2007
3
4
4
70
1

An optimization technique for vendor selection with quantity discounts using Genetic Algorithm
http://jiei.azad.ac.ir/article_511084.html
1
Vendor selection decisions are complicated by the fact that various conflicting multiobjective factors must be considered in the decision making process. The problem of vendor selection becomes still more complicated with the inclusion of incremental discount pricing schedule. Such hard combinatorial problems when solved using meta heuristics produce near optimal solutions. This paper proposes a multicomponent multiple vendor selection model with vendors offering quantity discounts. This problem is then evaluated using Genetic Algorithm with a case study approach. Combinatorial approach is used to group the vendors for selection and Genetic Algorithm to allocate the optimal order quantities for each vendor.
0

1
13


N
Arunkumar
Assistant Professor, Dep. of Mechanical Engineering, St. Joseph's College of Engineering, Chennai 600 119, India
Iran


L
Karunamoorthy
Professor, Head of the Central Workshop Division, Dep. of Mechanical Eng., Anna University,
Chennai 600 025, India
Iran


N
Uma Makeshwaraa
Graduate Research Assistant, Dep. of Mechanical Engineering, University of Texas at Austin
Iran
Vendor selection
Multiobjective
Combinatorial
Genetic Algorithm
Quantity discounts
1

Evaluating quantitative stock selection strategies in Tehran Stock Exchange
http://jiei.azad.ac.ir/article_511085.html
1
There are different strategies for selecting stocks, and different investors use different strategies according to their risk tolerance or their expected rate of return. In this study, the profitability of a broad range of stock selection strategies in Tehran Stock Exchange over the period 13701383, has been examined, and it has been investigated whether the successful strategies in other countries are also successful in Iran or not. Although a lot of comprehensive studies have been done in the developed and in a considerable number of emerging markets, and successful strategies have been well documented in those countries, such studies have never been done in Tehran Stock Exchange. The sample is all the companies in Tehran Stock Exchange in the aforementioned period. Also, in order to evaluate different strategies, various portfolios have been formed for each year according to each strategy. Then, computing the return of winner portfolios, those strategies generating the maximum return in excess of market return, are presented. The evaluation of the performance of the strategies has been done regarding various diagnostics criteria like risk and return. The results show that value strategy is the most successful strategy in Iran and generates significant excess return, in contrast to growth, size, price momentum and fundamental strategies. In other words, the most successful strategy in Iran is the multivariate strategy which selects the stocks with high E/P, B/P, C/P, S/P and D/E. Moreover, as apposed to the developed markets and a considerable number of emerging markets, size and momentum strategies are not profitable ones in Tehran Stock Exchange and can not distinguish between profitable and unprofitable stocks.
0

14
23


A.M
Kimiagari
Assistant Professor, Dep. of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Iran


S
Amini
M.Sc., Dep. of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Iran
Value strategy
Growth strategy
Momentum strategy
Size strategy
Stock Selection
Tehran Stock Exchange
1

Two optimal algorithms for finding bidirectional shortest path design problem in a block layout
http://jiei.azad.ac.ir/article_511086.html
1
In this paper, Shortest Path Design Problem (SPDP) in which the path is incident to all cells is considered. The bidirectional path is one of the known types of configuration of networks for Automated Guided Vehicles (AGV).To solve this problem, two algorithms are developed. For each algorithm an Integer Linear Programming (ILP) is determined. The objective functions of both algorithms are to find the shortest path. The path must be connected and incident to all cells at least in one edge or node. A simple BranchandCut approach is used to solve the ILP models. Computational results show that the models easily can solve the problem with less than 45 cells using a commercial ILP solver.
0

24
34


M
Hamzeei
M.Sc., Dep. of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Iran


R
Zanjirani Farahani
Assistant Professor, Dep. of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Iran
AGV
Block layout
Bidirectional path
Integer Linear Programming, BranchandCut
1

Advanced operations research techniques for multiconstraint QoS routing in internet
http://jiei.azad.ac.ir/article_511087.html
1
Internet Traffic has grown exponentially over last few years due to provision of multiple class services through Internet backbone. With the explosive use of Internet, contemporary Internet routers are susceptible to overloads and their services deteriorate drastically and often cause denial of services. In this paper, an analysis is made how forecasting technique, routing algorithm and Genetic algorithm can be simultaneously applied for solving a multiconstrained routing problem in Quality of service (QoS) traffic in Internet. Also, a model is suggested for solving the abovementioned problem. Simulation results show that the throughput of the given network is enhanced by implementing the model. It can also be seen that the average delay of packets flowing through the network comes down when the proposed model is employed for the network.
0

