Total views : 320

Resolving the Recruitment and Selection Problem as NP-Hard Problem

Affiliations

  • Centre for Information Technology and Systems, University of Lagos, Lagos, Nigeria
  • Department of Computer Science, Covenant University, Ota, Nigeria

Abstract


Background/Objectives: As organizations increasingly strive to attract and retain high calibre ICT-compliant staff, recruitment and selection is attracting huge attention. This paper classified the recruitment and selection problem as NP-hard problem and applied metaheuristic algorithm to solve it. Methods/Statistical Analysis: This study focused on University of Lagos, Nigeria as case study and applied computational theory in the form of metaheuristic algorithm in a bid to improve on the existing recruitment and selection process. We reviewed literature, gathered requirements, designed a system and statically tested the process-correctness of the proposed system. The operational recruitment and selection data collected and used for statical testing were obtained from secondary materials of the University. Findings: We were able to establish that the recruitment and selection problem in University of Lagos, Nigeria is NP-Hard and equally confirmed appropriateness of applying metaheuristic solution rather than exact algorithm to such problem, given its complex and varied nature. In our view, this is an addition to the growing body of knowledge of the metaheuristic community. Also, the research outcome is an addition to the human resource management community knowledge space. Against the backdrop that human resources are germane to the socio-economic transformation of nations coupled with the challenges of obtaining optimal solutions (best-known candidates) from a teeming pool of applicants, human resource experts are excited that a value-addition metaheuristic solution such as ours can reduce their recruitment and selection stress by about 50%. With the right crop of employees, set organizational goals and objectives can be achieved in the most efficient and effective fashion. Applications/Improvements: The study outcome is a software architecture that will scaleup the search for optimal solutions (best candidates) from any pool of job applicants in a timely and cost-effective fashion. This best-fit search apparatus will help human resource experts in aligning organization’s human resource strategy with its corporate strategy and objectives.

Keywords

NP-Hard, Problem, Recruitment, Resolving, Selection.

Full Text:

 |  (PDF views: 305)

References


  • Glover F. Tabu Search - Part 1. ORSA Journal on Computing. 1989; 1(2):190-206. Doi: 10.1287/ijoc.1.3.190.
  • Sorensen K, Sevaux M and Schittekat P. London: Springer: Multiple neighbourhood search in commercial VRP packages: evolving towards self-adaptive methods, volume 136 of Lecture Notes in Economics and Mathematical Systems, chapter Adaptive, self-adaptive and multi-level metaheuristics. 2008; 239-53.
  • Sorensen K and Glover F. Metaheuristics. New York: Springer: In: Gass SI and Fu M, editors. Encyclopedia of Operations Research and Management Science. 2013; 960-70.
  • Sorensen K. Metaheuristics - the metaphor exposed. International Transactions in Operational Research. 2015; 22(1):3-18.
  • Fister I, Yang XS, Brest J and Fister D. A brief review of nature-inspired algorithms for optimization. arXiv preprint. arXiv:1307.4186. 2013.
  • Bianchi L, Dorigo M, Gambardella LM, Gutjahr WJ. A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing: an International Journal. 2009; 8(2):239-87.
  • Weyland D. A rigorous analysis of the harmony search algorithm: How the research community can be misled by a “novel” methodology. International Journal of Applied Metaheuristic Computing (IJAMC). 2010; 1(2):50-60.
  • Blum C and Roli A. Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys. 2003; 35(3):268-308.
  • Tomoiaga B, Chindriş M, Sumper A, Sudria-Andreu A, Villafafila-Robles R. Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA- II. Energies. 2013; 6(3):1439-55.
  • Yang XS. Metaheuristic Optimization. Scholarpedia. 2011; 6(8):11472.
  • Mondy RW. NJ: Pearson, Prentice Hall: Human resource management (10th ed.). 2008.
  • Strategic Plan. University of Lagos 25-year Strategic Plan. University of Lagos Press, Lagos, Nigeria. ISBN: 978-978-51929-7-1. 2013.
  • Gusdorf ML. Recruitment and Selection: Hiring the Right Person. A two-part learning module for undergraduate students. Society for Human Resource Management. SHRM Academic Initiatives 1800 Duke Street, Alexandria, VA 22314, USA. 2008.
  • Anglian Ruskin University. Staff Recruitment and Selection Policy and Procedure. November 2012. Cambridge Chelmsford Peterborough. 2012 November.
  • Failte Ireland. Recruitment and Selection. Online Business Tool, 88-95 Amiens Street, Dublin 1. 2013.
  • Leeuwen Jan van, ed. Handbook of Theoretical Computer Science. Amsterdam: Elsevier: Vol. A, Algorithms and complexity. ISBN 0262720140. OCLC 247934368. 1998.
  • Fodor J. Oxford and New York: Oxford University Press: LOT2: The Language of Thought Revisited. 2010.
  • Martin RC. UML Tutorial: Sequence Diagrams. Engineering Notebook Column. 1998; p. 1-5.
  • Jacques Sakarovitch. Cambridge University Press: Elements of automata theory. Translated from the French by Reuben Thomas. ISBN 978-0-521-84425-3. Zbl 1188.68177. 2009.
  • Yan SY. Singapore: World Scientific Publishing Co. Pte. Ltd: An Introduction to Formal Languages and Machine Computation. 1998; 155-56.
  • Chakraborty P, Saxena PC, Katti CP. Fifty Years of Automata Simulation: A Review. ACM Inroads. 2011; 2(4):59-70.
  • Gorton I. Springer: Essential Software Architecture. Second Edition. 2011.
  • Sirohi N and Parashar A. Component Based System and Testing Techniques. International Journal of Advanced Research in Computer and Communication Engineering. 2013; 2(6):33-42.
  • Dizaji ZA and Gharehchopogh FS. A Hybrid of Ant Colony Optimization and Chaos Optimization Algorithms Approach for Software Cost Estimation. Indian Journal of Science and Technology. 2015 January; 8(2).
  • Effatnejad R and Rouhi F. Unit Commitment in Power System t by Combination of Dynamic Programming (DP), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Indian Journal of Science and Technology. 2015 January; 8(2).

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.