63,881 research outputs found
Launcher attitude control: some additional design and optimization tools
This paper deals with the launcher attitude control during atmospheric flight. A two step approach combining an H1 control design and an optimization procedure is proposed. The first step is multi-objective stationary H1 design based on the Cross Standard Form. It provides easily a first rough solution from a few physical tuning parameters. The second step is a fine tuning using an multi-constraint satisfaction algorithm. This algorithm enables the certification criteria computed on the validation model to be met and is also used to propagate the nominal tuning to the full flight envelope
Decision-based genetic algorithms for solving multi-period project scheduling with dynamically experienced workforce
The importance of the flexibility of resources increased rapidly with the turbulent changes in the industrial context, to meet the customersâ requirements. Among all resources, the most important and considered as the hardest to manage are human resources, in reasons of availability and/or conventions. In this article, we present an approach to solve project scheduling with multi-period human resources allocation taking into account two flexibility levers. The first is the annual hours and working time regulation, and the second is the actorsâ multi-skills. The productivity of each operator was considered as dynamic, developing or degrading depending on the prior allocation decisions. The solving approach mainly uses decision-based genetic algorithms, in which, chromosomes donât represent directly the problem solution; they simply present three decisions: tasksâ priorities for execution, actorsâ priorities for carrying out these tasks, and finally the priority of working time strategy that can be considered during the specified working period. Also the principle of critical skill was taken into account. Based on these decisions and during a serial scheduling generating scheme, one can in a sequential manner introduce the project scheduling and the corresponding workforce allocations
Uncertainty in Soft Temporal Constraint Problems:A General Framework and Controllability Algorithms forThe Fuzzy Case
In real-life temporal scenarios, uncertainty and preferences are often
essential and coexisting aspects. We present a formalism where quantitative
temporal constraints with both preferences and uncertainty can be defined. We
show how three classical notions of controllability (that is, strong, weak, and
dynamic), which have been developed for uncertain temporal problems, can be
generalized to handle preferences as well. After defining this general
framework, we focus on problems where preferences follow the fuzzy approach,
and with properties that assure tractability. For such problems, we propose
algorithms to check the presence of the controllability properties. In
particular, we show that in such a setting dealing simultaneously with
preferences and uncertainty does not increase the complexity of controllability
testing. We also develop a dynamic execution algorithm, of polynomial
complexity, that produces temporal plans under uncertainty that are optimal
with respect to fuzzy preferences
Compositional Model Repositories via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences
The predominant knowledge-based approach to automated model construction,
compositional modelling, employs a set of models of particular functional
components. Its inference mechanism takes a scenario describing the constituent
interacting components of a system and translates it into a useful mathematical
model. This paper presents a novel compositional modelling approach aimed at
building model repositories. It furthers the field in two respects. Firstly, it
expands the application domain of compositional modelling to systems that can
not be easily described in terms of interacting functional components, such as
ecological systems. Secondly, it enables the incorporation of user preferences
into the model selection process. These features are achieved by casting the
compositional modelling problem as an activity-based dynamic preference
constraint satisfaction problem, where the dynamic constraints describe the
restrictions imposed over the composition of partial models and the preferences
correspond to those of the user of the automated modeller. In addition, the
preference levels are represented through the use of symbolic values that
differ in orders of magnitude
A Landscape Analysis of Constraint Satisfaction Problems
We discuss an analysis of Constraint Satisfaction problems, such as Sphere
Packing, K-SAT and Graph Coloring, in terms of an effective energy landscape.
Several intriguing geometrical properties of the solution space become in this
light familiar in terms of the well-studied ones of rugged (glassy) energy
landscapes. A `benchmark' algorithm naturally suggested by this construction
finds solutions in polynomial time up to a point beyond the `clustering' and in
some cases even the `thermodynamic' transitions. This point has a simple
geometric meaning and can be in principle determined with standard Statistical
Mechanical methods, thus pushing the analytic bound up to which problems are
guaranteed to be easy. We illustrate this for the graph three and four-coloring
problem. For Packing problems the present discussion allows to better
characterize the `J-point', proposed as a systematic definition of Random Close
Packing, and to place it in the context of other theories of glasses.Comment: 17 pages, 69 citations, 12 figure
- âŠ