Papers by Nasim Nezamoddini
International Journal of Computer and Information Engineering, Apr 2, 2021
Sophisticated numerical simulations like finite element analysis (FEA) involve a complicated proc... more Sophisticated numerical simulations like finite element analysis (FEA) involve a complicated process from model setup to post-processing tasks that require replication of time-consuming steps. Utilizing FEA automation program simplifies the complexity of the involved steps while minimizing human errors in analysis set up, calculations, and results processing. One of the main challenges in designing FEA automation programs is to identify user requirements and link them to possible design alternatives. This paper presents a decision-making framework to design a Python based FEA automation program for modal analysis, frequency response analysis, and random vibration fatigue (RVF) analysis procedures. Analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) are applied to evaluate design alternatives considering the feedback received from experts and program users.
Expert Systems with Applications, 2015
This research proposes a new reliable network design methodology that is based on a pattern minin... more This research proposes a new reliable network design methodology that is based on a pattern mining guided genetic algorithm (GA). The proposed method can be applied for a variety of applications including telecommunication, ad hoc, and power systems. In these networks, failures in certain parts of a network make it necessary for other parts to tolerate a higher traffic load in order to maintain adequate network connections. In addition, path changes due to dynamic routing of traffic can cause a time delay of communications in the network. To understand and reduce the connection failures costs, vigorous investigations are required to select the best design option under budget constraints. Given that many options for network topology and reliability allocation exist, a GA guided with pattern mining is proposed as an effective optimization method to design reliable network while considering link and node failures. Experimental designs under various assumptions have concluded that the guided GA approach is effective in identifying a network solution within a short period of time.
Smart Cities, 2022
The world is moving toward a new connected world in which millions of intelligent processing devi... more The world is moving toward a new connected world in which millions of intelligent processing devices communicate with each other to provide services in transportation, telecommunication, and power grids in the future’s smart cities. Distributed computing is considered one of the efficient platforms for processing and management of massive amounts of data collected by smart devices. This can be implemented by utilizing multi-agent systems (MASs) with multiple autonomous computational entities by memory and computation capabilities and the possibility of message-passing between them. These systems provide a dynamic and self-adaptive platform for managing distributed large-scale systems, such as the Internet-of-Things (IoTs). Despite, the potential applicability of MASs in smart cities, very few practical systems have been deployed using agent-oriented systems. This research surveys the existing techniques presented in the literature that can be utilized for implementing adaptive multi...
International Journal of Production Economics, 2019
Modern supply chains are complicated networks that stretch over different geographical locations,... more Modern supply chains are complicated networks that stretch over different geographical locations, which make them vulnerable to a variety of risks. An effective supply chain management provides a competitive advantage by reducing overhead costs and delays in product deliveries. To cope with internal and external risks, an integrated planning scheme can effectively adjust supply chain network operations and minimize negative effects of unexpected changes and failures. This paper proposes a risk-based optimization framework that handles supply chain's strategic, tactical, and operational decisions. A supply chain is considered as a network of suppliers, manufacturing plants, distribution centers, and markets. The proposed model deals with uncertainties associated with demands, facility interruptions, lead times, and failures in supply, production, and distribution channels. To study more realistic supply chain operations, delays uncertainties are also included in the model. The structural design, communication between different centers, and inventory decisions are determined based on the risk perspective of the decision maker. To solve the proposed model, a new genetic algorithm is designed which is integrated with artificial neural network that learns from previous plans and search for better ones by minimizing any mismatch between supply and demand. The effectiveness of the proposed framework is investigated by comparing its results with those obtained from traditional techniques and regular GA. The results show that including adjustable tactical plans and incorporating learning mechanism considerably reduces inventory level and increases the profit level in supply chain systems.
Uploads
Papers by Nasim Nezamoddini