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

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 dimen- sional 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 opti- mal solution, different parameters of genetic algorithm including the population sizing, crossover and mu- tation 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.