Abstract: The Colombian government has defined an e-government strategy to take advantage of Information Technologies (IT) in order to contribute to the building of a more efficient, transparent and participative State that provides better services to citizens and businesses. In this regard, the Justice sector is one of the government sectors where IT has generated more expectation considering that the country has a judicial processes backlog. This situation has led to the search for alternative forms of access to justice that speed up the process while providing a low cost for citizens. To this end, the Colombian government has authorized the use of Alternative Dispute Resolution methods (ADR), a remedy where disputes can be resolved more quickly compared to judicial processes while facilitating greater communication between the parties, without recourse to judicial authority. One of these methods is conciliation, which includes a special modality that takes advantage of IT for the development of itself known as virtual conciliation. With this option the conciliation is supported by information systems, applications or platforms and communications are provided through it. This paper evaluates the level of maturity in how the service of virtual conciliation is under the framework of this strategy. This evaluation is carried out considering Shahkooh's 5-phase model for e-government. As a result, it is evident that in the context of conciliation, maturity does not reach the necessary level in the model so that it can be considered as virtual conciliation; therefore, it is necessary to define strategies to maximize the potential of IT in this context.
Abstract: The magnetohydrodynamic (MHD) Falkner-Skan
equations appear in study of laminar boundary layers flow over
a wedge in presence of a transverse magnetic field. The partial
differential equations of boundary layer problems in presence of
a transverse magnetic field are reduced to MHD Falkner-Skan
equation by similarity solution methods. This is a nonlinear ordinary
differential equation. In this paper, we solve this equation via
spectral collocation method based on Bessel functions of the first
kind. In this approach, we reduce the solution of the nonlinear
MHD Falkner-Skan equation to a solution of a nonlinear algebraic
equations system. Then, the resulting system is solved by Newton
method. We discuss obtained solution by studying the behavior
of boundary layer flow in terms of skin friction, velocity, various
amounts of magnetic field and angle of wedge. Finally, the results
are compared with other methods mentioned in literature. We can
conclude that the presented method has better accuracy than others.
Abstract: Ant algorithms are well-known metaheuristics which
have been widely used since two decades. In most of the literature,
an ant is a constructive heuristic able to build a solution from scratch.
However, other types of ant algorithms have recently emerged: the
discussion is thus not limited by the common framework of the
constructive ant algorithms. Generally, at each generation of an ant
algorithm, each ant builds a solution step by step by adding an
element to it. Each choice is based on the greedy force (also called the
visibility, the short term profit or the heuristic information) and the
trail system (central memory which collects historical information of
the search process). Usually, all the ants of the population have the
same characteristics and behaviors. In contrast in this paper, a new
type of ant metaheuristic is proposed, namely SMART (for Solution
Methods with Ants Running by Types). It relies on the use of different
population of ants, where each population has its own personality.
Abstract: This paper reviews the model-based qualitative and
quantitative Operations Management research in the context of
Construction Supply Chain Management (CSCM). Construction
industry has been traditionally blamed for low productivity, cost and
time overruns, waste, high fragmentation and adversarial
relationships. The construction industry has been slower than other
industries to employ the Supply Chain Management (SCM) concept
and develop models that support the decision-making and planning.
However the last decade there is a distinct shift from a project-based
to a supply-based approach of construction management. CSCM
comes up as a new promising management tool of construction
operations and improves the performance of construction projects in
terms of cost, time and quality. Modeling the Construction Supply
Chain (CSC) offers the means to reap the benefits of SCM, make
informed decisions and gain competitive advantage. Different
modeling approaches and methodologies have been applied in the
multi-disciplinary and heterogeneous research field of CSCM. The
literature review reveals that a considerable percentage of the CSC
modeling research accommodates conceptual or process models
which present general management frameworks and do not relate to
acknowledged soft Operations Research methods. We particularly
focus on the model-based quantitative research and categorize the
CSCM models depending on their scope, objectives, modeling
approach, solution methods and software used. Although over the last
few years there has been clearly an increase of research papers on
quantitative CSC models, we identify that the relevant literature is
very fragmented with limited applications of simulation,
mathematical programming and simulation-based optimization. Most
applications are project-specific or study only parts of the supply
system. Thus, some complex interdependencies within construction
are neglected and the implementation of the integrated supply chain
management is hindered. We conclude this paper by giving future
research directions and emphasizing the need to develop optimization
models for integrated CSCM. We stress that CSC modeling needs a
multi-dimensional, system-wide and long-term perspective. Finally,
prior applications of SCM to other industries have to be taken into
account in order to model CSCs, but not without translating the
generic concepts to the context of construction industry.
Abstract: A case study of the generation scheduling optimization
of the multi-hydroplants on the Yuan River Basin in China is reported
in this paper. Concerning the uncertainty of the inflows, the
long/mid-term generation scheduling (LMTGS) problem is solved by
a stochastic model in which the inflows are considered as stochastic
variables. For the short-term generation scheduling (STGS) problem, a
constraint violation priority is defined in case not all constraints are
satisfied. Provided the stage-wise separable condition and low
dimensions, the hydroplant-based operational region schedules
(HBORS) problem is solved by dynamic programming (DP). The
coordination of LMTGS and STGS is presented as well. The
feasibility and the effectiveness of the models and solution methods
are verified by the numerical results.
Abstract: The problem of lot sizing, sequencing and scheduling
multiple products in flow line production systems has been studied
by several authors. Almost all of the researches in this area assumed
that setup times and costs are sequence –independent even though
sequence dependent setups are common in practice. In this paper we
present a new mixed integer non linear program (MINLP) and a
heuristic method to solve the problem in sequence dependent case.
Furthermore, a genetic algorithm has been developed which applies
this constructive heuristic to generate initial population. These two
proposed solution methods are compared on randomly generated
problems. Computational results show a clear superiority of our
proposed GA for majority of the test problems.
Abstract: An effort to develop a unit commitment approach
capable of handling large power systems consisting of both thermal
and hydro generating units offers a large profitable return. In order to
be feasible, the method to be developed must be flexible, efficient
and reliable. In this paper, various proposed methods have been
described along with their strengths and weaknesses. As all of these
methods have some sort of weaknesses, a comprehensive algorithm
that combines the strengths of different methods and overcomes each
other-s weaknesses would be a suitable approach for solving
industry-grade unit commitment problem.
Abstract: This paper explores the effectiveness of machine
learning techniques in detecting firms that issue fraudulent financial
statements (FFS) and deals with the identification of factors
associated to FFS. To this end, a number of experiments have been
conducted using representative learning algorithms, which were
trained using a data set of 164 fraud and non-fraud Greek firms in the
recent period 2001-2002. The decision of which particular method to
choose is a complicated problem. A good alternative to choosing
only one method is to create a hybrid forecasting system
incorporating a number of possible solution methods as components
(an ensemble of classifiers). For this purpose, we have implemented
a hybrid decision support system that combines the representative
algorithms using a stacking variant methodology and achieves better
performance than any examined simple and ensemble method. To
sum up, this study indicates that the investigation of financial
information can be used in the identification of FFS and underline the
importance of financial ratios.
Abstract: The vehicle routing problem (VRP) is a famous combinatorial optimization problem. Because of its well-known difficulty, metaheuristics are the most appropriate methods to tackle large and realistic instances. The goal of this paper is to highlight the key ideas for designing VRP metaheuristics according to the following criteria: efficiency, speed, robustness, and ability to take advantage of the problem structure. Such elements can obviously be used to build solution methods for other combinatorial optimization problems, at least in the deterministic field.