Ahmed, F., & Deb, K. (2013). Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms. Soft Computing, 17(7), 1283-1299. https://doi.org/10.1007/s00500-012-0964-8
Ali, R. A., Nikolić, M., & Zahra, A. (2017). Personnel selection using group fuzzy AHP and SAW methods. Journal of Engineering Management and Competitiveness (JEMC), 7(1), 3-10 https://doi.org/10.5937/jemc1701003A
Altay, N., & Green, W. G. (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175(1), 475-493. https://doi.org/10.1016/j.ejor.2005.05.016 https://doi.org/10.1016/j.ejor.2005.05.016
[+]
Ahmed, F., & Deb, K. (2013). Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms. Soft Computing, 17(7), 1283-1299. https://doi.org/10.1007/s00500-012-0964-8
Ali, R. A., Nikolić, M., & Zahra, A. (2017). Personnel selection using group fuzzy AHP and SAW methods. Journal of Engineering Management and Competitiveness (JEMC), 7(1), 3-10 https://doi.org/10.5937/jemc1701003A
Altay, N., & Green, W. G. (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175(1), 475-493. https://doi.org/10.1016/j.ejor.2005.05.016 https://doi.org/10.1016/j.ejor.2005.05.016
Amindoust, A., Ahmed, S., Saghafinia, A., & Bahreininejad, A. (2012). Sustainable supplier selection: A ranking model based on fuzzy inference system. Applied Soft Computing, 12(6), 1668-1677. https://doi.org/10.1016/j.asoc.2012.01.023
Amiri, M., Zandieh, M., Soltani, R., & Vahdani, B. (2009). A hybrid multi-criteria decision-making model for firms competence evaluation. Expert Systems with Applications, 36(10), 12314-12322. https://doi.org/10.1016/j.eswa.2009.04.045
Arabali, A., Ghofrani, M., Etezadi-Amoli, M., Fadali, M. S., & Baghzouz, Y. (2013). Genetic-algorithmbased optimization approach for energy management. IEEE Transactions on Power Delivery, 28(1), 162-170. https://doi.org/10.1109/TPWRD.2012.2219598
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108
Benet-Martinez, V., & John, O. P. (1998). Los Cinco Grandes across cultures and ethnic groups: Multitrait-multimethod analyses of the Big Five in Spanish and English. Journal of personality and social psychology, 75(3), 729. https://doi.org/10.1037/0022-3514.75.3.729
Bhateja, A., & Kumar, S. (2014). Genetic algorithm with elitism for cryptanalysis of vigenere cipher. Paper presented at the 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT). https://doi.org/10.1109/ICICICT.2014.6781311
Boran, F. E., Genç, S., & Akay, D. (2011). Personnel selection based on intuitionistic fuzzy sets. Human Factors and Ergonomics in Manufacturing & Service Industries, 21(5), 493-503. https://doi.org/10.1002/hfm.20252
Boselie, P., Dietz, G., & Boon, C. (2005). Commonalities and contradictions in HRM and performance research. Human resource management journal, 15(3), 67-94. https://doi.org/10.1111/j.1748- 8583.2005.tb00154.x
Bukhari, A. C., Tusseyeva, I., & Kim, Y.-G. (2013). An intelligent real-time multi-vessel collision risk assessment system from VTS view point based on fuzzy inference system. Expert Systems with Applications, 40(4), 1220-1230. https://doi.org/10.1016/j.eswa.2012.08.016
Buller, P. F., & McEvoy, G. M. (2012). Strategy, human resource management and performance: Sharpening line of sight. Human resource management review, 22(1), 43-56. https://doi.org/10.1016/j.hrmr.2011.11.002
Butz, M. V., Sastry, K., & Goldberg, D. E. (2003). Tournament selection: Stable fitness pressure in XCS. Paper presented at the Genetic and Evolutionary Computation Conference, Berlin. https://doi.org/10.1007/3-540-45110-2_83
Canós, L., & Liern, V. (2008). Soft computing-based aggregation methods for human resource management. European Journal of Operational Research, 189(3), 669-681. https://doi.org/10.1016/j.ejor.2006.01.054
Carrera, D. A., & Mayorga, R. V. (2008). Supply chain management: A modular fuzzy inference system approach in supplier selection for new product development. Journal of Intelligent Manufacturing, 19(1), 1-12. https://doi.org/10.1007/s10845-007-0041-9
Combs, J., Liu, Y., Hall, A., & Ketchen, D. (2006). How much do high-performance work practices matter? A meta-analysis of their effects on organizational performance. Personnel psychology, 59(3), 501-528. https://doi.org/10.1111/j.1744-6570.2006.00045.x
Cui, W., & He, Y. (2016). Tournament selection based fruit fly optimization and its application in template matching. Paper presented at the Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 2016 IEEE.
