A Stochastic Model for Implementing Postponement Strategies in Distribution Networks

An essential aspect of modern logistic concepts is the question how to deal with demand uncertainties. One concept that has been proposed but not yet fully explored in this context is postponement. In this concept the finalization of a product is procrastinated, i.e., the final products are not completed in single factories but in facilities in a distribution network that are located on the itinerary from factories to customers. Up to now, mainly global evaluations of pros and cons of postponement strategies are available. In order to allow for decision making for specific postponement implementations including its various effects in distribution networks, a double-stage stochastic mixed inte-ger linear programming (MIP) model is presented in this paper. This model supports managers to find out appropriate implementations of postponement strategies to manage demand uncertainties. By solving a set of problem instances it becomes obvious that the presented model formulation can be used to obtain solutions with (commercially) available mathematical programming software.