Load Allocation in Academic Environment: A Multi Objective PSO Approach
In an Organization, mapping the competency of personnel with different level of expertise, skill set, and experience in professional fields is a tough, complex but essential task. In this work, we have considered an Engineering College with moderate number of faculties with different level of experi...
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| Published in | GSTF International journal on computing Vol. 3; no. 4; p. 9 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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Global Science and Technology Forum
31.05.2014
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| Abstract | In an Organization, mapping the competency of personnel with different level of expertise, skill set, and experience in professional fields is a tough, complex but essential task. In this work, we have considered an Engineering College with moderate number of faculties with different level of experience, expertise and research exposure. Here we have considered the load assignment to the faculties at the beginning of a semester as the competency mapping task. Each faculty having capabilities of teaching different subjects out of the total set of papers needs to take about two theory papers with or without laboratory component. The decisive factors for subject assignment may be depth of knowledge, sincerity, class management, contribution towards research, text book publication. Further preference of the faculty member should be considered with top priority unless there are some valid constraints. Again the teaching personnel in a department hold different designations and different administrative responsibility, therefore each of them cannot be assigned equal hours of teaching load. The All India Council for Technical Education (AICTE) guidelines is considered as a baseline for assignment of teaching load. The decisive factors are considered as objectives to be optimized and multi-objective particle Swarm optimization (MOPSO) is employed to perform the competency mapping task. The simulation results show the effectiveness of this approach. |
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| AbstractList | In an Organization, mapping the competency of personnel with different level of expertise, skill set, and experience in professional fields is a tough, complex but essential task. In this work, we have considered an Engineering College with moderate number of faculties with different level of experience, expertise and research exposure. Here we have considered the load assignment to the faculties at the beginning of a semester as the competency mapping task. Each faculty having capabilities of teaching different subjects out of the total set of papers needs to take about two theory papers with or without laboratory component. The decisive factors for subject assignment may be depth of knowledge, sincerity, class management, contribution towards research, text book publication. Further preference of the faculty member should be considered with top priority unless there are some valid constraints. Again the teaching personnel in a department hold different designations and different administrative responsibility, therefore each of them cannot be assigned equal hours of teaching load. The All India Council for Technical Education (AICTE) guidelines is considered as a baseline for assignment of teaching load. The decisive factors are considered as objectives to be optimized and multi-objective particle Swarm optimization (MOPSO) is employed to perform the competency mapping task. The simulation results show the effectiveness of this approach. In an Organization, mapping the competency of personnel with different level of expertise, skill set, and experience in professional fields is a tough, complex but essential task. In this paper, the authors have considered an