35
43


HK
Arunkumar
Dep. of Mechanical Engineering, College of Engineering, TVPM, India
Iran


S
Sivakumar
Professor, Dep. of Mechanical Engineering, College of Engineering, TVPM, India
Iran
Qos
Multiconstrained routing
Congestion control
Genetic Algorithm
1

Multistart simulated annealing for dynamic plant layout problem
http://jiei.azad.ac.ir/article_511088.html
1
In today’s dynamic market, organizations must be adaptive to market fluctuations. In addition, studies show that materialhandling cost makes up between 20 and 50 percent of the total operating cost. Therefore, this paper considers the problem of arranging and rearranging, when there are changes in product mix and demand, manufacturing facilities such that the sum of material handling and rearrangement costs is minimized. This problem is called the dynamic plant layout problem (DPLP). In this paper, the authors develop a multistart simulated annealing for DPLP. To compare the performance of metaheuristics, data sets taken from literature are used in the comparison.
0

44
50


B
Ashtiani
Ph.D. Candidate, Dep. of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Iran


M.B
Aryanezhad
Professor, Dep. of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Iran


B
Farhang Moghaddam
Dep. of Industrial Engineering, Islamic Azad University, Parand Branch, Tehran, Iran
Iran
Dynamic layout
Simulated Annealing
Cooling schedule
Multistart simulated annealing
1

The trim loss concentration in onedimensional cutting stock problem (1DCSP) by defining a virtual cost
http://jiei.azad.ac.ir/article_511089.html
1
Nowadays, OneDimensional Cutting Stock Problem (1DCSP) is used in many industrial processes and recently has been considered as one of the most important research topic. In this paper, a metaheuristic algorithm based on the Simulated Annealing (SA) method is represented to minimize the trim loss and also to focus the trim loss on the minimum number of large objects. In this method, the 1DCSP is taken into account as Itemoriented and the authors have tried to minimize the trim loss concentration by using the simulated annealing algorithm and also defining a virtual cost for the trim loss of each stock. The solved sample problems show the ability of this algorithm to solve the 1DCSP in many cases.
0

51
58


H
Javanshir
Assistant Professor, Dep. of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
Iran


M
Shadalooee
M.Sc., Dep. of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
Iran
Onedimensional cutting stock problem
Simulated Annealing
Trim loss concentration
Itemoriented
FDD algorithm
Virtual cost
1

On the multivariate variation control chart
http://jiei.azad.ac.ir/article_511090.html
1
Multivariate control charts such as Hotelling`s T^ 2 and X^ 2 are commonly used for monitoring several related quality characteristics. These control charts use correlation structure that exists between quality characteristics in an attempt to improve monitoring. The purpose of this article is to discuss some issues related to the G chart proposed by Levinson et al. [9] for detecting shifts in the process variancecovariance matrix. They use a G statistic which is distributed as a chisquare with p(p+1) / 2 degrees of freedom where p denotes the number of variables under study. The authors show through simulation that the chisquare distribution only holds for certain cases. The results could be important to practitioners who use G chart for monitoring purposes.
0

59
66


R
Noorossana
Professor, Dep. of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Iran


S.M
Seyedaliakbar
Assistant Prof., Dep. of Industrial Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
Iran
Statistical Process Control
T2 chart
χ2 chart
G chart
Goodness of fit test
1

A new approach to determine efficient DMUs in DEA models using inverse optimization
http://jiei.azad.ac.ir/article_511091.html
1
This paper proposes a new approach for determining efficient DMUs in DEA models using inverse optimization and without solving any LPs. It is shown that how a twophase algorithm can be applied to detect efficient DMUs. It is important to compare computational performance of solving the simultaneous linear equations with that of the LP, when computational issues and complexity analysis are at focus.
0

67
70


GH.R
Amin
Assistant Professor of OR, Dep. of Computer Science, Postgraduate Engineering Centre, Islamic Azad University,
Tehran South Branch, Tehran, Iran
Iran
Data Envelopment Analysis (DEA)
Decision Making Units (DMUs)
Inverse optimization
Ellipsoid algorithm