2011: Vol. 35, Núm. 2
http://hdl.handle.net/2099/13225
Sun, 06 Oct 2024 03:46:47 GMT2024-10-06T03:46:47ZIterative beam search for simple assembly line balancing with a fixed number of work stations
http://hdl.handle.net/2099/13296
Iterative beam search for simple assembly line balancing with a fixed number of work stations
Blum, Christian
The simple assembly line balancing problem (SALBP) concern
s the assignment of tasks with
pre-defined processing times to work stations that are arran
ged in a line. Hereby, precedence
constraints between the tasks must be respected. The optimi
zation goal of the SALBP-2 variant
of the problem concerns the minimization of the so-called cy
cle time, that is, the time in which the
tasks of each work station must be completed. In this work we p
ropose to tackle this problem with
an iterative search method based on beam search. The propose
d algorithm is able to generate
optimal solutions, respectively the best upper bounds, for
283 out of 302 test cases. Moreover,
for 9 further test cases the algorithm is able to improve the c
urrently best upper bounds. These
numbers indicate that the proposed iterative beam search al
gorithm is currently a state-of-the-art
method for the SALBP-2
Thu, 09 May 2013 14:55:29 GMThttp://hdl.handle.net/2099/132962013-05-09T14:55:29ZBlum, ChristianThe simple assembly line balancing problem (SALBP) concern
s the assignment of tasks with
pre-defined processing times to work stations that are arran
ged in a line. Hereby, precedence
constraints between the tasks must be respected. The optimi
zation goal of the SALBP-2 variant
of the problem concerns the minimization of the so-called cy
cle time, that is, the time in which the
tasks of each work station must be completed. In this work we p
ropose to tackle this problem with
an iterative search method based on beam search. The propose
d algorithm is able to generate
optimal solutions, respectively the best upper bounds, for
283 out of 302 test cases. Moreover,
for 9 further test cases the algorithm is able to improve the c
urrently best upper bounds. These
numbers indicate that the proposed iterative beam search al
gorithm is currently a state-of-the-art
method for the SALBP-2Diagnostic measures for linear mixed measurement error models
http://hdl.handle.net/2099/13277
Diagnostic measures for linear mixed measurement error models
Zare, Karim; Rasekh, Abdolrahman
In this paper, we present case deletion and mean shift outlie
r models for linear mixed measure-
ment error models using the corrected likelihood of Nakamur
a (1990). We derive the corrected
score test statistic for outliers detection based on mean sh
ift outlier models. Furthermore, several
case deletion diagnostics are constructed as a tool for influ
ence diagnostics. It is found that they
can be written in terms of studentized residuals of model, er
ror contrast matrix and the inverse of
the response variable covariance matrix. Our influence diag
nostics are illustrated through a real
data set.
Fri, 03 May 2013 16:40:23 GMThttp://hdl.handle.net/2099/132772013-05-03T16:40:23ZZare, KarimRasekh, AbdolrahmanIn this paper, we present case deletion and mean shift outlie
r models for linear mixed measure-
ment error models using the corrected likelihood of Nakamur
a (1990). We derive the corrected
score test statistic for outliers detection based on mean sh
ift outlier models. Furthermore, several
case deletion diagnostics are constructed as a tool for influ
ence diagnostics. It is found that they
can be written in terms of studentized residuals of model, er
ror contrast matrix and the inverse of
the response variable covariance matrix. Our influence diag
nostics are illustrated through a real
data set.Stress-strength reliability of Weibull distribution based on progressively censored samples
http://hdl.handle.net/2099/13276
Stress-strength reliability of Weibull distribution based on progressively censored samples
Asgharzadeh, Akbar; Valiollahi, Reza; Raqab, Mohammad Z.
Based on progressively Type-II censored samples, this pape
r deals with inference for the stress-
strength reliability
R
=
P
(
Y
<
X
) when
X
and
Y
are two independent Weibull distributions with
different scale parameters, but having the same shape param
eter. The maximum likelihood esti-
mator, and the approximate maximum likelihood estimator of
R
are obtained. Different confidence
intervals are presented. The Bayes estimator of
R
and the corresponding credible interval using
the Gibbs sampling technique are also proposed. Further, we
consider the estimation of
R
when
the same shape parameter is known. The results for exponenti
al and Rayleigh distributions can
be obtained as special cases with different scale parameter
s. Analysis of a real data set as well a
Monte Carlo simulation have been presented for illustrativ
e purposes.
