Abstract
The continued drive to improve efficiency within the service operations sector is motivating the development of more sophisticated service chain planning tools to aid in longer term planning decisions. This involves optimising resource against expected demand and is critical for successful operations of service industries with large multi-skilled workforces, such as telecoms, utility companies and logistic companies. To effectively plan over longer durations a key requirement is the ability to simulate the effects any long term decisions have on the shorter term planning processes. For this purpose, a mathematical model encapsulating all the factors of the shorter term planning, such as skills, geographical constraints, and other business objectives was defined. Attempting to use conventional methods to optimise over this model highlighted poor scalability as the complexity increased. This has motivated the development of a heuristic method to provide near optimal solutions to the model in a shorter timescale. The specific problem we look at is that of matching resource to demand across the skill dimension. We design a genetic algorithm to solve this problem and show that it produces better solutions than a current planning approach, providing a powerful means to automate that process. We also show it reaching near optimal solutions in all cases, proving it is a feasible replacement for the poorly scaling linear model approach.
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References
Voudouris, C.: Defining and understanding service chain management. Service Chain Management, pp. 1–17. Springer (2008)
Owusu, G., O’Brien, P., McCall, J., Doherty, N.F.: Transforming Field and Service Operations. Springer, Stellenbosch (2013)
Shakya, S., Kassem, S., Mohamed, A., Hagras, H., Owusu, G.: Enhancing field service operations via fuzzy automation of tactical supply plan. Transforming Field and Service Operations, pp. 101–114. Springer (2013)
Owusu, G,. Anim-Ansah, G., Kern, M.: Strategic resource planning. Service Chain Management, pp. 35–49. Springer (2008)
Floudas, C.A., Lin, X.: Mixed integer linear programming in process scheduling: modeling, algorithms, and applications. Annals of Operations Research, pp. 131–162. Springer (2005)
Ashlock, D.: Evolutionary Computation for Modeling and Optimization, Springer, Heidelberg (2006)
Kordon, K.: Applying Computational Intelligence: How To Create Value, Springer, Berlin (2010)
Shakya, S., Santana, R.: A review of estimation of distribution algorithms and Markov networks. Markov Networks in Evolutionary Algorithms. Adaptation, Learning and Optimization. Series vol. 14, Springer, pp. 21–37 (2012)
Kiranyaz, S., Ince, T., Gabbouj, M.: Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition. Springer, Berlin (2014)
Dorigo, M., Stutzle, T., Ant Colony Optimization. MIT Press, Cambridge (2004)
Goldberg, D.: Genetic Algorithms in Search, Optimization amd Machine Learning. Addison-Wesley, Boston (1989)
Haupt, R.L., Haupt, S., Practical Genetic Algorithms, 2nd edn. Wiley, New York (2004)
Spears, W.M., De Jong, K.D.: On the virtues of parameterized uniform crossover. Naval Research Laboratory, Washington (1995)
Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. Foundations of Genetic Algorithms 1, pp. 69–93. Morgan Kaufmann Publishers, Inc. (1991)
Watchmaker framework for evolutionary computation. http://watchmaker.uncommons.org/. Accessed May 2015
CPLEX optimizer. http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/. Accessed Jan 2015
SCPSolver—an easy to use java linear programming interface. http://scpsolver.org/. Accessed Jan 2015
Voudouris, C., Owusu, G., Dorne, R., Lesaint, D.: Forecasting and demand planning. Service Chain Management, pp. 51–64. Springer (2008)
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Ainslie, R.T., Shakya, S., McCall, J., Owusu, G. (2015). Optimising Skill Matching in the Service Industry for Large Multi-skilled Workforces. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXII. SGAI 2015. Springer, Cham. https://doi.org/10.1007/978-3-319-25032-8_18
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DOI: https://doi.org/10.1007/978-3-319-25032-8_18
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