0966861IerapetritouProduction planning and scheduling are decision making stages in process operations which have traditionally been practiced in a non-symbiotic manner. The full coupling of planning and scheduling models across the different temporal and spatial scales can substantially improve production operations. However, simultaneous modeling of planning and scheduling problems is currently plagued by its computational intensity, the uncertainty in model parameters and the inherently complex nature of manufacturing processes.Intellectual Merit: The PI plans on developing an integrated decision-making model that considers production planning and detailed short term scheduling to optimize the overall plant production capacity. By integrating planning and scheduling decision making levels the model becomes very complex and conventional solution methods turn out to be inefficient. She plans to develop an efficient modeling framework and advanced solution approaches based on decomposition principles to provide practical solutions to industry supplying alternative choices, but also meaningful research contributions in large-scale optimization. The main goal is to provide the decision maker solutions that are optimal in terms of production capacity and feasible in terms of detailed production scheduling. To achieve this target, the following ideas will be investigated:- Efficient ways to represent scheduling feasibility so that the integration with the planning problem will result in reasonable size model.- Efficient modeling approaches for planning and scheduling integration based on alternative ways to represent the scheduling feasibility and optimality objectives.- Solution methodologies for the integrated model based on decomposition methods.Broader Impact: The ultimate goal of this work is to optimize the production planning operations in large-scale, single-facility production plants considering the details of scheduling decisions. Realistic case studies from petrochemical, fast moving consumer goods manufacturing and chemical companies will be utilized to test and verify the results of the proposed analysis tools. The impact of the work goes beyond a specific industrial sector since the general ideas and findings can be applied or extended to different types of production facilities. The educational component of the work involves one graduate and two undergraduate students. Funding for the undergraduate students would be pursued through REU supplements. Students affiliated with program SUPER (Science for Undergraduates a Program for Excellence in Research) of Douglass College of Women will be actively involved with this project.
|Effective start/end date||2/1/10 → 1/31/13|
- National Science Foundation (National Science Foundation (NSF))