An adaptive social-aware device-to-device communication mechanism for wireless networks

Device-to-Device (D2D) communication is an essential element in 5G networks, which enables users to communicate either directly without network assistance or with minimum signaling through a base station. For an effective D2D communication, related problems in mode and peer selection need to be addressed. In mode selection, the problem is how to guarantee selection always chooses the best available mode. In peer selection, the problem is how to select optimum peers among surrounding peers in terms of connection conditions and social relationships between peers. The main objectives of this research are to identify mode selection between devices and establishing a connection with the best D2D pair connection without privacy leakage. Multi-Attribute Decision Making and Social Choice theories are applied to achieve the objectives. Mode selection scheme is based on Received Signal Strength (RSS), delay and bandwidth attributes to choose and switch among the available modes intelligently based on the highest ranking. Then, the peering selection scheme is proposed using RSS, delay, bandwidth and power attribute to find an optimum peer with concerning social relationship, by evaluating trust level between peers and excluding the untrusted peers from ranking which will increase the optimum quality of D2D connection. The proposed schemes are validated and tested using MATLAB. Two main scenarios, namely crowded network and user speed were considered to evaluate the proposed mechanism with three existing approaches where the selection is based on a single attribute. The obtained results showed that the proposed mechanism outperforms other approaches in terms of delay, signal to noise ratio, delivery ratio and throughput with better performance up to 70%. The proposed mechanism provides a smooth switching between different modes and employs an automatic peering selection with trusted peers only. It can be applied in different types of network that serves the massive number of users with different movement speed of the user

Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding

Optimization is considered as a decision-making process for getting the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing objectives that need to be optimized simultaneously, while respecting some constraints or involving selection among feasible discrete alternatives. In this Reprint of the Special Issue, 19 research papers co-authored by 88 researchers from 14 different countries explore aspects of multi-objective or multi-attribute modeling and optimization in crisp or uncertain environments by suggesting multiple-attribute decision-making (MADM) and multi-objective decision-making (MODM) approaches. The papers elaborate upon the approaches of state-of-the-art case studies in selected areas of applications related to sustainable development decision aiding in engineering and management, including construction, transportation, infrastructure development, production, and organization management

