This paper describes a two-phase simulation optimisation algorithm that integrates the genetic algorithm and response surface-based linear search algorithm for developing an optimal cyclic plan in a multi-echelon environment during the maturity phase of the life cycle of a product. The problem involves a search in a high-dimensional space with different ranges for decision variable scales, multiple objective functions and problem-specific constraints. The paper provides an illustrative example of the two-phase simulation optimisation algorithm applied to a generic supply chain network.
['Galina Merkuryeva', 'Liana Napalkova']
Two-phase simulation optimisation algorithm with applications to multi-echelon cyclic planning