Engineering College with moderate number of faculties with different level of experience, expertise and research exposure. They have considered the load assignment to the faculties at the beginning of a semester as the competency mapping task. Each faculty having capabilities of teaching different subjects out of the total set of papers needs to take about two theory papers with or without laboratory component. The All India Council for Technical Education guidelines is considered as a baseline for assignment of teaching load. The decisive factors are considered as objectives to be optimized and multi-objective particle Swarm optimization is employed to perform the competency mapping task. The simulation results show the effectiveness of this approach. |
| ArticleNumber | 36 |
| Author | Rout, Sushri Samita Misra, Bijan Bihari Samanta, Sasmita |
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Coello Coello, A Ponsich, "A Survey on Multi-Objective Evolutionary Algorithms for the solution of the Portfolio Optimization problem and other Finance and Economics applications," Evolutionary Computation, IEEE Transactions on, vol.PP, no.99, pp. 1, 0,2012 Z Jia and L Gong, “Multi-criteria Human Resource Allocation for Optimization Problems Using Multi-objective Particle Swarm Optimization Algorithm “, International conference on Computer Science and Software Engineering, 2008, pp. 1187–1190 TungYangJ Chou.” Multiobjective optimization formanpower assignment in consulting engineering firms”Applied Soft Computing2011111183–1190 K. Deb, A. Pratap, S. Agrawal, and T. Meyarivan, “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA–II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002. H Eskandari, C Geiger, G Lamont, “ FastPGA: A Dynamic Population Sizing Approach for Solving Expensive Multiobjective Optimization Problems” in Proceedings of 4th International Conference, EMO 2007, Matsushima, Japan, March 5-8, 2007, pp. 141–155. EckartZitzler“Evolutionary Algorithm for Multi-objective Optimizatiom: Methods and Applications”, Doctoral Thesis, Swiss Federal Institute of Technology, Zurich, 1999. [33]. K Deb, "Multi-Objective Optimization using Evolutionary Algorithms,"2001ChichesterJohn Wiley & Sons N. Srinivas and K. Deb, “Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms,” Evolutionary Computation, vol. 2, no. 3, pp. 221–248, Fall 1994. D Parchment, S Sankarnarayanan,” Intelligent Agent Based Student Staff Scheduling System” International Journal of Computer Information Systems and Industrial Management Applications, (IJCISIM), 2013,pp. 383–404 J. Schaffer, “Multiple objective optimization with vector evaluated genetic algorithms,” in Proceedings of the 1st International Conference on Genetic Algorithms. Hillsdale, NJ, USA: L. Erlbaum Associates Inc., 1985, pp. 93–100 SteuerR EMultiple Criteria Optimization: Theory, Computations, and Application1986New YorkJohn Wiley & Sons, Inc88846–X J. Schaffer, “Multiple objective optimization with vector evaluated genetic algorithms,” in Proceedings of the 1st International Conference on Genetic Algorithms. Hillsdale, NJ, USA: L. Erlbaum Associates Inc., 1985, pp. 93–100. C.A. Coello Coello, “Evolutionary Multi-objective Optimization: A Historical View of the Field”, IEEE Computational Intelligence Magazine, Feb 2006, pp. 28–36. Nebro, J., Durillo, F. Luna, B. Dorronsoro, and E. Alba, “Mocell: A cellular genetic algorithm for multiobjective optimization,” International Journal of Intelligent Systems, vol. 24, no. 7, pp. 726–746, 2009. ZadehL AOptimality and Nonscalar-Valued Performance Criteria," IEEE Trans. Automat. Contr., Vol. AC-8, p. 1, 1963. [14]. Y Censor, "Pareto Optimality in Multiobjective Problems," Appl. Math. Optimiz., Vol. 4, pp 41–59, 1977. [15]. N Da Cunha and E. Polak, "Constrained Minimization Under Vector-Valued Criteria in Finite Dimensional SpacesJ. Math. Anal. Appl196719103–124 Q Zhang and H. Li, “MOEA/D: A Multiobjective