An Adaptive Robust Optimization Model for Parallel Machine Scheduling
Event details
| Date | 16.09.2021 |
| Hour | 14:00 › 16:00 |
| Speaker | Krzysztof Postek, Delft Institute of Applied Mathematics |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
Seminar organized by the Management of Technology & Entrepreneurship Institute
Title
"An Adaptive Robust Optimization Model for Parallel Machine Scheduling"
Speaker
Krzysztof Postek, Delft Institute of Applied Mathematics
Abstract
Real-life machine scheduling involves: (i) limited information about the exact task durations, and (ii) an opportunity to reschedule each time a task completed its processing and a machine becomes idle. Robust optimization is the natural methodology to cope with the first characteristic, yet the existing literature does not consider the possibility to adjust decisions as more information about the tasks' durations is revealed. This is despite that re-optimizing the schedule is a standard practice. We develop an approach that takes into account, at the beginning of the planning horizon, the possibility that scheduling decisions can be adjusted. We demonstrate that this can lead to better here-and-now decisions and develop the first exact MILP model for adjustable robust scheduling, and scalable heuristic. Using them, we show via a numerical study, that adjustable scheduling leads to solutions with better and more stable makespan realizations compared to static approaches.
Joint work with Shimrit Shtern (Technion) and Izack Cohen (Bar Ilan University).
Title
"An Adaptive Robust Optimization Model for Parallel Machine Scheduling"
Speaker
Krzysztof Postek, Delft Institute of Applied Mathematics
Abstract
Real-life machine scheduling involves: (i) limited information about the exact task durations, and (ii) an opportunity to reschedule each time a task completed its processing and a machine becomes idle. Robust optimization is the natural methodology to cope with the first characteristic, yet the existing literature does not consider the possibility to adjust decisions as more information about the tasks' durations is revealed. This is despite that re-optimizing the schedule is a standard practice. We develop an approach that takes into account, at the beginning of the planning horizon, the possibility that scheduling decisions can be adjusted. We demonstrate that this can lead to better here-and-now decisions and develop the first exact MILP model for adjustable robust scheduling, and scalable heuristic. Using them, we show via a numerical study, that adjustable scheduling leads to solutions with better and more stable makespan realizations compared to static approaches.
Joint work with Shimrit Shtern (Technion) and Izack Cohen (Bar Ilan University).
Practical information
- General public
- Free