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A Case Study of Human Resource Allocation for Effective Hotel Management
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 Title & Authors
A Case Study of Human Resource Allocation for Effective Hotel Management
Murakami, Kayoko; Tasan, Seren Ozmehmet; Gen, Mitsuo; Oyabu, Takashi;
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The purpose of this study is to optimally allocate the human resources to tasks while minimizing the total daily human resource costs and smoothing the human resource usage. The human resource allocation problem (hRAP) under consideration contains two kinds of special constraints, i.e. operational precedence and skill constraints in addition to the ordinary constraints. To deal with the multiple objectives and the special constraints, first we designed this hRAP as a network problem and then proposed a Pareto multistage decisionbased genetic algorithm (P-mdGA). During the evolutionary process of P-mdGA, a Pareto evaluation procedure called generalized Pareto-based scale-independent fitness function approach is used to evaluate the solutions. Additionally, in order to improve the performance of P-mdGA, we use fuzzy logic controller for fine-tuning of genetic parameters. Finally, in order to demonstrate the applicability and to evaluate the performance of the proposed approach, P-mdGA is applied to solve a case study in a hotel, where the managers usually need helpful automatic support for effectively allocating hotel staff to hotel tasks.
Human Resource Allocation;Operational Precedence Constraint;Skill;Genetic Algorithm;Pareto Evaluation;Smoothing Resource Usage;
 Cited by
Alvarez-Valdes, R., Crespo, E., and Tamarit, J. M. (1999), Labour scheduling at an airport refueling installation, Journal of the Operational Research Society, 50, 211-218. crossref(new window)

Beasley, J. E. and Cao, B. (1998), A dynamic programming based algorithm for the crew scheduling problem, Computers and Operations Research, 25, 567-582. crossref(new window)

Brusco, M. J. and Jacobs, L. W. (2001), Starting-time decisions in labor tour scheduling: An experimental analysis and case study, European Journal of Operational Research, 131, 459-475. crossref(new window)

Brusco, M. J., Jacobs, L. W., Bongiorno, R. J., Lyons, D. V., and Tang, B. (1995), Improving personnel scheduling at airline stations, Operations Research, 43, 741-751. crossref(new window)

Easton, F. F. and Rossin, D. F. (1996), A stochastic goal program for employee scheduling, Decision Sciences, 27, 541-568. crossref(new window)

Easton, F. F. and Rossin, D. F. (1997), Overtime schedules for full-time service workers, Omega, 25, 285-299. crossref(new window)

Ernst, A. T., Jiang, H., Krishnamoorthy, M., and Sier, D. (2004), Staff scheduling and rostering: A review of applications, methods and models, European Journal of Operational Research, 153, 3-27. crossref(new window)

Gartner, J., Musliu, N., and Slany, W. (2001), Rota: A research project on algorithms for workforce scheduling and shift design optimization, AI Communications, 14, 83-92.

Gen, M. and Cheng, R. (2000), Genetic Algorithm and Engineering Optimization, John Wiley and Sons, NY.

Gen, M. and Cheng, R., and Lin, L. (2008), Network Models and Optimization: Multiobjective Genetic Algorithm Approach, Springer.

Hirano, K., Tasan, S. O., Gen, M., and Oyabu, T. (2008), Skill-based resource allocation problem by multistage decision-based genetic algorithm, Proceedings, Asia Conference on Intelligent Manufacturing and Logistics Systems, 463-471.

Ho, S.-Y., Shu, L.-S., and Chen, J.-H. (2004), Intelligent evolutionary algorithms for large parameter optimization problems, IEEE Transaction on Evolutionary Computation, 8, 522-541. crossref(new window)

Hussein, M. L. and Abo-Sinna, M. A. (1995), A fuzzy dynamic approach to the multicriterion resource allocation problem, Fuzzy Sets and Systems, 69, 115-124. crossref(new window)

Kwak, N. K. and Lee, C. (1997), A linear goal programming model for human resource allocation in a health-care organization, Journal of Medical Systems, 21, 129-140. crossref(new window)

Lee, Z. J., Su, S. F., Lee, C. Y., and Hung, Y. S. (2003), A heuristic genetic algorithm for solving resource allocation problems, Knowledge and Information Systems, 5, 503-511. crossref(new window)

Lin, C. K. Y., Lai, K. F., and Hung, S. L. (2000), Development of a workforce management system for a customer hotline service, Computers and Operations Research, 27, 987-1004. crossref(new window)

Lin, C. M. and Gen, M. (2008), Multi-criteria human resource allocation for solving multistage combinatorial optimization problems using multiobjective hybrid genetic algorithm, Expert Systems with Applications, 34, 2480-2490. crossref(new window)

Litsios, S. (1965), A resource allocation problem, Operations Research, 13, 960-988. crossref(new window)

Nammuni, K., Levine, J., and Kingston, J. (2002), Skillbased resource allocation using genetic algorithms and ontologies, Proceedings, International Workshop on Intelligent Knowledge Management Techniques, IOS Press, Amsterdam.

Rachmawati, L. and Srinivasan, D. (2005), A hybrid fuzzy evolutionary algorithm for a multi-objective resource allocation problem, Proceedings, 5th International Conference on Hybrid Intelligent Systems, 55-60.

Saaty, T. L., Peniwati, K., and Shang, J. S. (2007), The analytic hierarchy process and human resource allocation: Half the story, Mathematical and Computer Modeling, 46, 1041-1053. crossref(new window)

Wang, P. Y., Wang, G. S., and Hu, Z. G. (1997), Speeding up the search process of genetic algorithm by fuzzy logic, European Congress on Intelligent Techniques and Soft Computing, 665-671.

Yoshimura, M., Fujimi, Y., Izui, K., and Nishiwaki, S. (2006), Decision-making support system for human resource allocation in product development projects, International Journal of Production Research, 44, 831-848. crossref(new window)