6,816 research outputs found
Comparing several heuristics for a packing problem
Packing problems are in general NP-hard, even for simple cases. Since now
there are no highly efficient algorithms available for solving packing
problems. The two-dimensional bin packing problem is about packing all given
rectangular items, into a minimum size rectangular bin, without overlapping.
The restriction is that the items cannot be rotated. The current paper is
comparing a greedy algorithm with a hybrid genetic algorithm in order to see
which technique is better for the given problem. The algorithms are tested on
different sizes data.Comment: 5 figures, 2 tables; accepted: International Journal of Advanced
Intelligence Paradigm
A greedy heuristic approach for the project scheduling with labour allocation problem
Responding to the growing need of generating a robust project scheduling, in this article we present a greedy algorithm to generate the project baseline schedule. The robustness achieved by integrating two dimensions of the human resources flexibilities. The first is the operatorsâ polyvalence, i.e. each operator has one or more secondary skill(s) beside his principal one, his mastering level being characterized by a factor we call âefficiencyâ. The second refers to the working time modulation, i.e. the workers have a flexible time-table that may vary on a daily or weekly basis respecting annualized working strategy. Moreover, the activity processing time is a non-increasing function of the number of workforce allocated to create it, also of their heterogynous working efficiencies. This modelling approach has led to a nonlinear optimization model with mixed variables. We present: the problem under study, the greedy algorithm used to solve it, and then results in comparison with those of the genetic algorithms
Understanding Algorithm Performance on an Oversubscribed Scheduling Application
The best performing algorithms for a particular oversubscribed scheduling
application, Air Force Satellite Control Network (AFSCN) scheduling, appear to
have little in common. Yet, through careful experimentation and modeling of
performance in real problem instances, we can relate characteristics of the
best algorithms to characteristics of the application. In particular, we find
that plateaus dominate the search spaces (thus favoring algorithms that make
larger changes to solutions) and that some randomization in exploration is
critical to good performance (due to the lack of gradient information on the
plateaus). Based on our explanations of algorithm performance, we develop a new
algorithm that combines characteristics of the best performers; the new
algorithms performance is better than the previous best. We show how hypothesis
driven experimentation and search modeling can both explain algorithm
performance and motivate the design of a new algorithm
Energy management in communication networks: a journey through modelling and optimization glasses
The widespread proliferation of Internet and wireless applications has
produced a significant increase of ICT energy footprint. As a response, in the
last five years, significant efforts have been undertaken to include
energy-awareness into network management. Several green networking frameworks
have been proposed by carefully managing the network routing and the power
state of network devices.
Even though approaches proposed differ based on network technologies and
sleep modes of nodes and interfaces, they all aim at tailoring the active
network resources to the varying traffic needs in order to minimize energy
consumption. From a modeling point of view, this has several commonalities with
classical network design and routing problems, even if with different
objectives and in a dynamic context.
With most researchers focused on addressing the complex and crucial
technological aspects of green networking schemes, there has been so far little
attention on understanding the modeling similarities and differences of
proposed solutions. This paper fills the gap surveying the literature with
optimization modeling glasses, following a tutorial approach that guides
through the different components of the models with a unified symbolism. A
detailed classification of the previous work based on the modeling issues
included is also proposed
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
Optimal Alignments for Designing Urban Transport Systems: Application to Seville
The achievement of some of the Sustainable Development Goals (SDGs) from the recent
2030 Agenda for Sustainable Development has drawn the attention of many countries towards
urban transport networks. Mathematical modeling constitutes an analytical tool for the formal
description of a transportation system whereby it facilitates the introduction of variables and the
definition of objectives to be optimized. One of the stages of the methodology followed in the
design of urban transit systems starts with the determination of corridors to optimize the population
covered by the system whilst taking into account the mobility patterns of potential users and the
time saved when the public network is used instead of private means of transport. Since the capture
of users occurs at stations, it seems reasonable to consider an extensive and homogeneous set of
candidate sites evaluated according to the parameters considered (such as pedestrian population
captured and destination preferences) and to select subsets of stations so that alignments can take
place. The application of optimization procedures that decide the sequence of nodes composing the
alignment can produce zigzagging corridors, which are less appropriate for the design of a single line.
The main aim of this work is to include a new criterion to avoid the zigzag effect when the alignment
is about to be determined. For this purpose, a curvature concept for polygonal lines is introduced,
and its performance is analyzed when criteria of maximizing coverage and minimizing curvature are
combined in the same design algorithm. The results show the application of the mathematical model
presented for a real case in the city of Seville in Spain.Ministerio de EconomĂa y Competitividad MTM2015-67706-
Project scheduling under uncertainty using fuzzy modelling and solving techniques
In the real world, projects are subject to numerous uncertainties at different levels of planning. Fuzzy project scheduling is one of the approaches that deal with uncertainties in project scheduling problem. In this paper, we provide a new technique that keeps uncertainty at all steps of the modelling and solving procedure by considering a fuzzy modelling of the workload inspired from the fuzzy/possibilistic approach. Based on this modelling, two project scheduling techniques, Resource Constrained Scheduling and Resource Leveling, are considered and generalized to handle fuzzy parameters. We refer to these problems as the Fuzzy Resource Constrained Project Scheduling Problem (FRCPSP) and the Fuzzy Resource Leveling Problem (FRLP). A Greedy Algorithm and a Genetic Algorithm are provided to solve FRCPSP and FRLP respectively, and are applied to civil helicopter maintenance within the framework of a French industrial project called Helimaintenance
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