2020-11-28T04:02:34Z
http://scientiairanica.sharif.edu/?_action=export&rf=summon&issue=280
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2010
17
2
A New Rolling-Horizon Technique for Lotsizing in a Capacitated Pure Flow Shop with Sequence-Dependent Setups
M.
Mohammadi
S. M. T.
Fatemi Ghomi
A new rolling-horizon approach is presented in this paper to solve the problem of lotsizing
in a capacitated pure
ow shop with sequence dependent setups. Two solution algorithms are provided,
based on a simplified version of the problem, combining the rolling-horizon approach with a heuristic. To
evaluate the effectiveness of the proposed algorithms, a comparison is made between the results obtained
by the proposed algorithms and those obtained by existing algorithms. The comparison indicates the
superiority of the proposed algorithm for large scale problems.
Lotsizing
Scheduling
Pure ow shop
Sequence-Dependent
Rolling-horizon
Multi-level
2010
12
01
http://scientiairanica.sharif.edu/article_3358_1abba25bc6163f0c8a905de6ed0351bf.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2010
17
2
An Exponential Cluster Validity Index for Fuzzy Clustering with Crisp and Fuzzy Data
M. H.
Fazel Zarandi
M. R.
Faraji
M.
Karbasian
This paper presents a new cluster validity index for finding a suitable number of fuzzy
clusters with crisp and fuzzy data. The new index, called the ECAS-index, contains exponential
compactness and separation measures. These measures indicate homogeneity within clusters and
heterogeneity between clusters, respectively. Moreover, a fuzzy c-mean algorithm is used for fuzzy
clustering with crisp data, and a fuzzy k-numbers clustering is used for clustering with fuzzy data. In
comparison to other indices, it is evident that the proposed index is more eective and robust under
different conditions of data sets, such as noisy environments and large data sets.
Fuzzy clustering
Cluster validity index
Fuzzy c-mean algorithm
Fuzzy k-numbers clustering
Fuzzy numbers
Compactness
separation
2010
12
01
http://scientiairanica.sharif.edu/article_3359_05096886dbe7a956b1c4ad9b2de6ab0a.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2010
17
2
A Cumulative Binomial Chart for Uni-variate Process Control
M. S.
Fallah Nezhad
M.S.
Owlia
In this paper, a control method based on binomial distribution is proposed in which, by
analyzing the cumulated data for a uni-variate quality characteristic, the possible mean shift is detected.
In this method, the domain of observations is rst divided into some specied intervals and then the number
of observations in each interval is counted. Control statistics are next dened using the counted values
based on the approximation methods. Necessary adaptations are made to form an appropriate statistic
for the process monitoring. Using a simulation technique, the performance of the proposed method is
compared with the ones of the optimal EWMA, GEWMA, CUSUM and GLR control charts. The results
show that with an equal in-control average run length, the cumulative Binomial control method performs
better than control charts in detecting a mean shift of any size less than 3. The analysis is also carried
out for autocorrelated data, showing that the proposed method performs better than other methods for
small to moderate values of autocorrelation coecients.
Statistical process control
Binomial distribution
Cumulative data
Simulation
2010
12
01
http://scientiairanica.sharif.edu/article_3360_2b1be755e96acb355b44ae247e1632a0.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2010
17
2
Optimal Ordering Policy Under Acceptance Sampling Plan and Trade Credit Financing
T-C.
Tsao
Acceptance of sampling plans and trade credit has become increasingly common in today's
business. These two issues should be considered simultaneously when determining an ordering decision.
This paper uses EOQ to model the decision under the acceptance sampling plan and trade credit; meaning,
how often it would be necessary to order to minimize the total related cost. We develop theorems based
on optimum lemmas to solve the problem. Computational analyses are given to illustrate the solution
procedures and we discuss the in
uence of credit period, acceptance sampling plan, holding cost and
ordering cost on the total cost, and the ordering decision. We conclude with a computational analysis
that leads to a variety of managerial insights.
ordering
Inventory
Acceptance sampling plan
Trade credit
optimization
2010
12
01
http://scientiairanica.sharif.edu/article_3361_e93a005b7d0569790804f8ab6526bd73.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2010
17
2
A Fuzzy Rule-Based Expert System for Diagnosing Asthma
M.