Daş, G. S., & Göçken, T. (2014). A fuzzy approach for the reviewer assignment problem. Computers & Industrial Engineering, 72, 50-57. https://doi.org/10.1016/j.cie.2014.02.014
Day, R. C., & Hamblin, R. L. (1964). Some effects of close and punitive styles of supervision. American Journal of Sociology, 69(5), 499-510. https://doi.org/10.1086/223653
De Feo, G., & De Gisi, S. (2010). Using an innovative criteria weighting tool for stakeholders involvement to rank MSW facility sites with the AHP. Waste Management, 30(11), 2370-2382. https://doi.org/10.1016/j.wasman.2010.04.010
De Jong, K. A. (1975). Analysis of the behavior of a class of genetic adaptive systems.
Driss, I., Mouss, K. N., & Laggoun, A. (2015). A new genetic algorithm for flexible job-shop scheduling problems. Journal of Mechanical Science and Technology, 29(3), 1273. https://doi.org/10.1007/s12206-015-0242-7
Dunnette, M. D. (1966). Personnel selection and placement. Dursun, M., & Karsak, E. E. (2010). A fuzzy MCDM approach for personnel selection. Expert Systems with Applications, 37(6), 4324-4330. https://doi.org/10.1016/j.eswa.2009.11.067
Errarhout, A., Kharraja, S., & Corbier, C. (2016). Two-stage Stochastic Assignment Problem in the Home Health Care. IFAC-PapersOnLine, 49(12), 1152-1157. https://doi.org/10.1016/j.ifacol.2016.07.659
García-Pedrajas, N., Ortiz-Boyer, D., & Hervás-Martínez, C. (2006). An alternative approach for neural network evolution with a genetic algorithm: Crossover by combinatorial optimization. Neural Networks, 19(4), 514-528. https://doi.org/10.1016/j.neunet.2005.08.014
Gladkov, L., Gladkova, N., & Leiba, S. (2014). Manufacturing scheduling problem based on fuzzy genetic algorithm. Paper presented at the Design & Test Symposium (EWDTS), 2014 East-West. https://doi.org/10.1109/EWDTS.2014.7027075
Golec, A., & Kahya, E. (2007). A fuzzy model for competency-based employee evaluation and selection. Computers & Industrial Engineering, 52(1), 143-161. https://doi.org/10.1016/j.cie.2006.11.004
Guillaume, R., Houé, R., & Grabot, B. (2014). Robust competence assessment for job assignment. European Journal of Operational Research, 238(2), 630-644. https://doi.org/10.1016/j.ejor.2014.04.022
Güngör, Z., Serhadlıoğlu, G., & Kesen, S. E. (2009). A fuzzy AHP approach to personnel selection problem. Applied Soft Computing, 9(2), 641-646. https://doi.org/10.1016/j.asoc.2008.09.003
Gupta, P., Mehlawat, M. K., & Mittal, G. (2013). A fuzzy approach to multicriteria assignment problem using exponential membership functions. International Journal of Machine Learning and Cybernetics, 4(6), 647-657. https://doi.org/10.1007/s13042-012-0122-8
Herrera, F., López, E., Mendana, C., & Rodrı́ guez, M. A. (1999). Solving an assignment-selection problem with verbal information and using genetic algorithms. European Journal of Operational Research, 119(2), 326-337. https://doi.org/10.1016/S0377-2217(99)00134-4
Herrera, F., López, E., Mendaña, C., & Rodrı́ guez, M. A. (2001). A linguistic decision model for personnel management solved with a linguistic biobjective genetic algorithm. Fuzzy Sets and Systems, 118(1), 47-64. https://doi.org/10.1016/S0165-0114(98)00373-X
Holland, J. H. (1975). Adaptation in natural and artificial systems. An introductory analysis with application to biology, control, and artificial intelligence. Ann Arbor, MI: University of Michigan Press.
