The model is a large-scale integer program involving thousands of variables and constraints. We use a multi-level optimization framework to decompose and solve the problem. Initial results with GHC algorithms required the design sequence to be fixed, with the algorithm used to identify optimal input parameter settings. This project introduces new neighborhood functions that allow GHC algorithms to be used to also identify the optimal design sequence. We develop a multilevel optimization framework for the problem to simultaneously address the various optimization models embedded in the problem.
Auxiliary models determine optimum skill groupings, travel routes, and repair kit compositions.
We develop a model for a gasoline supply chain that considers multimodal transportation, storage, and market activity. The optimization model is a multiperiod network flow model that is solved iteratively. Simulation is used to assess the effect of random disruptions in the supply network. As the chemical industry is becoming increasingly competitive, tools to hedge against uncertainty become increasingly important. The project develops a two-stage stochastic optimization approach to the problem of planning in the process industries.