Zolnoori
M. H.
Fazel Zarandi
M.
Moin
H.
Heidarnejad
Asthma is a chronic lung disorder of which the number of suerers estimated to be
between 1.4-27.1% of the population in dierent areas of the world. Results of various studies show that
asthma is usually under-diagnosed, especially in developing countries, because of limited access to medical
specialist and laboratory data. The purpose of this paper is to design a fuzzy rule-based expert system to
alleviate this hazard by diagnosing asthma at initial stages. A knowledge representation of this system
is provided from a high level, based on patient perception, and organized into two dierent structures
called Type A and Type B. Type A is composed of six modules, including symptoms, allergic rhinitis,
genetic factors, symptom hyper-responsiveness, medical factors and environmental factors. Type B is
composed of 8 modules including symptoms, allergic rhinitis, genetic factors, response to short-term drug
use, bronchodilator tests, challenge tests, PEF tests and exhaled nitric oxide. The nal result of every
system is de-fuzzied in order to provide the assessment of the possibility of asthma for the patient.
Verication and validations criteria are considered throughout a life-cycle; the system was developed by
the participation of general physicians, experienced asthma physicians and asthmatic patients.
Fuzzy sets
Medical expert system
Asthma
Diagnosis
2010
12
01
http://scientiairanica.sharif.edu/article_3362_55e8c5d5b1f78764fd24bd2ab35afb93.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2010
17
2
Determination of the Number of Kanbans and Batch Sizes in a JIT Supply Chain System
S.K.
Chaharsooghi
A.
Sajedinejad
Under stochastic demand in the multi-stage SCM in a JIT environment, the probability
of generating a function of the stationary distributions of the backlogged demand was extended. The
batch size of WIPs has a great impact on the packing, unpacking or transferring costs of a chain; it
has been attempted to integrate the delivery batch size of each plant in a multi-stage serial chain with
the production-ordering and supplier kanbans of the chain. An algorithm was developed to evaluate the
optimal numbers of kanbans and batch sizes of each plant by minimizing the total cost of a chain. A
numerical example is also provided to indicate the signicance of adding the proposed assumptions, as
well as demonstrating the approach adopted towards solving the problem.
Supply chain
JIT
Stochastic demand
Batch size
Kanban
2010
12
01
http://scientiairanica.sharif.edu/article_3363_739258fd6616787052fcc11acde87973.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2010
17
2
Fuzzy Development of Multivariate Variable Control Charts Using the Fuzzy Likelihood Ratio Test
H.
Moheb Alizadeh
A. R.
Arshadi Khamseh
S. M. T.
Fatemi Ghomi
This paper is an eort to evolve multivariate variable control charts in a fuzzy environment
where each observation in each sample is assumed to be a canonical fuzzy number. To do this, a likelihood
ratio test should be exploited in a fuzzy environment, because multivariate variable control charts are
constructed using this test. In this way, membership functions of likelihood ratio statistics applied to
control the process mean and dispersion are obtained solving four non-linear programming problems. Using
these membership functions, membership degrees of in and out of control states of both process mean and
dispersion are computed. Hence contrary to the classic multivariate variable control charts categorizing the
process into just two states, i.e. in and out of control, the process can be considered in several intermediate
states, based on the computed membership degrees, bringing about more
exibility in process analysis.
Multivariate control charts
Likelihood ratio test
non-linear programming
Fuzzy numbers
fuzzy random variables
2010
12
01
http://scientiairanica.sharif.edu/article_3364_bfe02d2f742c0d69c75026ccdb0c6a7e.pdf