Hougaard, J. L., Moreno-Ternero, J. D., & Østerdal, L. P. (2014). Assigning agents to a line. Games and Economic Behavior, 87, 539-553. https://doi.org/10.1016/j.geb.2014.02.011
Iwaro, J., Mwasha, A., Williams, R. G., & Zico, R. (2014). An Integrated Criteria Weighting Framework for the sustainable performance assessment and design of building envelope. Renewable and Sustainable Energy Reviews, 29, 417-434. https://doi.org/10.1016/j.rser.2013.08.096
Jang, J.-S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics, IEEE Transactions on, 23(3), 665-685. https://doi.org/10.1109/21.256541
Jia, L., Wang, Y., & Fan, L. (2014). Multiobjective bilevel optimization for production-distribution planning problems using hybrid genetic algorithm. Integrated Computer-Aided Engineering, 21(1), 77-90. https://doi.org/10.3233/ICA-130452
Jiménez-Domingo, E., Colomo-Palacios, R., & Gómez-Berbís, J. M. (2014). A Multi-Objective Genetic Algorithm for Software Personnel Staffing for HCIM Solutions. International Journal of Web Portals (IJWP), 6(2), 26-41. https://doi.org/10.4018/ijwp.2014040103
Jogaratnam, G. (2017). The effect of market orientation, entrepreneurial orientation and human capital on positional advantage: Evidence from the restaurant industry. International Journal of Hospitality Management, 60, 104-113. https://doi.org/10.1016/j.ijhm.2016.10.002
Kalali, N. S. (2015). A fuzzy inference system for supporting the retention strategies of human capital. Procedia-Social and Behavioral Sciences, 207, 344-353. https://doi.org/10.1016/j.sbspro.2015.10.104
Katou, A. A., & Budhwar, P. S. (2010). Causal relationship between HRM policies and organisational performance: Evidence from the Greek manufacturing sector. European management journal, 28(1), 25-39. https://doi.org/10.1016/j.emj.2009.06.001
Korkmaz, İ., Gökçen, H., & Çetinyokuş, T. (2008). An analytic hierarchy process and two-sided matching based decision support system for military personnel assignment. Information Sciences, 178(14), 2915-2927. https://doi.org/10.1016/j.ins.2008.03.005
Kusumawardani, R. P., & Agintiara, M. (2015). Application of fuzzy AHP-TOPSIS method for decision making in human resource manager selection process. Procedia Computer Science, 72, 638-646. https://doi.org/10.1016/j.procs.2015.12.173
Lin, H.-T. (2010). Personnel selection using analytic network process and fuzzy data envelopment analysis approaches. Computers & Industrial Engineering, 59(4), 937-944. https://doi.org/10.1016/j.cie.2010.09.004
Lin, M., Chin, K. S., Wang, X., & Tsui, K. L. (2016). The therapist assignment problem in home healthcare structures. Expert Systems with Applications, 62, 44-62. https://doi.org/10.1016/j.eswa.2016.06.010
Lin, S.-Y., Horng, S.-J., Kao, T.-W., Fahn, C.-S., Huang, D.-K., Run, R.-S., . . . Kuo, I.-H. (2012). Solving the bi-objective personnel assignment problem using particle swarm optimization. Applied Soft Computing, 12(9), 2840-2845. https://doi.org/10.1016/j.asoc.2012.03.031
Lin, S.-Y., Horng, S.-J., Kao, T.-W., Huang, D.-K., Fahn, C.-S., Lai, J.-L., . . . Kuo, I.-H. (2010). An efficient bi-objective personnel assignment algorithm based on a hybrid particle swarm optimization model. Expert Systems with Applications, 37(12), 7825-7830. https://doi.org/10.1016/j.eswa.2010.04.056
Liu, J., Luo, X.-G., Zhang, X.-M., Zhang, F., & Li, B.-N. (2013). Job scheduling model for cloud computing based on multi-objective genetic algorithm. IJCSI International Journal of Computer Science Issues, 10(1), 134-139.
