Abstract
This case study analyses different simulation-based optimisation methods of multi-echelon supply chain planning in the maturity phase of the product life cycle. Some standard optimisation software add-ons as well as the proposed model in the case study are used to solve the problem. A supply chain generic network is employed as an application system. Several optimisation scenarios are introduced in order to analyse and compare abilities of different optimisation methods and tools. A hybrid genetic-response surface-based linear search algorithm is introduced to enhance the solution of multi-echelon cyclic planning and optimisation problems and generate the optimal cyclic plan.
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References
Merkuryev Y, Merkuryeva G, Desmet B et al (2007) Integrating analytical and simulation techniques in multi-echelon cyclic planning. In: Proceedings first Asian international conference on modelling and simulation (AMS 2007), pp 460–464
Campbell GM, Mabert VA (1991) Cyclical schedules for capacitated lot sizing with dynamic demands. Manag Sci 37:409–427
Merkuryeva G, Napalkova L (2007) Development of simulation-based environment for multi-echelon cyclic planning and optimisation. In: Proceedings 6th EUROSIM congress on modelling and simulation
Abraham A, Jain L, Goldberg R (2005) Evolutionary multiobjective optimisation: theoretical advances and applications. Springer, New York
Azadivar F (1992) A tutorial on simulation optimization. In: Proceedings 1992 winter simulation conference, pp 198–204
Merkuryev Y, Visipkov V (1994) A survey of optimization methods in discrete systems simulation. In: Proceedings first joint conference of International Simulation Society, pp 104–110
Carson M, Maria A (1997) Simulation optimisation: methods and applications. In: Proceedings 1997 winter simulation conference, pp 118–126
Fu MC, Glover FW, April J (2005) Simulation optimisation: a review, new developments and applications. In: Proceedings 2005 winter simulation conference, pp 83–95
Konak A, Coit DW, Smoth AE (2006) Multi-objective optimisation using genetic algorithms: A tutorial. Reliab Eng Syst Saf 91:992–1007
Deb K, Pratap A, Agrawal S et al (2002) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimisation: NSGA-II. IEEE Trans Evol Computat 6:182–197
Goldberg DE (1985) Optimal initial population size for binary-coded genetic algorithms. In: Technical Report TCGA-850001, University of Alabama, USA
Merkuryeva G (2005) Response surface-based simulation meta-modelling methods with applications to optimisation problems. In: Dolgui A et al (eds) Supply chain optimisation product/process design, facility location and flow control. Springer, New York
Chopra S, Meindl P (2001) Supply chain management: strategy, planning and operation. Pearson Education, Upper Saddle River, NJ
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Merkuryeva, G., Napalkova, L. (2009). Supply Chain Cyclic Planning and Optimisation. In: Merkuryev, Y., Merkuryeva, G., Piera, M., Guasch, A. (eds) Simulation-Based Case Studies in Logistics. Springer, London. https://doi.org/10.1007/978-1-84882-187-3_6
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DOI: https://doi.org/10.1007/978-1-84882-187-3_6
Publisher Name: Springer, London
Print ISBN: 978-1-84882-186-6
Online ISBN: 978-1-84882-187-3
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