EvolutionaryAlgorithm based on Decomposition,” IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712–731, 2007. SawaragiYNakayamaHTaninoTTheory of Multiobjective Optimization (vol. 176 of Mathematics in Science and Engineering)1985Orlando, FLAcademic Press Inc620370–9 W Elloumi and A M Alimi, “A More Efficient MOPSO for Optimization”, ACS/IEEE International Conference on Computer Systems and Applications –AICCSA 2010, pp. 1–7, 2010. I Arbnor, B Bjerke, “Methodology for creating business knowledge”, SAGE Publication, 2nd Edition, pp. 21–72. G Meenakshi, ”Multi source feedback based performance appraisal system using Fuzzy logic decision support system,” International Journal on Soft Computing (IJSC ), vol. l.3, no.1, pp. 91–106, February 2012 J. Nebro, F. Luna, E. Alba 1, A. Beham, B. Dorronsoro, ” AbYSS : Adapting Scatter Search for Multiobjective Optimization”, TECH-REPORT: ITI-2006–2 J. Knowles and D. Corne, “Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy,” Evolutionary Computation, vol. 8, no. 2, pp. 149–172, 2000. V. Shahhosseini, M.H. Sebt,”Competency-based selection and assignment of human resources to construction projects,” Scientia Iranica A, vol. 18, issue 2, pp. 163–180, 2011 S Wang, L Gong, S Yan.” The Allocation Optimization of Project Human Resource Based on Particle Swarm Optimization Algorithm”. IITA International Conference on Services Science, Management and Engineering, pp. 169–172, 2009. E. Zitzler and S. K¨unzli, “Indicator-based selection in multiobjective search,” in Parallel Problem Solving from Nature (PPSN VIII), X. Yao et al., Eds. Berlin, Germany: Springer-Verlag, 2004, pp. 832–842. S Rout, B Misra, S Samanta, “Competency Mapping in Academic Environment: A Multi Objective Approach” Information and Communication Technologies (WICT), 2012 World Congress on, vol., no., pp.543,548, Oct. 30 2012-Nov.2012. M Song and Guo-Chang GU, “Research On Particle Swarm Optimization : A Review”, in Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, 26-29 August 2004, pp. 2236–2241. TungYangChouJ“Multiobjective optimization formanpower assignment in consulting engineering firms”Applied Soft Computing2011111183119010.1016/j.asoc.2010.02.016 M.Geoffrion, J. S. Dyer, A. Feinberg (December 1972). "An Interactive Approach for Multi-Criterion Optimization, with an Application to the Operation of an Academic Department". Management Science. Application Series (INFORMS) 19 (4Part1):357–368. E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: Improving the Strength Pareto Evolutionary Algorithm,” in EUROGEN 2001. Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, K. Giannakoglou, D. Tsahalis, J. Periaux, P. Papailou, and T. Fogarty, Eds., Athens, Greece, 2002, pp. 95–100. W Qing, Z Han-chao,” Optimization of Task AllocationAnd Knowledge Workers Scheduling Based-on Particle Swarm Optimization” International Conference on Electric Information and Control Engineering (ICEICE), 2011, pp. 574 – 578, 2011. P. Liu and X. Li, “The Application of Expertise Knowledge Map in Human Resource Management,” International Conference on Management and Service Science (MASS), pp. 1 – 4, 2011. John McCullock, “Particle Swarm Optimization”. Internet: http://mnemstudio.org/particle-swarm-introduction.htm Zitzler Eckart (36_CR63) 2001 36_CR28 Y Sawaragi (36_CR11) 1985 36_CR27 36_CR29 36_CR24 36_CR23 36_CR26 R E Steuer (36_CR10) 1986 36_CR25 36_CR20 36_CR64 36_CR22 36_CR66 36_CR21 36_CR65 36_CR60 R E Steuer (36_CR43) 1986 36_CR62 36_CR61 Zitzler Eckart (36_CR30) 2001 36_CR39 36_CR38 36_CR34 36_CR37 36_CR36 36_CR31 36_CR33 36_CR32 L A Zadeh (36_CR13) 1967; 19 Yang Tung (36_CR35) 2011; 11 Yang Tung (36_CR2) 2011; 11 36_CR49 36_CR45 36_CR48 36_CR47 36_CR42 36_CR41 36_CR40 36_CR7 36_CR8 36_CR9 36_CR3 L A Zadeh (36_CR46) 1967; 19 36_CR4 36_CR5 36_CR6 36_CR1 36_CR17 36_CR16 36_CR19 36_CR18 36_CR57 36_CR12 36_CR56 36_CR15 36_CR59 36_CR14 36_CR58 36_CR53 36_CR52 36_CR55 Y Sawaragi (36_CR44) 1985 36_CR54 36_CR51 36_CR50 |
