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An Optimization Approach for A RFID-enabled Passport Tracking System

Published: 07 December 2016 Publication History

Abstract

RFID is an automatic object identification technology that identifies objects within a given radio frequency range through radio waves without human intervention or data entry. In the industry, the implementation of RFID was rapidly developing into different sectors such logistics and supply chain management and object tracking. Even though, this implementation faces several hurdles from different perspectives such as the collision that may occur between RFID readers and economic challenge. This work investigates the design of a RFID-enabled passport tracking system in terms of numbers of related facilities that should be established. To this aim, a multi-objective optimization model was developed. The objectives are minimizing the implementation and operational costs and RFID reader interference. To reveal Pareto solutions, two solution methods namely the ϵ-constraint method and the LP-metrics method were applied. The best solution by comparing the obtained Pareto solutions was determined using the Max-Min method. The implementation of the developed model based on a case study has proved its applicability in presenting an optimal design for the RFID-enabled passport tracking system and trade-offs among the two objectives.

References

[1]
Muller-Seitz, G., Dautzenberg, K., Creusen, U. and Stromereder, C. (2009). Customer Acceptance Of RFID Technology: Evidence From The German Electronic Retail Sector. Journal of Retailing and Consumer Services, 16 (1), 31--39.
[2]
Mohammed, A and Wang, Q. (2016). A study inintegrity of an RFID-monitoring HMSC. International Journal of Food Properties.
[3]
Mohammed, A and Wang, Q. (2017). The fuzzy multiobjective distribution planner for a green meat supply chain, Intern. Journal of Production Economics, 184, 47--58.
[4]
Nemmaluri, A., Corner, M. D. and Shenoy, P. (2008). Sherlock: Automatically Locating Objects For Humans. MobiSys, 187--198.
[5]
Mohammed, A., Wang, Q., Alyahya1, S. and Bennett, N. (2016). Design and optimization of an RFID-enabled automatedwarehousing system under uncertainties: a multi-criterion fuzzyprogramming approach. Int J Adv Manuf Technol, DOI 10.1007/s00170-016-9792-9.
[6]
Karippacheril, T. G., Diaz Rios, L. and Srivastava, L. (2011). Global Markets, Global Challenges: Improving Food Safety And Traceability While Empowering Smallholders Through ICT, In book: ICT in Agriculture Sourcebook: Connecting Smallholders to Knowledge, Networks and Institutions, Ch 12, World Bank, 285--308.
[7]
H. Chen, Y. Zhu, K. Hu, and T. Ku, "RFID network planning using a multi-swarm optimizer," Journal of Network and Computer Applications, vol. 34, pp. 888--901, May 2011.
[8]
S. Kardasa, S. Celika, M. Yildiza, and A. Levib, "PUF-enhanced offline RFID security and privacy," Journal of Network and Computer Applications, vol. 35, pp. 2059--2067, November 2012.
[9]
Mysore, N., P. Nenavat, R. S. Unnithan, R. Mulukutla, and S. Rao. 2009. "An Efficient Algorithm for RFID Reader Positioning for Coverage of Irregularly-shaped Areas." Proceedings of IEEE International Conference on Automation Science and Engineering, Bangalore, 233--240.
[10]
Ma, L., K. Hu, Y. Zhu, and H. Chen. 2014. "Cooperative Artificial Bee Colony Algorithm for Multi-objective RFID Network Planning." Journal of Network and Computer Applications 42: 143--162.
[11]
Ehrgott, M. (2005). Multicriteria Optimization. 2nd ed., Springer, New York.
[12]
Al-e-hashem, M. S. M. J., Malekly, H., Aryanezhad, M. B. (2011). A Multi-Objective Robust Optimization Model For Multi-Product Multi-Site Aggregate Production Planning In A Supply Chain Under Uncertainty. International Journal of Production Economics, 134 (1), 28--42.

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  • (2018)A trasilient decision making tool for vendor selection: a hybrid-MCDM algorithmManagement Decision10.1108/MD-04-2018-0478Online publication date: 5-Dec-2018

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ICCMA '16: Proceedings of the 4th International Conference on Control, Mechatronics and Automation
December 2016
195 pages
ISBN:9781450352130
DOI:10.1145/3029610
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 December 2016

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Author Tags

  1. Design
  2. Multi-objective optimization
  3. RFID
  4. Tracking system

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  • (2018)A trasilient decision making tool for vendor selection: a hybrid-MCDM algorithmManagement Decision10.1108/MD-04-2018-0478Online publication date: 5-Dec-2018

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