Publications

Aamir, Munaf S.; Ballantine, Marissa D.; Taylor, Robert A.; Flory, John A.

Abstract not provided.

Backlund, Peter B.; Melander, Darryl J.; Pierson, Adam J.; Flory, John A.; Dessanti, Alexander D.; Henry, Stephen M.; Gauthier, John H.

Schedule Management Optimization (SMO) is a tool for automatically generating a schedule of project tasks. Project scheduling is traditionally achieved with the use of commercial project management software or case-specific optimization formulations. Commercial software packages are useful tools for managing and visualizing copious amounts of project task data. However, their ability to automatically generate optimized schedules is limited. Furthermore, there are many real-world constraints and decision variables that commercial packages ignore. Case-specific optimization formulations effectively identify schedules that optimize one or more objectives for a specific problem, but they are unable to handle a diverse selection of scheduling problems. SMO enables practitioners to generate optimal project schedules automatically while considering a broad range of real-world problem characteristics. SMO has been designed to handle some of the most difficult scheduling problems -- those with resource constraints, multiple objectives, multiple inventories, and diverse ways of performing tasks. This report contains descriptions of the SMO modeling concepts and explains how they map to real-world scheduling considerations.

Flory, John A.; Padilla, Denise D.; Gauthier, John H.; Nelson, April M.; Miller, Steven P.

Upcoming weapon programs require an aggressive increase in Application Specific Integrated Circuit (ASIC) production at Sandia National Laboratories (SNL). SNL has developed unique modeling and optimization tools that have been instrumental in improving ASIC production productivity and efficiency, identifying optimal operational and tactical execution plans under resource constraints, and providing confidence in successful mission execution. With ten products and unprecedented levels of demand, a single set of shared resources, highly variable processes, and the need for external supplier task synchronization, scheduling is an integral part of successful manufacturing. The scheduler uses an iterative multi-objective genetic algorithm and a multi-dimensional performance evaluator. Schedule feasibility is assessed using a discrete event simulation (DES) that incorporates operational uncertainty, variability, and resource availability. The tools provide rapid scenario assessments and responses to variances in the operational environment, and have been used to inform major equipment investments and workforce planning decisions in multiple SNL facilities.

Flory, John A.; Padilla, Denise D.; Gauthier, John H.; Nelson, April M.; Miller, Steven P.

Abstract not provided.