Abstract
The onset of Industry 4.0 requires for development of self-aware systems. The core of such systems are computationally powerful yet energy efficient embedded modules, called computer on module (COM), capable of performing various tasks of control, data acquisition and signal processing. The market is flooded with various COM systems thus making the selection of the most appropriate one for appropriate task is a very difficult task. This decision problem is addressed by employing PROMETHEE and DEXi methods. The proposed solution is a decision model based on 13 attributes. The evaluation is performed on a set of 53 currently available COMs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The Yocto Project is a Linux Foundation Collaborative open source Project whose goal is to produce tools and processes that enable the creation of Linux distributions for embedded software that are independent of the underlying architecture of the embedded hardware.
- 2.
S+R stands for System and Support.
References
Gerber, A.: Choosing the best hardware for your next IoT project, May 2017. https://www.ibm.com/developerworks/library/iot-lp101-best-hardware-devices-iot-project/iot-lp101-best-hardware-devices-iot-project-pdf.pdf. Accessed 14 Feb 2018
Maksimović, M., Vujović, V., Davidović, N., Milošević, V., Perišić, B.: Raspberry Pi as internet of things hardware: performances and constraints. Des. Issues 3, 8 (2014)
Polianytsia, A., Starkova, O., Herasymenko, K.: Survey of hardware IoT platforms. In: 2016 Third International Scientific-Practical Conference on Problems of Infocommunications Science and Technology (PIC ST), pp. 152–153. IEEE (2016)
Pitchipoo, P., Venkumar, P., Rajakarunakaran, S.: Modeling and development of a decision support system for supplier selection in the process industry. J. Ind. Eng. Int. 9, 23 (2013)
Chan, F.T.S., Chan, H.K., Ip, R.W.L., Lau, H.C.W.: A decision support system for supplier selection in the airline industry. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 221, 741–758 (2007)
Dweiri, F., Kumar, S., Khan, S.A., Jain, V.: Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Syst. Appl. 62(Suppl C), 273–283 (2016)
Kar, A.K.: Revisiting the supplier selection problem: an integrated approach for group decision support. Expert Syst. Appl. 41(6), 2762–2771 (2014)
Chen, D., Cong, J., Gurumani, S., Hwu, W., Rupnow, K., Zhang, Z.: Platform choices and design demands for IoT platforms: cost, power, and performance tradeoffs. IET Cyber-Phys. Syst.: Theory Appl. 1(1), 70–77 (2016)
Samie, F., Bauer, L., Henkel, J.: IoT technologies for embedded computing: a survey. In: Proceedings of the Eleventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, p. 8. ACM (2016)
Promethee-Gaia: Get Started with Visual PROMETHEE. http://www.promethee-gaia.net/documents.html
Bohanec, M.: DEXi: Program for Multi-Attribute Decision Making, User’s Manual, Version 5.00. IJS report DP-11897. Jožef Stefan Institute, Ljubljana (2015). http://kt.ijs.si/MarkoBohanec/DEXi/html/DEXiDoc.htm
Philips Semiconductors: I2S bus specification, June 1996
Brans, J.-P., Vincke, P., Mareschal, B.: How to select and how to rank projects: the Promethee method. Eur. J. Oper. Res. 24(2), 228–238 (1986)
Behzadian, M., Kazemzadeh, R.B., Albadvi, A., Aghdasi, M.: Promethee: a comprehensive literature review on methodologies and applications. Eur. J. Oper. Res. 200(1), 198–215 (2010)
Brans, J.-P., Mareschal, B.: Promethee methods. In: Figueira, J., Greco, S., Ehrgott, M. (eds.) Multiple Criteria Decision Analysis, State of the Art Survey. Springer Science, New York (2005). https://doi.org/10.1007/0-387-23081-5_5
Zhaoxu, S., Min, H.: Multi-criteria decision making based on Promethee method. In: 2010 International Conference on Computing, Control and Industrial Engineering (CCIE), vol. 1, pp. 416–418. IEEE (2010)
Mareschal, B., Brans, J.-P.: Geometrical representations for MCDA. Eur. J. Oper. Res. 34(1), 69–77 (1988)
Bohanec, M., Žnidaršič, M., Rajkovič, V., Bratko, I., Zupan, B.: DEX methodology: three decades of qualitative multi-attribute modeling. Informatica 37(1), 49–54 (2013)
Bohanec, M.: Decision making: a computer-science and information-technology viewpoint. Interdiscip. Descr. Complex Syst. 7(2), 22–37 (2009)
Bohanec, M.: DEXi: Program for Multi Attribute Decision Making, User’s Manual. Jožef Stefan Institute, July 2015
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Nusev, G., Boškoski, P., Bohanec, M., Mileva Boshkoska, B. (2018). A DSS Model for Selection of Computer on Module Based on PROMETHEE and DEX Methods. In: Dargam, F., Delias, P., Linden, I., Mareschal, B. (eds) Decision Support Systems VIII: Sustainable Data-Driven and Evidence-Based Decision Support. ICDSST 2018. Lecture Notes in Business Information Processing, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-319-90315-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-90315-6_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-90314-9
Online ISBN: 978-3-319-90315-6
eBook Packages: Computer ScienceComputer Science (R0)