4,080 research outputs found
The Project Scheduling Problem with Non-Deterministic Activities Duration: A Literature Review
Purpose: The goal of this article is to provide an extensive literature review of the models and solution procedures proposed by many researchers interested on the Project Scheduling Problem with nondeterministic activities duration. Design/methodology/approach: This paper presents an exhaustive literature review, identifying the existing models where the activities duration were taken as uncertain or random parameters. In order to get published articles since 1996, was employed the Scopus database. The articles were selected on the basis of reviews of abstracts, methodologies, and conclusions. The results were classified according to following characteristics: year of publication, mathematical representation of the activities duration, solution techniques applied, and type of problem solved. Findings: Genetic Algorithms (GA) was pointed out as the main solution technique employed by researchers, and the Resource-Constrained Project Scheduling Problem (RCPSP) as the most studied type of problem. On the other hand, the application of new solution techniques, and the possibility of incorporating traditional methods into new PSP variants was presented as research trends. Originality/value: This literature review contents not only a descriptive analysis of the published articles but also a statistical information section in order to examine the state of the research activity carried out in relation to the Project Scheduling Problem with non-deterministic activities duration.Peer Reviewe
Project scheduling under undertainty – survey and research potentials.
The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;
Stability and resource allocation in project planning.
The majority of resource-constrained project scheduling efforts assumes perfect information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule is executed. In reality, project activities are subject to considerable uncertainty, which generally leads to numerous schedule disruptions. In this paper, we present a resource allocation model that protects a given baseline schedule against activity duration variability. A branch-and-bound algorithm is developed that solves the proposed resource allocation problem. We report on computational results obtained on a set of benchmark problems.Constraint satisfaction; Information; Model; Planning; Problems; Project management; Project planning; Project scheduling; Resource allocati; Scheduling; Stability; Uncertainty; Variability;
Spectrum sensing algorithms and software-defined radio implementation for cognitive radio system
The scarcity of spectral resources in wireless communications, due to a fixed frequency allocation policy, is a strong limitation to the increasing demand for higher data rates. However, measurements showed that a large part of frequency channels are underutilized or almost unoccupied. The cognitive radio paradigm arises as a tempting solution to the spectral congestion problem. A cognitive radio must be able to identify transmission opportunities in unused channels and to avoid generating harmful interference with the licensed primary users. Its key enabling technology is the spectrum sensing unit, whose ultimate goal consists in providing an indication whether a primary transmission is taking place in the observed channel. Such indication is determined as the result of a binary hypothesis testing experiment wherein null hypothesis (alternate hypothesis) corresponds to the absence (presence) of the primary signal. The first parts of this thesis describes the spectrum sensing problem and presents some of the best performing detection techniques. Energy Detection and multi-antenna Eigenvalue-Based Detection algorithms are considered. Important aspects are taken into account, like the impact of noise estimation or the effect of primary user traffic. The performance of each detector is assessed in terms of false alarm probability and detection probability. In most experimental research, cognitive radio techniques are deployed in software-defined radio systems, radio transceivers that allow operating parameters (like modulation type, bandwidth, output power, etc.) to be set or altered by software.In the second part of the thesis, we introduce the software-defined radio concept. Then, we focus on the implementation of Energy Detection and Eigenvalue-Based Detection algorithms: first, the used software platform, GNU Radio, is described, secondly, the implementation of a parallel energy detector and a multi-antenna eigenbased detector is illustrated and details on the used methodologies are given. Finally, we present the deployed experimental cognitive testbeds and the used radio peripherals. The obtained algorithmic results along with the software-defined radio implementation may offer a set of tools able to create a realistic cognitive radio system with real-time spectrum sensing capabilities
The Fuzzy Project Scheduling Problem with Minimal Generalized Precedence Relations
In scheduling, estimations are affected by the
imprecision of limited information on future events,
and the reduction in the number and level of detail of
activities. Overlapping of processes and activities
requires the study of their continuity, along with
analysis of the risks associated with imprecision. In this
line, this paper proposes a fuzzy heuristic model for the
Project Scheduling Problem with flows and minimal
feeding, time and work Generalized Precedence
Relations with a realistic approach to overlapping, in
which the continuity of processes and activities is
allowed in a discretionary way. This fuzzy algorithm
handles the balance of process flows, and computes the
optimal fragmentation of tasks, avoiding the
interruption of the critical path and reverse criticality.
The goodness of this approach is tested on several
problems found in the literature; furthermore, an
example of a 15-story building was used to compare
the better performance of the algorithm implemented in
Visual Basic for Applications (Excel) over that same
example input in Primavera© P6 Professional V8.2.0,
using five different scenarios.This research was supported by the FAPA program of Universidad de Los Andes, Colombia. The authors would like to thank the research group of Construction Engineering and Management (INgeco) of Universidad de Los Andes, and the five anonymous referees for their helpful and constructive suggestions.Ponz Tienda, JL.; Pellicer Armiñana, E.; Benlloch Marco, J.; Andrés Romano, C. (2015). The Fuzzy Project Scheduling Problem with Minimal Generalized Precedence Relations. Computer-Aided Civil and Infrastructure Engineering. 30(11):872-891. doi:10.1111/mice.12166S8728913011Adeli, H., & Park, H. S. (1995). Optimization of space structures by neural dynamics. Neural Networks, 8(5), 769-781. doi:10.1016/0893-6080(95)00026-vAdeli, H., & Karim, A. (1997). Scheduling/Cost Optimization and Neural Dynamics Model for Construction. Journal of Construction Engineering and Management, 123(4), 450-458. doi:10.1061/(asce)0733-9364(1997)123:4(450)Adeli, H., & Wu, M. (1998). 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Type-2 fuzzy logic system applications for power systems
PhD ThesisIn the move towards ubiquitous information & communications technology, an
opportunity for further optimisation of the power system as a whole has arisen.
