comment: 10 pages, 2 figures. This article has been accepted for publication
in a future issue of this journal, but has not been fully edited. Content may
change prior to final publication. Citation information: DOI
10.1109/TPWRS.2016.2629266, IEEE Transactions on Power Systems 201
Stochastic Database Cracking: Towards Robust Adaptive Indexing in Main-Memory Column-Stores
Modern business applications and scientific databases call for inherently
dynamic data storage environments. Such environments are characterized by two
challenging features: (a) they have little idle system time to devote on
physical design; and (b) there is little, if any, a priori workload knowledge,
while the query and data workload keeps changing dynamically. In such
environments, traditional approaches to index building and maintenance cannot
apply. Database cracking has been proposed as a solution that allows on-the-fly
physical data reorganization, as a collateral effect of query processing.
Cracking aims to continuously and automatically adapt indexes to the workload
at hand, without human intervention. Indexes are built incrementally,
adaptively, and on demand. Nevertheless, as we show, existing adaptive indexing
methods fail to deliver workload-robustness; they perform much better with
random workloads than with others. This frailty derives from the inelasticity
with which these approaches interpret each query as a hint on how data should
be stored. Current cracking schemes blindly reorganize the data within each
query's range, even if that results into successive expensive operations with
minimal indexing benefit. In this paper, we introduce stochastic cracking, a
significantly more resilient approach to adaptive indexing. Stochastic cracking
also uses each query as a hint on how to reorganize data, but not blindly so;
it gains resilience and avoids performance bottlenecks by deliberately applying
certain arbitrary choices in its decision-making. Thereby, we bring adaptive
indexing forward to a mature formulation that confers the workload-robustness
previous approaches lacked. Our extensive experimental study verifies that
stochastic cracking maintains the desired properties of original database
cracking while at the same time it performs well with diverse realistic
workloads.Comment: VLDB201
Generating health technology assessment evidence for rare diseases
Objectives: Rare diseases are often heterogeneous in their progression and response to treatment, with only a small population for study. This provides challenges for evidence generation to support HTA, so novel research methods are required.
Methods: Discussion with an expert panel was augmented with references and case studies to explore robust approaches for HTA evidence generation for rare disease treatments.
Results: Traditional RCTs can be modified using sequential, three-stage or adaptive designs to gain more power from a small patient population or to focus trial design. However, such designs need to maintain important design aspects such as randomization and blinding and be analyzed to take account of the multiple analyses performed. N-of-1 trials use within-patient randomization to test repeat periods of treatment and control until a response is clear. Such trials could be particularly valuable for rare diseases and when prospectively planned across several patients and analyzed using Bayesian techniques, a population effect can be estimated that might be of value to HTA. When the optimal outcome is unclear in a rare disease, disease specific patient reported outcomes can elucidate impacts on patients’ functioning and wellbeing. Likewise, qualitative research can be used to elicit patients’ perspectives, with just a small number of patients.
Conclusions: International consensus is needed on ways to improve evidence collection and assessment of technologies for rare diseases, which recognize the value of novel study designs and analyses in a setting where the outcomes and effects of importance are yet to be agreed.</p
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