Lopez-Cabrales, A., Valle, R., & Herrero, I. (2006). The contribution of core employees to organizational capabilities and efficiency. Human Resource Management, 45(1), 81-109. https://doi.org/10.1002/hrm.20094
Malhotra, R., Singh, N., & Singh, Y. (2011). Genetic algorithms: Concepts, design for optimization of process controllers. Computer and Information Science, 4(2), 39. https://doi.org/10.5539/cis.v4n2p39
Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International journal of man-machine studies, 7(1), 1-13. https://doi.org/10.1016/S0020- 7373(75)80002-2
Marvel, M. R., Davis, J. L., & Sproul, C. R. (2016). Human capital and entrepreneurship research: A critical review and future directions. Entrepreneurship Theory and Practice, 40(3), 599-626. https://doi.org/10.1111/etap.12136
Mediouni, A., & Cheikhrouhou, N. (2019). Expert Selection for Humanitarian Projects Development: A Group Decision Making approach with Incomplete Information Relations. IFAC-PapersOnLine, 52(13), 1943-1948. https://doi.org/10.1016/j.ifacol.2019.11.487
Minguela-Rata, B., & Arias-Aranda, D. (2009). New product performance through multifunctional teamwork: An analysis of the development process towards quality excellence. Total Quality Management, 20(4), 381-392. https://doi.org/10.1080/14783360902781824
Mitchell, M. (1998). An introduction to genetic algorithms: MIT press. https://doi.org/10.7551/mitpress/3927.001.0001
Mutlu, Ö., Polat, O., & Supciller, A. A. (2013). An iterative genetic algorithm for the assembly line worker assignment and balancing problem of type-II. Computers & Operations Research, 40(1), 418- 426. https://doi.org/10.1016/j.cor.2012.07.010
Niknafs, A. (2016). A Hybrid Search Method for Evolutionary Dynamic Optimization of the 3- dimensional Personnel Assignment Problem and its Case Study Evaluation at The City of Calgary. University of Calgary.
Niknafs, A., Denzinger, J., & Ruhe, G. (2013). A systematic literature review of the personnel assignment problem. Paper presented at the Proceedings of the International Multiconference of Engineers and Computer Scientists, Hong Kong.
Oreski, S., & Oreski, G. (2014). Genetic algorithm-based heuristic for feature selection in credit risk assessment. Expert Systems with Applications, 41(4), 2052-2064. https://doi.org/10.1016/j.eswa.2013.09.004
Pépiot, G., Cheikhrouhou, N., Fürbringer, J.-M., & Glardon, R. (2008). A fuzzy approach for the evaluation of competences. International Journal of Production Economics, 112(1), 336-353. https://doi.org/10.1016/j.ijpe.2006.08.025
Rabiei, A., Sayyad, H., Riazi, M., & Hashemi, A. (2015). Determination of dew point pressure in gas condensate reservoirs based on a hybrid neural genetic algorithm. Fluid Phase Equilibria, 387, 38-49. https://doi.org/10.1016/j.fluid.2014.11.027
Rabiei, P., & Arias-Aranda, D. (2018). An Adaptive Network-based Fuzzy Inference System for predicting organizational commitment according to different levels of job satisfaction in growing economies. SIMULATION, 94(4), 341-358. https://doi.org/10.1177/0037549717712037
Rao, R., & Patel, V. (2013). Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems. International Journal of Industrial Engineering Computations, 4(1), 29-50. https://doi.org/10.5267/j.ijiec.2012.09.001
Razali, N. M., & Geraghty, J. (2011). Genetic algorithm performance with different selection strategies in solving TSP. Paper presented at the Proceedings of the world congress on engineering, Hong Kong.
