Scientia Iranica
Volume:17 Issue: 2, 2010

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.

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 e ective and robust under different conditions of data sets, such as noisy environments and large data sets.

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 speci ed intervals and then the number of observations in each interval is counted. Control statistics are next de ned 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.

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 creditmeaning, 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.

Asthma is a chronic lung disorder of which the number of su erers estimated to be between 1.4-27.1% of the population in di erent 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 di erent 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-fuzzi ed in order to provide the assessment of the possibility of asthma for the patient. Veri cation and validations criteria are considered throughout a life-cyclethe system was developed by the participation of general physicians, experienced asthma physicians and asthmatic patients.

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 chainit 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 signi cance of adding the proposed assumptions, as well as demonstrating the approach adopted towards solving the problem.

This paper is an e ort 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.