Dear users,
This is our new website
(we are launching the new one in order to improve our communication and provide better services to the editors and authors. So we will upload all data soon).
Please click here to visit our current website, and also to submit your paper:
www.ijsom.com
Thanks for your patience during relocation.
Feel free to contact us via info@ijsom.com and ijsom.info@gmail.com
|
|
|
|
|
|
|
 |
Search published articles |
 |
|
Showing 2 results for Akbari
Mitra Darvish, Mehdi Seifabrghy, Mohammad Ali Saniei Monfared, Fatemeh Akbari,
Volume 1, Issue 1 (5-2014)
Abstract
This paper explains a model for analyzing and measuring the propagation of order amplifications (i.e. bullwhip effect) for a single-product supply network topology considering exogenous uncertainty and linear and time-invariant inventory management policies for network entities. The stream of orders placed by each entity of the network is characterized assuming customer demand is ergodic. In fact, we propose an exact formula in order to measure the bullwhip effect in the addressed supply network topology considering the system in Markovian chain framework and presenting a matrix of network member relationships and relevant order sequences. The formula turns out using a mathematical method called frequency domain analysis. The major contribution of this paper is analyzing the bullwhip effect considering exogenous uncertainty in supply networks and using the Fourier transform in order to simplify the relevant calculations. We present a number of numerical examples to assess the analytical results accuracy in quantifying the bullwhip effect.
Ellips Masehian, Vahid Eghbal Akhlaghi, Hossein Akbaripour, Davoud Sedighizadeh,
Volume 2, Issue 1 (5-2015)
Abstract
Regarding the large number of developed Particle Swarm Optimization (PSO) algorithms and the various applications for which PSO has been used, selecting the most suitable variant of PSO for solving a particular optimization problem is a challenge for most researchers. In this paper, using a comprehensive survey and taxonomy on different types of PSO, an Expert System (ES) is designed to identify the most proper PSO for solving different optimization problems. Algorithms are classified according to aspects like particle, variable, process, and swarm. After integrating different acquirable information and forming the knowledge base of the ES consisting 100 rules, the system is able to logically evaluate all the algorithms and report the most appropriate PSO-based approach based on interactions with users, referral to knowledge base and necessary inferences via user interface. In order to examine the validity and efficiency of the system, a comparison is made between the system outputs against the algorithms proposed by newly published articles. The result of this comparison showed that the proposed ES can be considered as a proper tool for finding an appropriate PSO variant that matches the application under consideration.
|
|
|
|
|
|
|
|
|
