2008
0
0
0
65
1

A Review and Evaluation of Statistical Process Control Methods in
Monitoring Process Mean and Variance Simultaneously
http://www.qjie.ir/article_10.html
1
In this paper, first the available single charting methods, which have been proposed to detect simultaneous shifts in a single process mean and variance, are reviewed. Then, by designing proper simulation studies these methods are evaluated in terms of incontrol and outofcontrol average run length criteria (ARL). The results of these simulation experiments show that the EWMA and EWMS methods are quite capable to detect large shifts in the means and variances. However, while the two EWMV procedures under study do not perform well, the MaxMin EWMA and MaxEWMA perform very well in all scenarios of mean and variance shifts.
0

1
8


Ahmad
Ostadsharifmemar
Department of Industrial Engineering, Sharif University of Technology
Iran
ostadsharif@mehr.sharif.edu


Seyed Taghi
Akhavan Niaki
Department of Industrial Engineering, Sharif University of Technology
Iran
niaki@sharif.edu
Control Chart
Simultaneous Control of Mean and Variance
Statistical quality control
1

Scheduling in Container Terminals using Network
Simplex Algorithm
http://www.qjie.ir/article_11.html
1
In static scheduling problem, where there is no change in situation, the challenge is that the large problems can be solved in a short time. In this paper, the Static Scheduling problem of Automated Guided Vehicles in container terminal is solved by the Network Simplex Algorithm (NSA). The algorithm is based on graph model and their performances are at least 100 times faster than traditional simplex algorithm for Linear Programs. Many random data are generated and fed to the model for 50 vehicles. The results show that NSA is fast and efficient. It is found that, in practice, NSA takes polynomial time to solve problems in this application.
0

9
16


Hassan
Rashidi
Department of Computer Engineering, Islamic Azad University of Qazvin, Qazvin, Iran
Iran
hrashi@gmail.com
Container Terminals
Minimum Cost Flow Problem
Network Simplex Algorithm
Optimization methods
Scheduling
1

Fuzzy Particle Swarm Optimization Algorithm for a Supplier Clustering
Problem
http://www.qjie.ir/article_12.html
1
This paper presents a fuzzy decisionmaking approach to deal with a clustering supplier problem in a supply chain system. During recent years, determining suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions is usually complex and unstructured. In general, many quantitative and qualitative factors, such as quality, price, and flexibility and delivery performance, must be considered to determine suitable suppliers. The aim of this study is to present a new approach using particle swarm optimization (PSO) algorithm for clustering suppliers under fuzzy environments and classifying smaller groups with similar characteristics.
Our numerical analysis indicates that the proposed PSO improves the performance of the fuzzy cmeans (FCM) algorithm.
0

17
24


esmaeil
Mehdizadeh
Islamic Azad University, Science and Research Branch Branch
Iran
emqiau@yahoo.com


reza
Tavakkoli Moghaddam
Department of Industrial Engineering, Faculty of Engineering, University of Tehran
Iran
tavakoli@ut.ac.ir
fuzzy clustering
supplier clustering problem and particle swarm optimization
1

Application of Rough Set Theory in Data Mining for Decision Support
Systems (DSSs)
http://www.qjie.ir/article_13.html
1
Decision support systems (DSSs) are prevalent information systems for decision making in
many competitive business environments. In a DSS, decision making process is intimately related to some factors which determine the quality of information systems and their related products. Traditional approaches to data analysis usually cannot be implemented in sophisticated Companies, where managers need some DSS tools for rapid decision making. In traditional approaches to decision making, usually scientific expertise together with statistical techniques are needed to support the managers. However, these approaches are not able to handle the huge amount of real data, and the processes are usually very slow. Recently, several innovative facilities have been presented for decision making process in enterprises. Presenting new techniques for development of huge databases, together with some heuristic models have enhanced the capabilities of DSSs to support managers in all levels of organizations. Today, data mining and knowledge discovery is considered as the main module of development of advanced DSSs. In this research, we use rough set theory for data mining for decision making process in a DSS. The proposed approach concentrates on individual objects rather than population of the objects. Finally, a rule extracted from a data set and the corresponding features (attributes) is considered in modeling data mining.
0

25
34


Mohammad Hossein
Fazel Zarandi
Department of Industrial Engineering, Amir kabir university of Technology, Tehran, Iran,
Iran
Zarandi@aut.ac.ir


Abolfazl
Kazemi
Department of Industrial Engineering, Amir kabir university of Technology, Tehran, Iran,
Iran
Data mining
Knowledge Discovery
Rough Set Theory
1

