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Showing 3 results for Hasani

Mr Aliakbar Hasani, Mr Seyed Hessameddin Zegordi,
Volume 26, Issue 1 (IJIEPR 2015)
Abstract

In this study, an optimization model is proposed to design a Global Supply Chain (GSC) for a medical device manufacturer under disruption in the presence of pre-existing competitors and price inelasticity of demand. Therefore, static competition between the distributors’ facilities to more efficiently gain a further share in market of Economic Cooperation Organization trade agreement (ECOTA) is considered. This competition condition is affected by disruption occurrence. The aim of the proposed model is to maximize the expected net after-tax profit of GSC under disruption and normal situation at the same time. To effectively deal with disruption, some practical strategies are adopted in the design of GSC network. The uncertainty of the business environment is modeled using the robust optimization technique based on the concept of uncertainty budget. To tackle the proposed Mixed-Integer Nonlinear Programming (MINLP) model, a hybrid Taguchi-based Memetic Algorithm (MA) with an adaptive population size is developed that incorporates a customized Adaptive Large Neighborhood Search (ALNS) as its local search heuristic. A fitness landscape analysis is used to improve the systematic procedure of neighborhood selection in the proposed ALNS. A numerical example and computational results illustrate the efficiency of the proposed model and algorithm in dealing with global disruptions under uncertainty and competition pressure.
Mahdi Karbasian, Ali Eghbali Babadi, Fatemeh Hasani,
Volume 28, Issue 2 (IJIEPR 2017)
Abstract

Abstract

The reliability and safety of any system is the most important qualitative characteristic of a system. This qualitative characteristic is of particular importance in systems whose functions are under various stresses, such as high temperature, high speed, high pressure, etc. A considerable point, which is rarely taken into account when calculating the reliability and safety of systems, is the presence of dependency among subsystems, and this dependency causes various failures in a system, one of the most important of which is the common cause failure (CCF). Failing to consider common cause failures in the calculation of system reliabilities, leads to optimistic estimations of system reliability rates, which results in too much trust in the system. In this paper, first we deal with identifying the reliability of the input of a dynamic positioning system consisting of different environmental sensors and various positioning systems with the aid of PBS and FFBD techniques. Then, we will calculate and allocate the above-mentioned reliability with the aid of a RBD. The common cause failures of different subsystems were considered in calculating the reliability of the previously mentioned system, with the aid of IEC 61508 standard, and then the degree of the effectiveness of common cause failures on the reliability of the studied system, was obtained. Finally, by considering different assumptions for the system under study, it was proved that the less the amount of the reliability of dependent components is, the higher the effectiveness of common cause failures on the system reliability will be


Aliakbar Hasani,
Volume 28, Issue 2 (IJIEPR 2017)
Abstract

In this paper, a comprehensive mathematical model for designing an electric power supply chain network via considering preventive maintenance under risk of network failures is proposed. The risk of capacity disruption of the distribution network is handled via using a two-stage stochastic programming as a framework for modeling the optimization problem. An applied method of planning for the network design and power generation and transmission system via considering failures scenarios, as well as network preventive maintenance schedule, is presented. The aim of the proposed model is to minimize the expected total cost consisting of power plants set-up, power generation and the maintenance activities. The proposed mathematical model is solved by an efficient new accelerated Benders decomposition algorithm. The proposed accelerated Benders decomposition algorithm uses an efficient acceleration mechanism based on the priority method which uses a heuristic algorithm to efficiently cope with computational complexities. A large number of considered scenarios are reduced via using a k-means clustering algorithm to decrease the computational effort for solving the proposed two-stage stochastic programming model. The efficiencies of the proposed model and solution algorithm are examined using data from the Tehran Regional Electric Company. The obtained results indicate that solutions of the stochastic programming are more robust than the obtained solutions provided by a deterministic model.



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