Theoretical Framework of Multi-Objective Simulation-Based Genetic Algorithm for Supply Chain Cyclic Planning and Optimisation

Proceedings of the 10th International Conference on Computer Modelling and Simulation (EUROSIM/UKsim-2008) 2008
Gaļina Merkurjeva, Liāna Napalkova

This paper develops a multi-objective simulationbased genetic algorithm (MOSGA) for multi-echelon supply chain cyclic planning and optimisation. The problem involves a search in high dimensional space with different ranges for decision variables scales, multiple objectives and problem specific constraints, such as power-of-two and nested/inverted-nested planning policies. In order to find the optimal solution, different parameters of genetic algorithm including the population sizing, crossover and mutation probabilities, selection and reproduction strategies and convergence criteria are investigated. For finding approximations of the Pareto optimal set, the nondominated sorting approach is used.

Keywords
Multi-echelon supply chains, cyclic planning, genetic algorithms, response surface-based linear search
DOI
10.1109/UKSIM.2008.105
Hyperlink
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4488977

Merkurjeva, G., Napalkova, L. Theoretical Framework of Multi-Objective Simulation-Based Genetic Algorithm for Supply Chain Cyclic Planning and Optimisation. In: Proceedings of the 10th International Conference on Computer Modelling and Simulation (EUROSIM/UKsim-2008), United Kingdom, Cambridge, 1-3 April, 2008. Cambridge: IEEE Computer Society, 2008, pp.467-474. ISBN 0-7695-3114-8. Available from: doi:10.1109/UKSIM.2008.105

Publication language
English (en)