We propose an inventory control model for an uncapacitated warehouse in a manufacturing facility under demand and lead time uncertainty. The objective is to make ordering decisions to minimize the total system cost. We introduce a two-stage tri-level optimization model with a rolling horizon to address the uncertain demand and lead time regardless of their underlying distributions. In addition, an exact algorithm is designed to solve the model. We compare this model in a case study with three decisionmaking strategies: optimistic, moderate, and pessimistic. Our computational results suggest that the performances of these models are either consistently inferior or highly sensitive to cost parameters (such as holding cost and shortage cost), whereas the new tri-level optimization model almost always results in the lowest total cost in all parameter settings.
AbstractWe propose an inventory control model for an uncapacitated warehouse in a manufacturing facility under demand and lead time uncertainty. The objective is to make ordering decisions to minimize the total system cost. We introduce a two-stage tri-level optimization model with a rolling horizon to address the uncertain demand and lead time regardless of their underlying distributions.In addition, an exact algorithm is designed to solve the model. We compare this model in a case study with three decision-making strategies: optimistic, moderate, and pessimistic. Our computational results suggest that the performances of these models are either consistently inferior or highly sensitive to cost parameters (such as holding cost and shortage cost), whereas the new trilevel optimization model almost always results in the lowest total cost in all parameter settings.
Driven by Renewable Portfolio Standards and Renewable Fuel Standard, biopower generation and biofuel production will increasingly compete for the same biomass resource over the next two decades. We use a linear programming model to study this competition as well as other interactions between the two policies. Our model describes the U.S. renewable energy portfolio by explicitly accounting for all major renewable energy resources, unique resource availability and policy requirements in all 50 states and Washington, DC, and policy deadlines set by all RPS and RFS2 policies within a 2013-2035 modeling horizon. Our modeling results were used to address five important questions regarding interactions between RPS and RFS2 and the impact on U.S. renewable energy portfolio.
AbstractDriven by Renewable Portfolio Standards and Renewable Fuel Standard, biopower generation and biofuel production will increasingly compete for the same biomass resource over the next two decades. We use a linear programming model to study this competition as well as other interactions between the two policies. Our model describes the U.S. renewable energy portfolio by explicitly accounting for all major renewable energy resources, unique resource availability and policy requirements in all 50 states and Washington D.C., and policy deadlines set by all RPS and RFS2 policies within a 2013-2035 modeling horizon. Our modeling results were used to address five important questions regarding interactions between RPS and RFS2 and the impact on U.S. renewable energy portfolio.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.