123,800 research outputs found
A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times
Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable Routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.Peer Reviewe
An adaptive prefix-assignment technique for symmetry reduction
This paper presents a technique for symmetry reduction that adaptively
assigns a prefix of variables in a system of constraints so that the generated
prefix-assignments are pairwise nonisomorphic under the action of the symmetry
group of the system. The technique is based on McKay's canonical extension
framework [J.~Algorithms 26 (1998), no.~2, 306--324]. Among key features of the
technique are (i) adaptability---the prefix sequence can be user-prescribed and
truncated for compatibility with the group of symmetries; (ii)
parallelizability---prefix-assignments can be processed in parallel
independently of each other; (iii) versatility---the method is applicable
whenever the group of symmetries can be concisely represented as the
automorphism group of a vertex-colored graph; and (iv) implementability---the
method can be implemented relying on a canonical labeling map for
vertex-colored graphs as the only nontrivial subroutine. To demonstrate the
practical applicability of our technique, we have prepared an experimental
open-source implementation of the technique and carry out a set of experiments
that demonstrate ability to reduce symmetry on hard instances. Furthermore, we
demonstrate that the implementation effectively parallelizes to compute
clusters with multiple nodes via a message-passing interface.Comment: Updated manuscript submitted for revie
A constrained-based optimization approach for seismic data recovery problems
Random and structured noise both affect seismic data, hiding the reflections
of interest (primaries) that carry meaningful geophysical interpretation. When
the structured noise is composed of multiple reflections, its adaptive
cancellation is obtained through time-varying filtering, compensating
inaccuracies in given approximate templates. The under-determined problem can
then be formulated as a convex optimization one, providing estimates of both
filters and primaries. Within this framework, the criterion to be minimized
mainly consists of two parts: a data fidelity term and hard constraints
modeling a priori information. This formulation may avoid, or at least
facilitate, some parameter determination tasks, usually difficult to perform in
inverse problems. Not only classical constraints, such as sparsity, are
considered here, but also constraints expressed through hyperplanes, onto which
the projection is easy to compute. The latter constraints lead to improved
performance by further constraining the space of geophysically sound solutions.Comment: International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2014); Special session "Seismic Signal Processing
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