Fri, 03 May 2013 16:39:05 GMThttp://hdl.handle.net/2099/132762013-05-03T16:39:05ZAsgharzadeh, AkbarValiollahi, RezaRaqab, Mohammad Z.Based on progressively Type-II censored samples, this pape
r deals with inference for the stress-
strength reliability
R
=
P
(
Y
<
X
) when
X
and
Y
are two independent Weibull distributions with
different scale parameters, but having the same shape param
eter. The maximum likelihood esti-
mator, and the approximate maximum likelihood estimator of
R
are obtained. Different confidence
intervals are presented. The Bayes estimator of
R
and the corresponding credible interval using
the Gibbs sampling technique are also proposed. Further, we
consider the estimation of
R
when
the same shape parameter is known. The results for exponenti
al and Rayleigh distributions can
be obtained as special cases with different scale parameter
s. Analysis of a real data set as well a
Monte Carlo simulation have been presented for illustrativ
e purposes.Generalized spatio-temporal models
http://hdl.handle.net/2099/13275
Generalized spatio-temporal models
Cepeda Cuervo, Edilberto
An important problem in statistics is the study of spatio-te
mporal data taking into account the
effect of explanatory variables such as latitude, longitud
e and time. In this paper, a new Bayesian
approach for analyzing spatial longitudinal data is propos
ed. It takes into account linear time
regression structures on the mean and linear regression str
uctures on the variance-covariance
matrix of normal observations. The spatial structure is inc
luded in the time regression parameters
and also in the regression structure of the variance covaria
nce matrix. Initially, we present a
summary of the spatial models and the Bayesian methodology u
sed to fit the models, as a
extension of the longitudinal data analysis. Next, the gene
ral spatial temporal model is proposed.
Finally, this proposal is used to study rainfall data
Fri, 03 May 2013 15:47:55 GMThttp://hdl.handle.net/2099/132752013-05-03T15:47:55ZCepeda Cuervo, EdilbertoAn important problem in statistics is the study of spatio-te
mporal data taking into account the
effect of explanatory variables such as latitude, longitud
e and time. In this paper, a new Bayesian
approach for analyzing spatial longitudinal data is propos
ed. It takes into account linear time
regression structures on the mean and linear regression str
uctures on the variance-covariance
matrix of normal observations. The spatial structure is inc
luded in the time regression parameters
and also in the regression structure of the variance covaria
nce matrix. Initially, we present a
summary of the spatial models and the Bayesian methodology u
sed to fit the models, as a
extension of the longitudinal data analysis. Next, the gene
ral spatial temporal model is proposed.
Finally, this proposal is used to study rainfall dataA simulation study on some confidence intervals for the population standard deviation
http://hdl.handle.net/2099/13228
A simulation study on some confidence intervals for the population standard deviation
Abu-Shawiesh, Moustafa Omar Ahmed; Banik, Shipra; Golam Kibria, B. M. Golam Kibria
In this paper a robust estimator against outliers along with
some other existing interval estimators
are considered for estimating the population standard devi
ation. An extensive simulation study has
been conducted to compare and evaluate the performance of th
e interval estimators. The exact
and the proposed robust method are easy to calculate and are n
ot overly computer-intensive. It
appears that the proposed robust method is performing bette
r than other confidence intervals for
estimating the population standard deviation, specificall
y in the presence of outliers and/or data
are from a skewed distribution. Some real-life examples are
considered to illustrate the application
of the proposed confidence intervals, which also supported t
he simulation study to some extent
Tue, 23 Apr 2013 18:28:56 GMThttp://hdl.handle.net/2099/132282013-04-23T18:28:56ZAbu-Shawiesh, Moustafa Omar AhmedBanik, ShipraGolam Kibria, B. M. Golam KibriaIn this paper a robust estimator against outliers along with
some other existing interval estimators
are considered for estimating the population standard devi
ation. An extensive simulation study has
been conducted to compare and evaluate the performance of th
e interval estimators. The exact
and the proposed robust method are easy to calculate and are n
ot overly computer-intensive. It
appears that the proposed robust method is performing bette
r than other confidence intervals for
estimating the population standard deviation, specificall
y in the presence of outliers and/or data
are from a skewed distribution. Some real-life examples are
considered to illustrate the application
of the proposed confidence intervals, which also supported t
he simulation study to some extent