| References_xml | – reference: D Parchment, S Sankarnarayanan,” Intelligent Agent Based Student Staff Scheduling System” International Journal of Computer Information Systems and Industrial Management Applications, (IJCISIM), 2013,pp. 383–404 – reference: EckartZitzler“Evolutionary Algorithm for Multi-objective Optimizatiom: Methods and Applications”, Doctoral Thesis, Swiss Federal Institute of Technology, Zurich, 1999. [33]. K Deb, "Multi-Objective Optimization using Evolutionary Algorithms,"2001ChichesterJohn Wiley & Sons – reference: TungYangJ Chou.” Multiobjective optimization formanpower assignment in consulting engineering firms”Applied Soft Computing2011111183–1190 – reference: ZadehL AOptimality and Nonscalar-Valued Performance Criteria," IEEE Trans. Automat. Contr., Vol. AC-8, p. 1, 1963. [14]. Y Censor, "Pareto Optimality in Multiobjective Problems," Appl. Math. Optimiz., Vol. 4, pp 41–59, 1977. [15]. N Da Cunha and E. Polak, "Constrained Minimization Under Vector-Valued Criteria in Finite Dimensional SpacesJ. Math. Anal. Appl196719103–124 – reference: Q Zhang and H. Li, “MOEA/D: A Multiobjective EvolutionaryAlgorithm based on Decomposition,” IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712–731, 2007. – reference: A. Jaimes, C.A. Coello Coello, A Ponsich, "A Survey on Multi-Objective Evolutionary Algorithms for the solution of the Portfolio Optimization problem and other Finance and Economics applications," Evolutionary Computation, IEEE Transactions on, vol.PP, no.99, pp. 1, 0,2012 – reference: M Song and Guo-Chang GU, “Research On Particle Swarm Optimization : A Review”, in Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, 26-29 August 2004, pp. 2236–2241. – reference: J. Schaffer, “Multiple objective optimization with vector evaluated genetic algorithms,” in Proceedings of the 1st International Conference on Genetic Algorithms. Hillsdale, NJ, USA: L. Erlbaum Associates Inc., 1985, pp. 93–100 – reference: I Arbnor, B Bjerke, “Methodology for creating business knowledge”, SAGE Publication, 2nd Edition, pp. 21–72. – reference: W Qing, Z Han-chao,” Optimization of Task AllocationAnd Knowledge Workers Scheduling Based-on Particle Swarm Optimization” International Conference on Electric Information and Control Engineering (ICEICE), 2011, pp. 574 – 578, 2011. – reference: S. Ghoneim, D. L. Essam, and H. A. Abbass, “Competency Awareness in Strategic Decision Making,” IEEE International multi-disciplinary conference on cognitive methods in situation awareness and decision support, pp. 106–109, 2011 – reference: J. Schaffer, “Multiple objective optimization with vector evaluated genetic algorithms,” in Proceedings of the 1st International Conference on Genetic Algorithms. Hillsdale, NJ, USA: L. Erlbaum Associates Inc., 1985, pp. 93–100. – reference: S Wang, L Gong, S Yan.” The Allocation Optimization of Project Human Resource Based on Particle Swarm Optimization Algorithm”. IITA International Conference on Services Science, Management and Engineering, pp. 169–172, 2009. – reference: P. Liu and X. Li, “The Application of Expertise Knowledge Map in Human Resource Management,” International Conference on Management and Service Science (MASS), pp. 1 – 4, 2011. – reference: SawaragiYNakayamaHTaninoTTheory of Multiobjective Optimization (vol. 176 of Mathematics in Science and Engineering)1985Orlando, FLAcademic Press Inc620370–9 – reference: W Elloumi and A M Alimi, “A More Efficient MOPSO for Optimization”, ACS/IEEE International Conference on Computer Systems and Applications –AICCSA 2010, pp. 1–7, 2010. – reference: N. Srinivas and K. Deb, “Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms,” Evolutionary Computation, vol. 2, no. 3, pp. 221–248, Fall 1994. – reference: S Rout, B Misra, S Samanta, “Competency Mapping in Academic Environment: A Multi Objective Approach” Information and Communication Technologies (WICT), 