Design and optimization of an RFID-enabled passport tracking system
Author(s) -
Abdulsalam Dukyil,
Ahmed Mohammed,
Mohamed Darwish
Publication year - 2017
Publication title -
journal of computational design and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.764
H-Index - 24
eISSN - 2288-5048
pISSN - 2288-4300
DOI - 10.1016/j.jcde.2017.06.002
Subject(s) - traceability , key (lock) , computer science , relation (database) , fuzzy logic , operations research , risk analysis (engineering) , engineering , computer security , medicine , software engineering , database , artificial intelligence
The implementation of RFID technology has been subject to ever-increasing popularity in relation to the traceability of products as one of the most cutting edge technologies. Implementing such a technology leads to an increase in the visibility management of products. Notwithstanding this, RFID communication performance is potentially affected by interference between the RFID devices. It is also subject to additional costs in investment that should be taken into account. Consequently, seeking a cost-effective design with a desired communication performance for RFID-enabled systems has become a key factor in order to be competitive in today’s markets. This study presents a cost and performance-effective design for a proposed RFID-enabled passport tracking system through the development of a multi-objective model that takes in account economic, performance and social criteria. The developed model is aimed at solving the design problem by (i) allocating the optimal numbers of related facilities that should be established and (ii) obtaining trade-offs among three objectives: minimising implementation and operational costs; minimising RFID reader interference; and maximising the social impact measured in the number of created jobs. To come closer to real design in terms of considering the uncertain parameters, the developed multi-objective model was developed in terms of a fuzzy multi-objective model (FMOM). To solve the fuzzy multi-objective optimization problem, two solution methods were used and a decision-making method was employed to select the final trade-off solution. A case study was applied to examine the applicability of the developed model and the proposed solution methods
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