Design and Development of a Genetic Algorithm Based on Fuzzy Inference Systems for Personnel Assignment Problem
In today’s competitive markets, the role of human resources as a sustainable competitive advantage is undeniable. Reliable hiring decisions for personnel assignation contribute greatly to a firms’ success. The Personnel Assignment Problem (PAP) relies on assigning the right people to the right positions. The solution to the PAP provided in this paper includes the introducing and testing of an algorithm based on a combination of a Fuzzy Inference System (FIS) and a Genetic Algorithm (GA). The evaluation of candidates is based on subjective knowledge and is influenced by uncertainty. A FIS is applied to model experts’ qualitative knowledge and reasoning. Also, a GA is applied for assigning assessed candidates to job vacancies based on their competency and the significance of each position. The proposed algorithm is applied in an Iranian company in the chocolate industry. Thirty-five candidates were evaluated and assigned to three different positions. The results were assessed by ten staff managers and the algorithm results proved to be satisfactory in discovering desirable solutions. Also, two GA selection techniques (tournament selection and proportional roulette wheel selection) were used and compared. Results show that tournament selection has better performance than proportional roulette wheel selection.
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Ahmed, F., & Deb, K. (2013). Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms. Soft Computing, 17(7), 1283-1299.
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.
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.
Arabali, A., Ghofrani, M., Etezadi-Amoli, M., Fadali, M. S., & Baghzouz, Y. (2013). Genetic-algorithm-based optimization approach for energy management. IEEE Transactions on Power Delivery, 28(1), 162-170.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of management, 17(1), 99-120.
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.
Bhateja, A., & Kumar, S. (2014). Genetic algorithm with elitism for cryptanalysis of vigenere cipher. Paper presented at the Issues and Challenges in Intelligent Computing Techniques (ICICT), 2014 International Conference on.
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.
Boselie, P., Dietz, G., & Boon, C. (2005). Commonalities and contradictions in HRM and performance research. Human resource management journal, 15(3), 67-94.
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.
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.
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.
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.
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.
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.
Day, R. C., & Hamblin, R. L. (1964). Some effects of close and punitive styles of supervision. American Journal of Sociology, 69(5), 499-510.
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.
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.
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.
Errarhout, A., Kharraja, S., & Corbier, C. (2016). Two-stage Stochastic Assignment Problem in the Home Health Care. IFAC-PapersOnLine, 49(12), 1152-1157.
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.
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.
Golec, A., & Kahya, E. (2007). A fuzzy model for competency-based employee evaluation and selection. Computers & Industrial Engineering, 52(1), 143-161.
Guillaume, R., Houé, R., & Grabot, B. (2014). Robust competence assessment for job assignment. European Journal of Operational Research, 238(2), 630-644.
Güngör, Z., Serhadlıoğlu, G., & Kesen, S. E. (2009). A fuzzy AHP approach to personnel selection problem. Applied Soft Computing, 9(2), 641-646.
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.
Herrera, F., López, E., Mendana, C., & Rodrı́guez, M. A. (1999). Solving an assignment–selection problem with verbal information and using genetic algorithms. European Journal of Operational Research, 119(2), 326-337.
Herrera, F., López, E., Mendaña, C., & Rodrı́guez, 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.
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.
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.
Jang, J.-S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics, IEEE Transactions on, 23(3), 665-685.
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.
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.
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.
Kalali, N. S. (2015). A fuzzy inference system for supporting the retention strategies of human capital. Procedia-Social and Behavioral Sciences, 207, 344-353.
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.
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.
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.
Lin, H.-T. (2010). Personnel selection using analytic network process and fuzzy data envelopment analysis approaches. Computers & Industrial Engineering, 59(4), 937-944.
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.
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.
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.
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.
Malhotra, R., Singh, N., & Singh, Y. (2011). Genetic algorithms: Concepts, design for optimization of process controllers. Computer and Information Science, 4(2), 39.
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.
Marvel, M. R., Davis, J. L., & Sproul, C. R. (2014). Human capital and entrepreneurship research: A critical review and future directions. Entrepreneurship Theory and Practice.
Mitchell, M. (1998). An introduction to genetic algorithms: MIT press.
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.
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.
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.
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.
Rabiei, P., & Arias-Aranda, D. (2017). An Adaptive Network-based Fuzzy Inference System for predicting organizational commitment according to different levels of job satisfaction in growing economies. SIMULATION, 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.
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.
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.
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.
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.
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.
Suleman, A., & Suleman, F. (2012). Ranking by competence using a fuzzy approach. Quality & Quantity, 46(1), 323-339.
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.
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.
Toroslu, I. H., & Arslanoglu, Y. (2007). Genetic algorithm for the personnel assignment problem with multiple objectives. Information Sciences, 177(3), 787-803.
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.
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.
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.
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.
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.
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
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.
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, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on.
Zimmermann, H.-J. (2011). Fuzzy set theory—and its applications: Springer Science & Business Media.
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