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Please use this identifier to cite or link to this item: http://hdl.handle.net/11129/4164

Title: Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing
Authors: Hashemipour, Majid
Azizi, Aydin
Eastern Mediterranean University, Faculty of Engineering, Dept. of Mechanical Engineering
Keywords: Mechanical Engineering
Manufacturing Industry-Artificial Intelligence
Computer integrated manufacturing systems
Production engineering-Automation
Expert systems (Computer science)-Artificial intelligence
Manufacturing Industry
Flexible Manufacturing system (FMS)
Radio Frequency Identification (RFID)
RFID Network Planning (RNP)
Artificial Intelligence
Artificial Neural Networks (ANN)
Hybrid Artificial Intelligence Algorithm
Redundant Antenna Elimination (RAE)
Probabilistic Logic Neural Networks (RPLNN)
Genetic Algorithm (GA)
Issue Date: Dec-2016
Publisher: Eastern Mediterranean University EMU
Citation: Azizi, Aydin. (2016). Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing. Thesis (Ph.D.), Eastern Mediterranean University, Institute of Graduate Studies and Research, Dept. of Mechanical Engineering, Famagusta: North Cyprus.
Abstract: Recent advances in technology and modern manufacturing industry have created a great need to model the behavior of manufacturing systems. Nowadays this need with the developments in computer technology and software engineering can be addressed by modern computational techniques. Artificial intelligence (AI) is one of the well-known advanced computational techniques which is growing fast, and have been utilized to model, control and optimize different disciplines of engineering, which manufacturing industry is no exception. Obtaining real time information has a great value in different fields of manufacturing industry such as flexible manufacturing systems, inventory management and supply chain management. One of the developing technology which has been utilized to identify and track parts and objects in manufacturing industry is Radio Frequency Identification (RFID) system. An RFID system has been made of three major components namely tags which mounted at the parts needed to be track, antenna to read tags and computer as a middle ware. Several challenges have been resulted due to adopting RFID in manufacturing industry environment. One of these challenges which has been research area of many scientists is known as RFID Network Planning (RNP) problem. Mainly RNP deals with calculating number of antennas which should be deployed in the RFID network to achieve full coverage of the tags which are needed to be read. A number of different optimization techniques have been used to optimize RNP, but many of them are complex and inefficient. The ultimate goal of this thesis is to present and evaluate iv a way of modelling and optimizing nonlinear RNP problem utilizing artificial intelligence techniques. The research developed uses Artificial Neural Network models (ANN) to bind together the computational artificial intelligence algorithm with knowledge representation an efficient artificial intelligence paradigm to model and optimize RFID networks. This effort has led to proposing a novel artificial intelligence algorithm which has been named hybrid artificial intelligence optimization technique to perform optimization of RNP as a hard learning problem. This hybrid optimization technique has been made of two different optimization phases. First phase is optimizing RNP by Redundant Antenna Elimination (RAE) algorithm and the second phase which completes RNP optimization process is Ring Probabilistic Logic Neural Networks (RPLNN). The proposed hybrid paradigm has been explored using a flexible manufacturing system (FMS) located in Eastern Mediterranean University laboratory (EMU- CIM lab) and the results are compared with well-known evolutionary optimization technique namely Genetic Algorithm (GA) to demonstrate the feasibility of the proposed architecture successfully. Keywords: Manufacturing Industry; Flexible Manufacturing system (FMS); Radio Frequency Identification (RFID); RFID Network Planning (RNP); Artificial Intelligence; Artificial Neural Networks (ANN); Hybrid Artificial Intelligence Algorithm; Redundant Antenna Elimination (RAE); Probabilistic Logic Neural Networks (RPLNN); Genetic Algorithm (GA)
ÖZ: Teknoloji ve modern üretim sektöründe ki son gelişmeler üretim sistemlerinin davranışını modellemek için büyük bir ihtiyaç yarattık. Günümüzde bilgisayar teknolojisi ve yazılım mühendisliği gelişmeler bu ihtiyacı modern hesaplama teknikleri ile ele almaktadır. Gerçek zamanlı bilgi edinme gibi esnek üretim sistemlerinin, envanter yönetimi ve tedarik zinciri yönetimi gibi imalat sanayinin farklı alanlarında büyük bir değeri vardır. Gelişen teknoloji biri Radyo Frekansı ile Tanımlama (RFID) sistemi imalat sanayi parçaları ve nesneleri tanımlamak ve izlemek için kullanılmıştır. Bir RFID sistemi, uç ana bileşenden (etiket, anten, bilgisayar) biri olarak kullanılmistir. RFIDnın ımalat sanayinde kullanımı çeşıtli zorluklar yaratmıştır. Bu zorluklardan biri RFID Ağ Planlama (RNP) sorunu olarak bilinir. RNP tam kapsama sağlamak için bulunması gereken anten sayısını hesaplar. RNP optimize etmek için farklı optimizasyon teknikleri kullanılır, ancak çoğu karmaşık ve verimsizdir. Bu tezin amacı modelleme ve yapay zeka teknikleri kullanarak doğrusal olmayan RNP problemini optimize etmektir. Burada geliştirilen araştırma Yapay Sinir Ağı modelleri (ANN) bilgi gösterimi ile hesaplama yaparak yapay zeka algoritmasını modellemek ve RFID ağlarını optimize etmek için kullanılmıştır. Bu çaba sert öğrenme problemi olarak RNP optimizasyonu gerçekleştirmek için hibrid yapay zeka optimizasyon tekniği seçildi yeni bir yapay zeka algoritması öneren yol açmıştır. Bu melez optimizasyon tekniği iki farklı optimizasyon aşamadan yapılmıştır. İlk aşama Yedek Anten Eliminasyon (RAE) algoritması ve RNP vi optimizasyon işlemini tamamlar ikinci faz RNP optimize ediyor Halka Probabilistik Mantık Sinir Ağları (RPLNN) 'dir. iyi bilinen evrimsel optimizasyon teknikleri ile önerilen melez paradigma Doğu Akdeniz Üniversitesi laboratuvarında (EPB CIM lab) bulunan bir esnek üretim sistemi (FMS) kullanılarak araştırılmıştır ve sonuçlar karşılaştırılmıştır yani Genetik Algoritma (GA) başarıyla önerilen mimarinin uygulanabilirliğini göstermek için. Anahtar Kelimeler: Üretim endüstrisi; Esnek İmalat sistemi (FMS); Radyo Frekansı Tanımlama (RFID); RFID Şebeke Planlaması (RNP); Yapay zeka; Yapay Sinir Ağları (ANN); Hibrid Yapay Zeka Algoritması; Yedekli Anten Yokedilmesi (RAE); Olasılıksal Mantıksal Sinir Ağı (RPLNN); Genetik Algoritma (GA)
Description: Doctor of Philosophy in Mechanical Engineering. Thesis (Ph.D.)--Eastern Mediterranean University, Faculty of Engineering, Dept. of Mechanical Engineering, 2016. Supervisor: Prof. Dr. Majid Hashemipour
URI: http://hdl.handle.net/11129/4164
Appears in Collections:Theses (Master's and Ph.D) – Mechanical Engineering

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