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Matching formulation of the Staff Transfer Problem: meta-heuristic approaches

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Abstract

In this paper, the Staff Transfer Problem (STP) in Human Resource Management is addressed as a stable matching problem. Earlier, formulation of this problem was of scheduling/allocation type. Here, the stable matching formulation is completely a new and more practical approach to the problem. This new formulation involves two preference lists: the first list contains the offices/locations preferred by the employees undergoing transfer and the second list contains the employees preferred by the employer of an office/location where those employees want to be transferred. The capacity of an office/location would act as a hard constraint. While matching these two lists, the objective is to maximize the number of transfers and at the same time to stabilize the matching, i.e., to minimize the number of blocking pairs. The resulting STP instance belongs to an instance of Maximum Size Minimum Blocking Pair Stable Matching with incomplete preference list (MAX SIZE MIN BP SMI) and has been proved in this paper to be NP-hard. As the problem is new in formulation, no previous work, method or result is available. There was no preference in selecting meta-heuristics. Among a large number of existing meta-heuristics, some most widely used meta-heuristics, namely, Simulated Annealing, Genetic Algorithms, Tabu Search and some variants of them have been chosen. Based on them four meta-heuristic approaches have been proposed, namely, btSA_match, gtSA_match, GA_match and TS_match. The variants btSA_match and gtSA_match are obtained from modifications made upon Simulated Annealing. EGA_match and TS_match are based on modified Genetic Algorithms and Tabu Search respectively. As there is no previous result in the existing literature, the performance has been compared among these four methods. It is observed that, variants of Simulated Annealing (SA) outperform others w.r.t. the performance metrics. The SA-variant with greedy nature, incorporated with a tabu list (gtSA_match) has shown that the best result on the basis of statistical analysis.

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References

  1. 1.

    Beer, M., et al.: Human Resource Management: A General Manager’s Perspective, Text and Cases. The Free Press, New York (1985)

  2. 2.

    Dessler, G.: Human Resource Management. Prentice Hall, New Delhi (1997)

  3. 3.

    Acharyya, S., Bagchi, A.: Staff transfers in a large organization: a constraint satisfaction approach. In: Proceedings of the KBCS’98, Mumbai, pp. 51–63 (1998)

  4. 4.

    Acharyya, S., Bagchi, A.: Constraint satisfaction methods for solving the staff transfer problem. Opsearch 42(3), 179–198 (2005)

  5. 5.

    Reeves, C.R.: Modern Heuristic Techniques for Combinatorial Problems. Orient Longman, Hyderabad (1993)

  6. 6.

    Fukunaga, A.S.: Variable selection heuristics in local search for SAT. In: Proceedings of the AAAI’97, pp. 275–280 (1997)

  7. 7.

    Hoos, H. H.: On the run-time behavior of stochastic local search algorithms for SAT. In: Proceedings of the AAAI’99, pp. 661–666 (1999)

  8. 8.

    MacAllester, D., Selman, B., Kautz, H.: Evidence for invariants in local search. In: Proceedings of the AAAI’97, pp. 321–326 (1997)

  9. 9.

    Selman, B., Kautz, H., Cohen, B.: Noise strategies for improving local search. In: Proceedings of the AAAI’94, pp. 337–343 (1994)

  10. 10.

    Acharyya, S., Bagchi, A.: A SAT approach for solving the Staff Transfer Problem. In: Proceedings of the IMECS’08, Hong Kong, pp. 64–68 (2008)

  11. 11.

    Acharyya, S.: WalkSAT approach in solving the staff transfer problem. In: Proceedings of the ICCIT’08, pp. 1132–1137. IEEE Xplore, Khulna (2008)

  12. 12.

    Acharyya, S., Bagchi, A.: SAT approaches for solving the staff transfer problem. In: Proceedings of the CSAE’11. IEEE Xplore: Shanghai, pp. 492–496 (2011)

  13. 13.

    Biro, P., Manlove, D.F.: Mittal S: Size versus stability in the marriage proble. Theor. Comput. Sci. 411(16–18), 1828–1841 (2010)

  14. 14.

    Gusfield, D., Irving, R.W.: The Stable Marriage Problem—Structure and Algorithms. MIT Press, Cambridge (1999)

  15. 15.

    Manlove, D.F., O’Malley, G., Prosser, P., Unsworth C.A.: Constraint programming approach to the hospitals/residents problem. Technical Report TR-2007-236. Department of Computing Science, University of Glasgow (2007)

  16. 16.

    Johnson, D.S., Aragon, C.R., McGeoch, L.A., Schevon, C.: Optimization by simulated annealing: an experimental evaluation, part II, graph colouring and number partitioning. Oper. Res. 39, 378–406 (1991)

  17. 17.

    Meiri, R., Zahavi, J.: Using simulated annealing to optimize the feature selection problem in marketing applications. Eur. J. Oper. Res. 171(3), 842–858 (2006)

  18. 18.

    Acharyya, S: Simulated annealing variants in solving the staff transfer problem. In: Proceedings of the ICAEE’11, Dhaka, pp. 331–336 (2011)

  19. 19.