Różewski, P., & Małachowski, B. (2009). Competence management in knowledge-based organisation: case study based on higher education organisation. Paper presented at the International Conference on Knowledge Science, Engineering and Management, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10488-6_35
Ruzic, M. D., Skenderovic, J., & Lesic, K. T. (2016). Application of the Mamdani fuzzy inference system to measuring HRM performance in hotel companies-A pilot study. Teorija in Praksa, 53(4), 976.
Sang, X., Liu, X., & Qin, J. (2015). An analytical solution to fuzzy TOPSIS and its application in personnel selection for knowledge-intensive enterprise. Applied Soft Computing, 30, 190-204. https://doi.org/10.1016/j.asoc.2015.01.002
Sharma, D., Singh, V., & Sharma, C. (2012). GA based scheduling of FMS using roulette wheel selection process. Paper presented at the Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011, New Delhi. https://doi.org/10.1007/978-81- 322-0491-6_86
Soares, A., Antunes, C. H., Oliveira, C., & Gomes, Á. (2014). A multi-objective genetic approach to domestic load scheduling in an energy management system. Energy, 77, 144-152. https://doi.org/10.1016/j.energy.2014.05.101
Suleman, A., & Suleman, F. (2012). Ranking by competence using a fuzzy approach. Quality & Quantity, 46(1), 323-339. https://doi.org/10.1007/s11135-010-9357-1
Tahriri, F., Mousavi, M., Haghighi, S. H., & Dawal, S. Z. M. (2014). The application of fuzzy Delphi and fuzzy inference system in supplier ranking and selection. Journal of Industrial Engineering International, 10(3), 66. https://doi.org/10.1007/s40092-014-0066-6
Tailor, A. R., & Dhodiya, J. M. (2016). Genetic algorithm based hybrid approach to solve optimistic, most-likely and pessimistic scenarios of fuzzy multi-objective assignment problem using exponential membership function. Br J Math Comput Sci, 17(2), 1-19. https://doi.org/10.9734/BJMCS/2016/26988
Toroslu, I. H., & Arslanoglu, Y. (2007). Genetic algorithm for the personnel assignment problem with multiple objectives. Information Sciences, 177(3), 787-803. https://doi.org/10.1016/j.ins.2006.07.032
Tosun, U., Dokeroglu, T., & Cosar, A. (2013). A robust island parallel genetic algorithm for the quadratic assignment problem. International Journal of Production Research, 51(14), 4117-4133. https://doi.org/10.1080/00207543.2012.746798
Veale, R., & Quester, P. (2007). Personal self confidence: Towards the development of a reliable measurement scale. Paper presented at the ANZMAC conference. Retrieved July.
Vecchione, M., Alessandri, G., & Barbaranelli, C. (2012). The Five Factor Model in personnel selection: Measurement equivalence between applicant and non-applicant groups. Personality and Individual Differences, 52(4), 503-508. https://doi.org/10.1016/j.paid.2011.11.014
Wong, J. Y., Sharma, S., & Rangaiah, G. (2016). Design of shell-and-tube heat exchangers for multiple objectives using elitist non-dominated sorting genetic algorithm with termination criteria. Applied Thermal Engineering, 93, 888-899. https://doi.org/10.1016/j.applthermaleng.2015.10.055
Yang, C., Peng, S., Jiang, B., Wang, L., & Li, R. (2014). Hyper-heuristic genetic algorithm for solving frequency assignment problem in TD-SCDMA. Paper presented at the Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation. https://doi.org/10.1145/2598394.2605445
Yu, D., Zhang, W., & Xu, Y. (2013). Group decision making under hesitant fuzzy environment with application to personnel evaluation. Knowledge-Based Systems, 52, 1-10. https://doi.org/10.1016/j.knosys.2013.04.010
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353. https://doi.org/10.1016/S0019- 9958(65)90241-X
Zhao, S., & Du, J. (2012). Thirty-two years of development of human resource management in China: Review and prospects. Human resource management review, 22(3), 179-188. https://doi.org/10.1016/j.hrmr.2012.02.001
Zhong, J., Hu, X., Zhang, J., & Gu, M. (2005). Comparison of performance between different selection strategies on simple genetic algorithms. Paper presented at the Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), International Conference on.
Zimmermann, H.-J. (2011). Fuzzy set theory-and its applications: Springer Science & Business Media.
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