TOPSIS-RTCID for range target-based criteria and interval data

[EN] The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is receiving considerable attention as an essential decision analysis technique and becoming a leading method. This paper describes a new version of TOPSIS with interval data and capability to deal with all types of criteria. An improved structure of the TOPSIS is presented to deal with high uncertainty in engineering and engineering decision-making. The proposed Range Target-based Criteria and Interval Data model of TOPSIS (TOPSIS-RTCID) achieves the core contribution in decision making theories through a distinct normalization formula for cost and benefits criteria in scale of point and range target-based values. It is important to notice a very interesting property of the proposed normalization formula being opposite to the usual one. This property can explain why the rank reversal problem is limited. The applicability of the proposed TOPSIS-RTCID method is examined with several empirical litreture’s examples with comparisons, sensitivity analysis, and simulation. The authors have developed a new tool with more efficient, reliable and robust outcomes compared to that from other available tools. The complexity of an engineering design decision problem can be resolved through the development of a well-structured decision making method with multiple attributes. Various decision approches developed for engineering design have neglected elements that should have been taken into account. Through this study, engineering design problems can be resolved with greater reliability and confidence.Jahan, A.; Yazdani, M.; Edwards, K. (2021). TOPSIS-RTCID for range target-based criteria and interval data. International Journal of Production Management and Engineering. 9(1):1-14. https://doi.org/10.4995/ijpme.2021.13323OJS11491Ahn, B.S. (2017). 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An Approach to Test Consistency and Generate Weights from Grey Pairwise Matrices in Grey Analytical Hierarchy Process. Journal of Grey System, 25.Behzadian, M., Otaghsara, S.K., Yazdani, M., Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39, 13051-13069. https://doi.org/10.1016/j.eswa.2012.05.056Cables, E., Lamata, M.T., Verdegay, J.L. (2018). FRIM-Fuzzy Reference Ideal Method in Multicriteria Decision Making. In Collan, M. & Kacprzyk, J. (Eds.) Soft Computing Applications for Group Decision-making and Consensus Modeling. Cham, Springer International Publishing. https://doi.org/10.1007/978-3-319-60207-3_19Çakır, S. (2016). An integrated approach to machine selection problem using fuzzy SMART-fuzzy weighted axiomatic design. Journal of Intelligent Manufacturing, 1-13. https://doi.org/10.1007/s10845-015-1189-3Celen, A. (2014). Comparative analysis of normalization procedures in TOPSIS method: with an application to Turkish deposit banking market. Informatica, 25, 185-208. https://doi.org/10.15388/Informatica.2014.10Celik, E., Erdogan, M., Gumus, A. (2016). An extended fuzzy TOPSIS-GRA method based on different separation measures for green logistics service provider selection. International Journal of Environmental Science and Technology, 13, 1377-1392. https://doi.org/10.1007/s13762-016-0977-4Dymova, L., Sevastjanov, P., Tikhonenko, A. (2013). A direct interval extension of TOPSIS method. Expert Systems With Applications, 40, 4841-4847. https://doi.org/10.1016/j.eswa.2013.02.022Garca-Cascales, M.S., Lamata, M.T. (2012). On rank reversal and TOPSIS method. Mathematical and Computer Modelling, 56, 123-132. https://doi.org/10.1016/j.mcm.2011.12.022Hafezalkotob, A., Hafezalkotob, A. (2015). Comprehensive MULTIMOORA method with target-based attributes and integrated significant coefficients for materials selection in biomedical applications. Materials & Design, 87, 949-959. https://doi.org/10.1016/j.matdes.2015.08.087Hafezalkotob, A., Hafezalkotob, A. (2016). Interval MULTIMOORA method with target values of attributes based on interval distance and preference degree: biomaterials selection. Journal of Industrial Engineering International, 13, 181-198. https://doi.org/10.1007/s40092-016-0176-4Hafezalkotob, A., Hafezalkotob, A. (2017). Interval target-based VIKOR method supported on interval distance and preference degree for machine selection. Engineering Applications of Artificial Intelligence, 57, 184-196. https://doi.org/10.1016/j.engappai.2016.10.018Hafezalkotob, A., Hafezalkotob, A., Sayadi, M.K. (2016). Extension of MULTIMOORA method with interval numbers: An application in materials selection. Applied Mathematical Modelling, 40, 1372-1386. https://doi.org/10.1016/j.apm.2015.07.019Hajiagha, S.H.R., Hashemi, S.S., Zavadskas, E.K., Akrami, H. (2012). Extensions of LINMAP model for multi criteria decision making with grey numbers. Technological and Economic Development of Economy, 18, 636-650. https://doi.org/10.3846/20294913.2012.740518Hazelrigg, G.A. (2003). Validation of engineering design alternative selection methods. Engineering Optimization, 35, 103-120. https://doi.org/10.1080/0305215031000097059Hu, J., Du, Y., Mo, H., Wei, D., Deng, Y. (2016). A modified weighted TOPSIS to identify influential nodes in complex networks. Physica A: Statistical Mechanics and its Applications, 444, 73-85. https://doi.org/10.1016/j.physa.2015.09.028Huang, Y., Jiang, W. (2018). Extension of TOPSIS Method and its Application in Investment. Arabian Journal for Science and Engineering, 43, 693-705. https://doi.org/10.1007/s13369-017-2736-3Jahan, A. (2018). Developing WASPAS-RTB method for range target-based criteria: toward selection for robust design. Technological and Economic Development of Economy, 24, 1362-1387. https://doi.org/10.3846/20294913.2017.1295288Jahan, A., Bahraminasab, M., Edwards, K.L. (2012). A target-based normalization technique for materials selection. Materials & Design, 35, 647-654. https://doi.org/10.1016/j.matdes.2011.09.005Jahan, A., Edwards, K.L. (2013). VIKOR method for material selection problems with interval numbers and target-based criteria. Materials & Design, 47, 759-765. https://doi.org/10.1016/j.matdes.2012.12.072Jahan, A., Edwards, K.L. (2015). A state-of-the-art survey on the influence of normalization techniques in ranking: Improving the materials selection process in engineering design. Materials & Design, 65, 335-342. https://doi.org/10.1016/j.matdes.2014.09.022Jahan, A., Edwards, K.L., Bahraminasab, M. (2016). Multi-criteria decision analysis for supporting the selection of engineering materials in product design, Oxford, Butterworth-Heinemann.Jahan, A., Mustapha, F., Ismail, M.Y., Sapuan, S.M., Bahraminasab, M. (2011). A comprehensive VIKOR method for material selection. Materials & Design, 32, 1215-1221. https://doi.org/10.1016/j.matdes.2010.10.015Jahan, A., Zavadskas, E.K. (2018). ELECTRE-IDAT for design decision-making problems with interval data and target-based criteria. Soft Computing, 23, 129-143. https://doi.org/10.1007/s00500-018-3501-6Jahanshahloo, G.R., Hosseinzadeh Lotfi, F., Davoodi, A.R. (2009). Extension of TOPSIS for decision-making problems with interval data: Interval efficiency. Mathematical and Computer Modelling, 49, 1137-1142. https://doi.org/10.1016/j.mcm.2008.07.009Jahanshahloo, G.R., Lotfi, F.H., Izadikhah, M. (2006). An algorithmic method to extend TOPSIS for decision-making problems with interval data. Applied Mathematics and Computation, 175, 1375-1384. https://doi.org/10.1016/j.amc.2005.08.048Kasirian, M., Yusuff, R. (2013). An integration of a hybrid modified TOPSIS with a PGP model for the supplier selection with interdependent criteria. International Journal of Production Research, 51, 1037-1054. https://doi.org/10.1080/00207543.2012.663107Kuo, T. (2017). A modified TOPSIS with a different ranking index. European Journal of Operational Research, 260, 152-160. https://doi.org/10.1016/j.ejor.2016.11.052Liang, D., Xu, Z. (2017). The new extension of TOPSIS method for multiple criteria decision making with hesitant Pythagorean fuzzy sets. Applied Soft Computing, 60, 167-179. https://doi.org/10.1016/j.asoc.2017.06.034Liao, H., Wu, X. (2019). DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making. Omega, 94. 102058. https://doi.org/10.1016/j.omega.2019.04.001Liu, H.C., You, J.X., Zhen, L., Fan, X.J. (2014). A novel hybrid multiple criteria decision making model for material selection with targetbased criteria. Materials & Design, 60, 380-390. https://doi.org/10.1016/j.matdes.2014.03.071Maghsoodi, A.I., Maghsoodi, A.I., Poursoltan, P., Antucheviciene, J., Turskis, Z. (2019). Dam construction material selection by implementing the integrated SWARA-CODAS approach with target-based attributes. Archives of Civil and Mechanical Engineering, 19, 1194-1210. https://doi.org/10.1016/j.acme.2019.06.010Milani, A.S., Shanian, A., Madoliat, R., Nemes, J.A. (2005). The effect of normalization norms in multiple attribute decision making models: a case study in gear material selection. Structural and Multidisciplinary Optimization, 29, 312-318. https://doi.org/10.1007/s00158-004-0473-1Peldschus, F. (2009). The analysis of the quality of the results obtained with the methods of multi-criteria decisions. 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Metaheuristic Algorithms for Spatial Multi-Objective Decision Making

