Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

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)