Abstract
Over the past thirty years, optimization modeling techniques have begun to be actively used in supply chain planning and management. Given the specifics of planning tasks in supply chains, linear programming and its methods such as dynamic programming, stochastic programming and scenario planning have become the most popular. These methods make it possible to optimize the supply chain across numerous databases, each of which corresponds to a scenario describing different options for development in an uncertain future. Despite quite intensive research in this area, dynamic and stochastic programming is still underused by managers to solve application tasks in various fields, including supply chain management. Hence, there is a need for development of new planning models in logistics and supply chain management in the context of incomplete information and methods that are used to investigate situations of risk and uncertainty.
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