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A heuristic method to schedule training programs for small and medium enterprises. (English) Zbl 1244.90100

Summary: During the life period of Small and Medium Enterprises (SMEs) in incubators they need some training programs to acquire the required knowledge in order to survive and succeed in the business environment. This paper presents a heuristic method based on an optimization model to schedule these programs at the most suitable times. Based on the proposed heuristic, each training program is implemented in a suitable time by considering the SMEs’ requirements and some other logical constraints. The proposed heuristic is described in detail, and its implementation is demonstrated via a real-life numerical example. The numerical results of the heuristic are compared with other methods.

MSC:

90B35 Deterministic scheduling theory in operations research
90C59 Approximation methods and heuristics in mathematical programming
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References:

[1] Aaboen, L., Explaining incubators using firm analogy, Technovation, 29, 10, 657-670 (2009)
[2] Alvarez-valdes, R.; Crespo, E.; Tamarit, J. M., Design and implementation of a course scheduling system using tabu search, European Journal of Operational Research, 137, 512-523 (2002) · Zbl 1007.90023
[3] Azimi, Z. N., Hybrid heuristics for examination timetabling problem, Applied Mathematics and Computation, 163, 2, 705-733 (2005) · Zbl 1116.90413
[4] Banfield, P.; Jennings, P. L.; Beaver, G., Competence-based training for small firms an expensive failure?, Long Range Planning, 29, 1, 94-102 (1996)
[5] Bergek, A.; Norman, C., Incubator best practice: A framework, Technovation, 28, 20-28 (2008)
[6] Blazewicz, J.; Ecker, K. H.; Pesch, E.; Schmidt, G.; Weglarz, J., Scheduling Computer and Manufacturing Processes (1996), Springer-Verlag: Springer-Verlag Berlin · Zbl 0911.90201
[7] Chrisman, J. J.; McMullen, W. E., Outsider assistance as a knowledge resource for new venture survival, Journal of Small Business Management, 42, 3, 229-244 (2004)
[8] Daskalaki, S.; Birbas, T., Efficient solutions for a university timetabling problem through integer programming, European Journal of Operational Research, 160, 1, 106-120 (2005) · Zbl 1067.90135
[9] Dimopoulou, M.; Miliotis, P., Implementation of a university course and examination timetabling system, European Journal of Operational Research, 130, 1, 202-213 (2001) · Zbl 0985.90046
[10] Feeser, H. R.; Willard, G. E., Founding strategy and performance: A comparison of high and low growth high-tech firms, Strategic Management Journal, 11, 2, 87-98 (1990)
[11] Garey, M. R.; Johnson, D. S., Computers and Intractability: A guide to the Theory of NP-Completeness (1979), Freeman: Freeman New York · Zbl 0411.68039
[12] Grimaldi, R.; Grandi, A., Business incubators and new venture creation: An assessment of incubating models, Te chnovation, 25, 2, 111-121 (2005)
[13] Head, C.; Shaban, S., A heuristic approach to simultaneous course/student timetabling, Computers and Operations Research, 34, 4, 919-933 (2007) · Zbl 1102.90022
[14] Juang, Y. S.; Lin, S. S.; Kao, H. P., An adaptive scheduling system with genetic algorithms for arranging employee training programs, Expert Systems with Applications, 33, 642-651 (2007)
[15] Lu, Z.; Hao, J. K., Adaptive Tabu Search for course timetabling, European Journal of Operational Research, 200, 1, 235-244 (2010) · Zbl 1190.90166
[16] Martocchio, J. J.; Baldwin, T. T., The evolution of strategic organizational training, Research in Personnel and Human Resources Management, 15, 1-46 (1997)
[17] Saidi-mehrabad, M.; Rezaei Sadrabadi, M.; Mohammadian, I., A new method to fuzzy modeling and its application in performance evaluation of tenants in incubators, International Journal of Advanced Manufacturing Technology, 37, 191-201 (2008)
[18] Salas, E.; Cannon-Bowers, J. A., The science of training: A decade of progress, Annual Review of Psychology, 52, 471-499 (2001)
[19] Tharenou, P.; Saks, A.; Moore, C., A review and critique of research on training and organizational-level outcomes, Human Resource Management Review, 17, 3, 251-273 (2007)
[20] Thompson, G. M., Using information on unconstrained student demand to improve university course schedules, Journal of Operations Management, 23, 2, 197-208 (2005)
[21] Valouxis, C.; Efthymios, H., Constraint programming approach for college timetabling, Computers and Operations Research, 30, 2, 1555-1572 (2003) · Zbl 1039.90039
[22] Wang, Y. Z., Using genetic algorithm methods to solve course scheduling problems, Expert Systems with Applications, 25, 39-50 (2003)
[23] Zahra, S. A.; Covin, J. G., Business strategy, technology policy, and firm performance, Strategic Management Journal, 14, 451-478 (1993)
[24] Zhang, D.; Liu, Y.; M’Hallah, R.; Leung, S. C.H., A simulated annealing with a new neighborhood structure based algorithm for high school timetabling problems, European Journal of Operational Research, 203, 3, 550-558 (2010) · Zbl 1177.90192
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