Theoretical Framework of Multi-objective Simulation-Based Genetic Algorithm for Supply Chain Cyclic Planning and Optimisation
Liāna Napalkova, Galina V. Merkuryeva · 2008
This paper develops a multi-objective simulation-based 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 non-dominated sorting approach is used. 1.