31,518 research outputs found
A Neuroevolutionary Approach to Stochastic Inventory Control in Multi-Echelon Systems
Stochastic inventory control in multi-echelon systems poses hard problems in optimisation under uncertainty. Stochastic programming can solve small instances optimally, and approximately solve larger instances via scenario reduction techniques, but it cannot handle arbitrary nonlinear constraints or other non-standard features. Simulation optimisation is an alternative approach that has recently been applied to such problems, using policies that require only a few decision variables to be determined. However, to find optimal or near-optimal solutions we must consider exponentially large scenario trees with a corresponding number of decision variables. We propose instead a neuroevolutionary approach: using an artificial neural network to compactly represent the scenario tree, and training the network by a simulation-based evolutionary algorithm. We show experimentally that this method can quickly find high-quality plans using networks of a very simple form
Genetic algorithms applied to the scheduling of the Hubble Space Telescope
A prototype system employing a genetic algorithm (GA) has been developed to support the scheduling of the Hubble Space Telescope. A non-standard knowledge structure is used and appropriate genetic operators have been created. Several different crossover styles (random point selection, evolving points, and smart point selection) are tested and the best GA is compared with a neural network (NN) based optimizer. The smart crossover operator produces the best results and the GA system is able to evolve complete schedules using it. The GA is not as time-efficient as the NN system and the NN solutions tend to be better
An efficient method for multiobjective optimal control and optimal control subject to integral constraints
We introduce a new and efficient numerical method for multicriterion optimal
control and single criterion optimal control under integral constraints. The
approach is based on extending the state space to include information on a
"budget" remaining to satisfy each constraint; the augmented
Hamilton-Jacobi-Bellman PDE is then solved numerically. The efficiency of our
approach hinges on the causality in that PDE, i.e., the monotonicity of
characteristic curves in one of the newly added dimensions. A semi-Lagrangian
"marching" method is used to approximate the discontinuous viscosity solution
efficiently. We compare this to a recently introduced "weighted sum" based
algorithm for the same problem. We illustrate our method using examples from
flight path planning and robotic navigation in the presence of friendly and
adversarial observers.Comment: The final version accepted by J. Comp. Math. : 41 pages, 14 figures.
Since the previous version: typos fixed, formatting improved, one mistake in
bibliography correcte
Effective approach to the problem of time: general features and examples
The effective approach to quantum dynamics allows a reformulation of the
Dirac quantization procedure for constrained systems in terms of an
infinite-dimensional constrained system of classical type. For semiclassical
approximations, the quantum constrained system can be truncated to finite size
and solved by the reduced phase space or gauge-fixing methods. In particular,
the classical feasibility of local internal times is directly generalized to
quantum systems, overcoming the main difficulties associated with the general
problem of time in the semiclassical realm. The key features of local internal
times and the procedure of patching global solutions using overlapping
intervals of local internal times are described and illustrated by two quantum
mechanical examples. Relational evolution in a given choice of internal time is
most conveniently described and interpreted in a corresponding choice of gauge
at the effective level and changing the internal clock is, therefore,
essentially achieved by a gauge transformation. This article complements the
conceptual discussion in arXiv:1009.5953.Comment: 42 pages, 9 figures; v2: streamlined discussions, more compact
manuscrip
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