51,618 research outputs found
Power-Aware Logical Topology Design Heuristics in Wavelength-Routing Networks
AbstractâWavelength-Routing (WR) networks are the most common solution for core networks. With the access segment moving from copper to Passive Optical Networks (PON), core networks will become one of the major culprits of Internet power consumption. However, WR networks offer some design flexibility which can be exploited to mitigate their energy requirements. One of the main steps which has to be faced in designing WR networks is the planning of the Logical Topology (LT) starting from the matrix of traffic requests. In this paper, we propose a Mixed Integer Linear Programming (MILP) formulation to find power-wise optimal LTs. In addition, due to the complexity of the MILP approach we propose a greedy heuristic and a genetic algorithm (GA) ensuring performance close to the one achieved by the MILP formulation. I
Optimality In Reserve Selection Algorithms: When Does It Matter And How Much?
This paper responds to recent criticisms in Biological Conservation of heuristic reserve selection algorithms. These criticisms primarily concern the fact that heuristic algorithms cannot guarantee an optimal solution to the problem of representing a group of targeted natural features in a subset of the sites in a region. We discuss optimality in the context of a range of needs for conservation planning. We point out that classical integer linear programming methods that guarantee an optimal solution, like branch and bound algorithms, are currently intractable for many realistic problems. We also show that heuristics have practical advantages over classical methods and that suboptimality is not necessarily a disadvantage for many real-world applications. Further work on alternative reserve selection algorithms is certainly needed, but the necessary criteria for assessing their utility must be broader than mathematical optimality
Re-scheduling in railways: the rolling stock balancing problem
This paper addresses the Rolling Stock Balancing Problem (RSBP). This problem arises at a passenger railway operator when the rolling stock has to be re-scheduled due to changing circumstances. These problems arise both in the planning process and during operations. The RSBP has as input a timetable and a rolling stock schedule where the allocation of the rolling stock among the stations does not fit to the allocation before and after the planning period. The problem is then to correct these off-balances, leading to a modified schedule that can be implemented in practice.For practical usage of solution approaches for the RSBP, it is important to solve the problem quickly. Therefore, the focus is on heuristic approaches. In this paper, we describe two heuristics and compare them with each other on some (variants of) real-life instances of NS, the main Dutch passenger railway operator. Finally, to get some insight in the quality of the proposed heuristics, we also compare their outcomes with optimal solutions obtained by solving existing rolling stock circulation models.heuristics;railway planning;integer linear programming;rolling stock re-scheduling
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A review of portfolio planning: Models and systems
In this chapter, we first provide an overview of a number of portfolio planning models
which have been proposed and investigated over the last forty years. We revisit the
mean-variance (M-V) model of Markowitz and the construction of the risk-return
efficient frontier. A piecewise linear approximation of the problem through a
reformulation involving diagonalisation of the quadratic form into a variable
separable function is also considered. A few other models, such as, the Mean
Absolute Deviation (MAD), the Weighted Goal Programming (WGP) and the
Minimax (MM) model which use alternative metrics for risk are also introduced,
compared and contrasted. Recently asymmetric measures of risk have gained in
importance; we consider a generic representation and a number of alternative
symmetric and asymmetric measures of risk which find use in the evaluation of
portfolios. There are a number of modelling and computational considerations which
have been introduced into practical portfolio planning problems. These include: (a)
buy-in thresholds for assets, (b) restriction on the number of assets (cardinality
constraints), (c) transaction roundlot restrictions. Practical portfolio models may also
include (d) dedication of cashflow streams, and, (e) immunization which involves
duration matching and convexity constraints. The modelling issues in respect of these
features are discussed. Many of these features lead to discrete restrictions involving
zero-one and general integer variables which make the resulting model a quadratic
mixed-integer programming model (QMIP). The QMIP is a NP-hard problem; the
algorithms and solution methods for this class of problems are also discussed. The
issues of preparing the analytic data (financial datamarts) for this family of portfolio
planning problems are examined. We finally present computational results which
provide some indication of the state-of-the-art in the solution of portfolio optimisation
problems
A Dynamic Programming Approach to Adaptive Fractionation
We conduct a theoretical study of various solution methods for the adaptive
fractionation problem. The two messages of this paper are: (i) dynamic
programming (DP) is a useful framework for adaptive radiation therapy,
particularly adaptive fractionation, because it allows us to assess how close
to optimal different methods are, and (ii) heuristic methods proposed in this
paper are near-optimal, and therefore, can be used to evaluate the best
possible benefit of using an adaptive fraction size.
The essence of adaptive fractionation is to increase the fraction size when
the tumor and organ-at-risk (OAR) are far apart (a "favorable" anatomy) and to
decrease the fraction size when they are close together. Given that a fixed
prescribed dose must be delivered to the tumor over the course of the
treatment, such an approach results in a lower cumulative dose to the OAR when
compared to that resulting from standard fractionation. We first establish a
benchmark by using the DP algorithm to solve the problem exactly. In this case,
we characterize the structure of an optimal policy, which provides guidance for
our choice of heuristics. We develop two intuitive, numerically near-optimal
heuristic policies, which could be used for more complex, high-dimensional
problems. Furthermore, one of the heuristics requires only a statistic of the
motion probability distribution, making it a reasonable method for use in a
realistic setting. Numerically, we find that the amount of decrease in dose to
the OAR can vary significantly (5 - 85%) depending on the amount of motion in
the anatomy, the number of fractions, and the range of fraction sizes allowed.
In general, the decrease in dose to the OAR is more pronounced when: (i) we
have a high probability of large tumor-OAR distances, (ii) we use many
fractions (as in a hyper-fractionated setting), and (iii) we allow large daily
fraction size deviations.Comment: 17 pages, 4 figures, 1 tabl
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