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Showing 5 results for Ramezani

R. Ramezanian, M.b. Aryanezhad , M. Heydari,
Volume 21, Issue 2 (IJIEPR 2010)
Abstract

  In this paper, we consider a flow shop scheduling problem with bypass consideration for minimizing the sum of earliness and tardiness costs. We propose a new mathematical modeling to formulate this problem. There are several constraints which are involved in our modeling such as the due date of jobs, the job ready times, the earliness and the tardiness cost of jobs, and so on. We apply adapted genetic algorithm based on bypass consideration to solve the problem. The basic parameters of this meta-heuristic are briefly discussed in this paper. Also a computational experiment is conducted to evaluate the performance of the implemented methods. The implemented algorithm can be used to solve large scale flow shop scheduling problem with bypass effectively .


Saeed Ramezani , Azizollah Memariani,
Volume 22, Issue 2 (IJIEPR 2011)
Abstract

 

  Condition Monitoring,

  Oil Analysis, Wear Behavior,

  Fuzzy Rule Based System

 

Maintenance , as a support function, plays an important role in manufacturing companies and operational organizations. In this paper, fuzzy rules used to interpret linguistic variables for determination of priorities. Using this approach, such verbal expressions, which cannot be explicitly analyzed or statistically expressed, are herein quantified and used in decision making.

In this research, it is intended to justify the importance of historic data in oil analysis for fault detection. Initial rules derived by decision trees and visualization then these fault diagnosis rules corrected by experts. With the access to decent information sources, the wear behaviors of diesel engines are studied. Also, the relation between the final status of engine and selected features in oil analysis is analyzed. The dissertation and analysis of determining effective features in condition monitoring of equipments and their contribution, is the issue that has been studied through a Data Mining model.
Taha Hosseinhejazi, Majid Ramezani, Mirmehdi Seyyed-Esfahani, Ali Mohammad Kimiagari,
Volume 24, Issue 2 (IJIEPR 2013)
Abstract

control of production processes in an industrial environment needs the correct setting of input factors, so that output products with desirable characteristics will be resulted at minimum cost. Moreover, such systems havetomeetset of qualitycharacteristicstosatisfycustomer requirements.Identifyingthemosteffectivefactorsindesignoftheprocesswhichsupportcontinuousandcontinualimprovement isrecentlydiscussedfromdifferentviewpoints.Inthisstudy, we examined the quality engineering problems in which several characteristics and factors are to be analyzed through a simultaneous equations system. Besides, the several probabilistic covariates can be included to the proposed model. The main purpose of this model is to identify interrelations among exogenous and endogenous variables, which give important insight for systematic improvements of quality. At the end, the proposed approach is described analytically by a numerical example.
Romina Madani, Amin Ramezani, Mohammad Taghi Madani Beheshti,
Volume 25, Issue 4 (IJIEPR 2014)
Abstract

Today, companies need to make use of appropriate patterns such as supply chain management system to gain and preserve a position in competitive world-wide market. Supply chain is a large scaled network consists of suppliers, manufacturers, warehouses, retailers and final customers which are in coordination with each other in order to transform products from raw materials into finished goods with optimal placement of inventory within the supply chain and minimizing operating costs in the face of demand fluctuations. Logistics is the management of the flow of goods between the point of origin and the point of consumption. One issue in Logistics management is the presence of possible long delays in goods transportation. In order to handle long delays, there are two possible solutions proposed in this paper. One solution is to use Model Predictive Controllers (MPCs) using orthonormal functions (Laguerre functions) and the other is to change supply chain model in which an integrator is imbedded. To this end, the two mentioned solutions will be implemented on a supply chain with long logistics delays and the results will be compared to classical MPC without using orthonormal basis and augmented model for different type of customer demand (constant, pulse and random demand).
Amin Saghaeeian, Reza Ramezanian,
Volume 28, Issue 4 (IJIEPR 2017)
Abstract

This study considers pricing, production and transportation decisions in a Stackelberg game between three-stage, multi-product, multi-source and single-period supply chains called leader and follower. These chains consist of; manufacturers, distribution centers (DCs) and retailers. Competition type is horizontal and SC vs. SC. The retailers in two chains try to maximize their profit through pricing of products in different markets and regarding the transportation and production costs. A bi-level nonlinear programming model is formulated in order to represent the Stackelberg game. Pricing decisions are based on discrimination pricing rules, where we can put different prices in different markets. After that the model is reduced to single-level nonlinear programming model by replacing Karush-Kuhn-Tucker conditions for the lower level (follower) problem. Finally, a numerical example is solved in order to analyze the sensitivity of effective parameters on price and profit.



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