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. We consider both discrete and continuous random parameters. In one research direction, we introduced the upper partial mean as a new measure of robustness and developed robust process planning algorithms under uncertainty. In another research direction, we develop approximation algorithms for two-stage stochastic integer programs.