The preemptive resourceconstrained project scheduling problem subject
to due dates and preemption penalties: An integer programming approach
http://www.qjie.ir/article_14.html
1
Extensive research has been devoted to resource constrained project scheduling problem. However, little attention has been paid to problems where a certain time penalty must be incurred if activity preemption is allowed. In this paper, we consider the project scheduling problem of minimizing the total cost subject to resource constraints, earlinesstardiness penalties and preemption penalties, where each time an activity is started after being preempted; a constant setup penalty is incurred. We propose a solution method based on a pure integer formulation for the problem. Finally, some test problems are solved with LINGO version 8 and computational results are reported.
0

35
39


Behrouz
Afshar nadjafi
Department of Industrial Engineering, Islamic Azad University of Qazvin, Qazvin, Iran,
Iran
afsharnb@merhr.sharif.edu


Shahram
Shadrokh
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran,
Iran
shadrokh@sharid.edu
Earlinesstardiness cost
Preemptive scheduling
Project Scheduling
Resource Constrained
1

Practical common weights scalarizing function approach for efficiency
analysis
http://www.qjie.ir/article_15.html
1
A characteristic of Data Envelopment Analysis (DEA) is to allow individual decision making units (DMUs) to select the factor weights which are the most advantageous for them in calculating their efficiency scores. This flexibility in selecting the weights, on the other hand, deters the comparison among DMUs on a common base. For dealing with this difficulty and assessing all the DMUs on the same scale, this paper proposes using a multiple objective linear programming (MOLP) approach based on scalarizing function for generating common set of weights under the DEA framework. This is an advantageous of the proposed approach against general approaches in the literature which are based on multiple objective nonlinear programming.
0

41
47


Alireza
Alinezhad
Department of Industrial Engineering, Islamic Azad University of Qazvin, Qazvin, Iran
Iran
alinezhad_ir@yahoo.com


Reza
Kiani Mavi
Department of Industrial Management, Islamic Azad University of Qazvin, Qazvin, Iran
Iran
rezakianimavi@yahoo.com


Majid
Zohrehbandian
Department of Industrial Management, Islamic Azad University of Qazvin, Qazvin, Iran
Iran


Ahmad
Makui
Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
Iran
amakui@iust.ac.ir
DEA
MOLP
Scalarizing function
1

Estimation of the cost function of a twoechelon inventory system with
lost sales using regression
http://www.qjie.ir/article_25.html
1
Multiechelon inventory systems are one of the most important and attractive areas in modelling supply chain management so that several researches and studies have been done about. In this paper, the total cost function of a twoechelon inventory system with central warehouse and many identical retailers controlled by continuous review inventory policy is estimated. We have assumed the demand in the retailers experiences independent Poisson distribution and unsatisfied demands are lost in the retailers and unsatisfied retailer order is backordered in the central warehouse. The transportation time from warehouse to each retailer and the lead time of the warehouse orders are assumed to be constant. The accuracy of the estimation is assessed by simulation. The main contribution of this paper is using statistical tools in twoechelon inventory systems analysis.
0

55
64


Mehdi
Seif Barghy
Department of Industrial Engineering, Islamic Azad University of Qazvin
Iran
seifbar@yahoo.com


Maghsoud
Amiri
Department of Management & Accounting, Allameh Tabatabei University
Iran
mg_amiri@yahoo.com


mostafa
Heidari
Islamic azad university of Qazvin
Iran
mostafa.heydari@yahoo.com
Inventory
lost sales
multi echelon inventory
regression
1

Using neural network to estimate weibull parameters
http://www.qjie.ir/article_26.html
1
As is well known, estimating parameters of the treeparameter weibull distribution is a complicated task and sometimes contentious area with several methods vying for recognition. Weibull distribution involves in reliability studies frequently and has many applications in engineering. However estimating the parameters of Weibull distribution is crucial in classical ways. This distribution has three parameters, but for simplicity, a parameter is ridded off and as a result, the estimation of the others will be easily done. When the threeparameter distribution is of interest, the classical estimation procedures such as maximum likelihood estimation (MLE) will be quite boring. In this paper to take advantage of application of artificial neural networks (ANN) to statistics, we propose using a simple neural network to estimate three parameters of Weibull distribution simultaneously. Trained neural network similar to moment method estimates Weibull parameters based on mean, standard deviation, median, skewness and kurtosis of the sample accurately. To assess the power of the proposed method we carry out simulation study and compare the results of the proposed method with real values of the parameters.
0

48
54


Babak
Abbasi
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran,
Iran
Abbasi@sharif.edu


behrouz
Afshar nadjafi
Department of Industrial Engineering, Islamic Azad University of Qazvin, Qazvin, Iran,
Iran
Afsharnb@mehr.sharif.edu
Artificial neural networks
parameters estimate
tree parameter weibull