2012 World Congress on, vol., no., pp.543,548, Oct. 30 2012-Nov.2012. – reference: Fonseca and P. Fleming, “Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization,” in Proceedings of the Fifth International Conference on Genetic Algorithms, S. Forrest, Ed., University of Illinois at Urbana-Champaign. San Mateo, California: Morgan Kauffman Publishers, 1993, pp. 416–423. – reference: M.Geoffrion, J. S. Dyer, A. Feinberg (December 1972). "An Interactive Approach for Multi-Criterion Optimization, with an Application to the Operation of an Academic Department". Management Science. Application Series (INFORMS) 19 (4Part1):357–368. – reference: H Eskandari, C Geiger, G Lamont, “ FastPGA: A Dynamic Population Sizing Approach for Solving Expensive Multiobjective Optimization Problems” in Proceedings of 4th International Conference, EMO 2007, Matsushima, Japan, March 5-8, 2007, pp. 141–155. – reference: Nebro, J., Durillo, F. Luna, B. Dorronsoro, and E. Alba, “Mocell: A cellular genetic algorithm for multiobjective optimization,” International Journal of Intelligent Systems, vol. 24, no. 7, pp. 726–746, 2009. – reference: V. Shahhosseini, M.H. Sebt,”Competency-based selection and assignment of human resources to construction projects,” Scientia Iranica A, vol. 18, issue 2, pp. 163–180, 2011 – reference: SteuerR EMultiple Criteria Optimization: Theory, Computations, and Application1986New YorkJohn Wiley & Sons, Inc88846–X – reference: J. Knowles and D. Corne, “Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy,” Evolutionary Computation, vol. 8, no. 2, pp. 149–172, 2000. – reference: E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: Improving the Strength Pareto Evolutionary Algorithm,” in EUROGEN 2001. Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, K. Giannakoglou, D. Tsahalis, J. Periaux, P. Papailou, and T. Fogarty, Eds., Athens, Greece, 2002, pp. 95–100. – reference: J. Nebro, F. Luna, E. Alba 1, A. Beham, B. Dorronsoro, ” AbYSS : Adapting Scatter Search for Multiobjective Optimization”, TECH-REPORT: ITI-2006–2 – reference: C.A. Coello Coello, “Evolutionary Multi-objective Optimization: A Historical View of the Field”, IEEE Computational Intelligence Magazine, Feb 2006, pp. 28–36. – reference: G Meenakshi, ”Multi source feedback based performance appraisal system using Fuzzy logic decision support system,” International Journal on Soft Computing (IJSC ), vol. l.3, no.1, pp. 91–106, February 2012 – reference: E. Zitzler and S. K¨unzli, “Indicator-based selection in multiobjective search,” in Parallel Problem Solving from Nature (PPSN VIII), X. Yao et al., Eds. Berlin, Germany: Springer-Verlag, 2004, pp. 832–842. – reference: TungYangChouJ“Multiobjective optimization formanpower assignment in consulting engineering firms”Applied Soft Computing2011111183119010.1016/j.asoc.2010.02.016 – reference: K. Deb, A. Pratap, S. Agrawal, and T. Meyarivan, “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA–II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002. – reference: John McCullock, “Particle Swarm Optimization”. Internet: http://mnemstudio.org/particle-swarm-introduction.htm – reference: Z Jia and L Gong, “Multi-criteria Human Resource Allocation for Optimization Problems Using Multi-objective Particle Swarm Optimization Algorithm “, International conference on Computer Science and Software Engineering, 2008, pp. 1187–1190 – ident: 36_CR34 doi: 10.1109/ICEICE.2011.5778029 – ident: 36_CR9 doi: 10.1109/ICMSS.2011.5998043 – ident: 36_CR20 doi: 10.1109/TEVC.2007.892759 – ident: 36_CR28 – ident: 36_CR47 – volume-title: “Evolutionary Algorithm for Multi-objective Optimizatiom: Methods and Applications”, Doctoral Thesis, Swiss Federal Institute of Technology, Zurich, 1999. [33]. 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| Title | Load Allocation in Academic Environment: A Multi Objective PSO Approach |
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