Spatial decision making is an everyday activity, common to individuals and organizations. However, recently there is an increasing interest in the importance of spatial decision-making systems, as more decision-makers with concerns about sustainability, social, economic, environmental, land use planning, and transportation issues discover the benefits of geographical information. Many spatial decision problems are regarded as optimization problems, which involve a large set of feasible alternatives, multiple conflicting objectives that are difficult and complex to solve. Hence, Multi-Objective Optimization methods (MOO)—metaheuristic algorithms integrated with Geographical Information Systems (GIS) are appealing to be powerful tools in these regards, yet their implementation in spatial context is still challenging. In this thesis, various metaheuristic algorithms are adopted and improved to solve complex spatial problems. Disaster management and urban planning are used as case studies of this thesis.These case studies are explored in the four papers that are part of this thesis. In paper I, four metaheuristic algorithms have been implemented on the same spatial multi-objective problem—evacuation planning, to investigate their performance and potential. The findings show that all tested algorithms were effective in solving the problem, although in general, some had higher performance, while others showed the potential of being flexible to be modified to fit better to the problem. In the same context, paper II identified the effectiveness of the Multi-objective Artificial Bee Colony (MOABC) algorithm when improved to solve the evacuation problem. In paper III, we proposed a multi-objective optimization approach for urban evacuation planning that considered three spatial objectives which were optimized using an improved Multi-Objective Cuckoo Search algorithm (MOCS). Both improved algorithms (MOABC and MOCS) proved to be efficient in solving evacuation planning when compared to their standard version and other algorithms. Moreover, Paper IV proposed an urban land-use allocation model that involved three spatial objectives and proposed an improved Non-dominated Sorting Biogeography-based Optimization algorithm (NSBBO) to solve the problem efficiently and effectively.Overall, the work in this thesis demonstrates that different metaheuristic algorithms have the potential to change the way spatial decision problems are structured and can improve the transparency and facilitate decision-makers to map solutions and interactively modify decision preferences through trade-offs between multiple objectives. Moreover, the obtained results can be used in a systematic way to develop policy recommendations. From the perspective of GIS - Multi-Criteria Decision Making (MCDM) research, the thesis contributes to spatial optimization modelling and extended knowledge on the application of metaheuristic algorithms. The insights from this thesis could also benefit the development and practical implementation of other Artificial Intelligence (AI) techniques to enhance the capabilities of GIS for tackling complex spatial multi-objective decision problems in the future