Integrated tactical planning under uncertainty in the supply chain of divergent-type production systems

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The proposed research program investigates the robustness in supply chain tactical planning in divergent type production systems faced with uncertainty. Tactical planning in a supply chain consists of integrated planning of procurement, production, distribution and sale activities in a network including suppliers, production units, and distribution centers. The divergent-type production systems are featured by the one-to-many recipes. Such production systems are faced usually with different types of uncertainties. The traditional supply chain planning tools are based on deterministic optimization models. The plans proposed by deterministic approaches are not realistic and robust in the presence of future uncertain events and will usually lead to decreased demand fulfillment comparing the promised service level. Adopting robust planning tools can lead to significant improvements in service level, resource utilization, procurement and distribution costs, environmental effects, etc. The main goal of this research program is to develop efficient robust tactical planning models for the supply chain of divergent-type production systems in the presence of uncertainties, such that they can be applied in industry. Other objectives of this research consist of application of the proposed robust tactical planning tool for 1) the softwood supply chain, where we are facing with the non-homogeneity in the characteristics of logs, and 2) in a reverse supply chain focused on the recovery of end-of-life products with uncertain conditions. Stochastic programming and robust optimization approaches will be adopted in order to model the supply chain tactical planning problem under uncertainty. Efficient hybrid solution algorithms based on exact and heuristic methods will also be proposed to solve the resulting models. For each application, we aim to analyze and compare the two robust tactical planning tools in terms of required modeling and computational effort, validity of estimated uncertainty sets, quality of results, and implementation limitations. Whilst the proposed robust tactical planning tools are important for Canadian industries from both economical and social points of view, this research program will also advance the literature of stochastic optimization.