Nonetheless, the fast growth of intermittent generation concurrently with markets
deregulation is driving a need for timely algorithms that can derive value from these
new data sources. Type-2 fuzzy logic systems can offer approximate solutions to
these computationally hard tasks by expressing non-linear relationships in a more
flexible fashion. This thesis explores how type-2 fuzzy logic systems can provide
solutions to two of these challenging power system problems; short-term load
forecasting and voltage control in distribution networks. On one hand, time-series
forecasting is a key input for economic secure power systems as there are many tasks
that require a precise determination of the future short-term load (e.g. unit
commitment or security assessment among others), but also when dealing with
electricity as commodity. As a consequence, short-term load forecasting becomes
essential for energy stakeholders and any inaccuracy can be directly translated into
their financial performance. All these is reflected in current power systems literature
trends where a significant number of papers cover the subject. Extending the existing
literature, this work focuses in how these should be implemented from beginning to
end to bring to light their predictive performance. Following this research direction,
this thesis introduces a novel framework to automatically design type-2 fuzzy logic
systems. On the other hand, the low-carbon economy is pushing the grid status even
closer to its operational limits. Distribution networks are becoming active systems with
power flows and voltages defined not only by load, but also by generation. As
consequence, even if it is not yet absolutely clear how power systems will evolve in
the long-term, all plausible future scenarios claim for real-time algorithms that can
provide near optimal solutions to this challenging mixed-integer non-linear problem.
Aligned with research and industry efforts, this thesis introduces a scalable
implementation to tackle this task in divide-and-conquer fashio
Applying Bayesian networks to model uncertainty in project scheduling
PhDRisk Management has become an important part of Project Management. In spite
of numerous advances in the field of Project Risk Management (PRM), handling
uncertainty in complex projects still remains a challenge. An important
component of Project Risk Management (PRM) is risk analysis, which attempts to
measure risk and its impact on different project parameters such as time, cost and
quality. By highlighting the trade-off between project parameters, the thesis
concentrates on project time management under uncertainty.
The earliest research incorporating uncertainty/risk in projects started in the late
1950’s. Since then, several techniques and tools have been introduced, and many
of them are widely used and applied throughout different industries. However,
they often fail to capture uncertainty properly and produce inaccurate, inconsistent
and unreliable results. This is evident from consistent problems of cost and
schedule overrun.
The thesis will argue that the simulation-based techniques, as the dominant and
state-of-the-art approach for modelling uncertainty in projects, suffers from
serious shortcomings. More advanced techniques are required.
Bayesian Networks (BNs), are a powerful technique for decision support under
uncertainty that have attracted a lot of attention in different fields. However,
applying BNs in project risk management is novel.
The thesis aims to show that BN modelling can improve project risk assessment.
A literature review explores the important limitations of the current practice of
project scheduling under uncertainty. A new model is proposed which applies
BNs for performing the famous Critical Path Method (CPM) calculation. The
model subsumes the benefits of CPM while adding BN capability to properly
capture different aspects of uncertainty in project scheduling
SIRU development. Volume 1: System development
A complete description of the development and initial evaluation of the Strapdown Inertial Reference Unit (SIRU) system is reported. System development documents the system mechanization with the analytic formulation for fault detection and isolation processing structure; the hardware redundancy design and the individual modularity features; the computational structure and facilities; and the initial subsystem evaluation results
XML Matchers: approaches and challenges
Schema Matching, i.e. the process of discovering semantic correspondences
between concepts adopted in different data source schemas, has been a key topic
in Database and Artificial Intelligence research areas for many years. In the
past, it was largely investigated especially for classical database models
(e.g., E/R schemas, relational databases, etc.). However, in the latest years,
the widespread adoption of XML in the most disparate application fields pushed
a growing number of researchers to design XML-specific Schema Matching
approaches, called XML Matchers, aiming at finding semantic matchings between
concepts defined in DTDs and XSDs. XML Matchers do not just take well-known
techniques originally designed for other data models and apply them on
DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical
structure of a DTD/XSD) to improve the performance of the Schema Matching
process. The design of XML Matchers is currently a well-established research
area. The main goal of this paper is to provide a detailed description and
classification of XML Matchers. We first describe to what extent the
specificities of DTDs/XSDs impact on the Schema Matching task. Then we
introduce a template, called XML Matcher Template, that describes the main
components of an XML Matcher, their role and behavior. We illustrate how each
of these components has been implemented in some popular XML Matchers. We
consider our XML Matcher Template as the baseline for objectively comparing
approaches that, at first glance, might appear as unrelated. The introduction
of this template can be useful in the design of future XML Matchers. Finally,
we analyze commercial tools implementing XML Matchers and introduce two
challenging issues strictly related to this topic, namely XML source clustering
and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure
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