    Jafari, H., Salmasi, N.: Maximizing the nurses’ preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm. J. Ind. Eng. Int. 11(3), 439–458 (2015)

  20. 20.

    Biswas, S., Acharyya, S.: A Bi-objective RNN model to reconstruct gene regulatory network: a modified multi-objective simulated annealing approach. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB) 15(6), 2053–2059 (2018)

  21. 21.

    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, Boston (1989)

  22. 22.

    Bierwirth, C., Mattfeld, D.C.: Production scheduling and rescheduling with genetic algorithms. Evol. Comput. 7(1), 1–17 (1999)

  23. 23.

    Domberger, R., Frey, L., Hanne, T.: Single and multiobjective optimization of the train staff planning problem using genetic algorithms. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 970–977. IEEE (2008)

  24. 24.

    García-Villoria, A., Pastor, R.: Solving the response time variability problem by means of a genetic algorithm. Eur. J. Oper. Res. 202(2), 320–327 (2010)

  25. 25.

    Peteghem, V.V., Vanhoucke, M.: A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem. Eur. J. Oper. Res. 201(2), 409–418 (2010)

  26. 26.

    Glover, F., Laguna, M.: Tabu Search. Kluwer Academic, Dordrecht (1998)

  27. 27.

    Brandão, J.: A deterministic tabu search algorithm for the fleet size and mix vehicle routing problem. Eur. J. Oper. Res. 195(3), 716–728 (2009)

  28. 28.

    Krajewska, M.A., Kopfer, H.: Transportation planning in freight forwarding companies: tabu search algorithm for the integrated operational transportation planning problem. Eur. J. Oper. Res. 197(2), 741–751 (2009)

  29. 29.

    Peng, B., Lü, Z., Cheng, T.C.E.: A tabu search/path relinking algorithm to solve the job shop scheduling problem. Comput. Oper. Res. 53, 154–164 (2015)

  30. 30.

    Mogale, D.G., Kumar, S.K., Márquez, F.P.G., Tiwari, M.K.: Bulk wheat transportation and storage problem of public distribution system. Comput. Ind. Eng. 104, 80–97 (2017)

  31. 31.

    Maiyar, L.M., Thakkar, J.J.: A combined tactical and operational deterministic food grain transportation model: particle swarm based optimization approach. Comput. Ind. Eng. 110, 30–42 (2017)

  32. 32.

    Maiyar, L.M., Thakkar, J.J.: Modelling and analysis of intermodal food grain transportation under hub disruption towards sustainability. Int. J. Prod. Econ. 217, 281–297 (2019)

  33. 33.

    Yadav, N.K.: Rescheduling-based congestion management scheme using particle swarm optimization with distributed acceleration constants. Soft Comput. 23(3), 847–857 (2019)

  34. 34.

    Huang, H., Lv, L., Ye, S., Hao, Z.: Particle swarm optimization with convergence speed controller for large-scale numerical optimization. Soft Comput. 23(12), 4421–4437 (2019)

  35. 35.

    Maiyar, L.M., Cho, S., Tiwari, M.K., Thoben, K.D., Kiritsis, D.: Optimising online review inspired product attribute classification using the self-learning particle swarm-based Bayesian learning approach. Int. J. Prod. Res. 57(10), 3099–3120 (2019)

  36. 36.

    Jana, B., Mitra, S., Acharyya, S.: Repository and mutation based particle swarm optimization (RMPSO): a new PSO variant applied to reconstruction of gene regulatory network. Appl. Soft Comput. 74, 330–355 (2019)

  37. 37.

    Guo, Z., Wang, S., Yue, X., Yang, H.: Global harmony search with generalized opposition-based learning. Soft. Comput. 21(8), 2129–2137 (2017)

  38. 38.

    Biswas, S., Dutta, S., Acharyya, S.: Identification of disease critical genes using collective meta-heuristic approaches: an application to preeclampsia. Interdiscip Sci Comput Life Sci 11(3), 444–459 (2019)

  39. 39.

    Wang, L., Hu, H., Liu, R., Zhou, X.: An improved differential harmony search algorithm for function optimization problems. Soft Comput. 23(13), 4827–4852 (2019)

  40. 40.

    Cui, L., Li, G., Zhu, Z., Wen, Z., Lu, N., Lu, J.: A novel differential evolution algorithm with a self-adaptation parameter control method by differential evolution. Soft Comput. 22(18), 6171–6190 (2018)

  41. 41.

    Mogale, D.G., Dolgui, A., Kandhway, R., Kumar, S.K., Tiwari, M.K.: A multi-period inventory transportation model for tactical planning of food grain supply chain. Comput. Ind. Eng. 110, 379–394 (2017)

  42. 42.

    Papadimitriou, C.H., Steiglitz, K.: Combinatorial optimization: algorithms and complexity. Prentice Hall, Upper Saddle River (1982)

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Correspondence to S. Acharyya.

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Acharyya, S., Datta, A.K. Matching formulation of the Staff Transfer Problem: meta-heuristic approaches. OPSEARCH (2019). https://doi.org/10.1007/s12597-019-00432-w

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Keywords

  • Human resource planning
  • Staff transfer
  • Stable matching
  • Optimization
  • Meta-heuristics