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486. Reducing scope 2 emissions through carbon-aware flow-shop scheduling
Invited abstract in session MD-23: Scheduling for sustainability, stream Circular Economy, Remanufacturing and Recycling .
Monday, 14:30-16:00Room: 82 (building: 116)
Authors (first author is the speaker)
| 1. | Andrea Mencaroni
|
| Industrial System Engineering and Product Design, Ghent University | |
| 2. | Dieter Claeys
|
| Industrial Systems Engineering and Product Design, Ghent University | |
| 3. | Stijn De Vuyst
|
| Industrial Systems Engineering and Product Design, Ghent University | |
| 4. | Birger Raa
|
| Industrial Systems Engineering and Product Design, Ghent University | |
| 5. | Pieter Leyman
|
| Industrial Systems Engineering and Production Design, Ghent University |
Abstract
Detailed scheduling has traditionally been optimized for the reduction of makespan and manufacturing costs. However, growing awareness of environmental concerns and increasingly stringent regulations are pushing industry towards reducing the carbon footprint of its operations. Scope 2 emissions, which are the indirect emissions related to the production and consumption of grid energy, are in fact estimated to be responsible for more than one-third of the global GHG emissions. In this context, carbon-aware scheduling can serve as a powerful way to reduce manufacturing’s carbon footprint by considering the time-sensitive carbon content of grid energy and the availability of on-site renewable energy.
This study introduces a carbon-aware flow-shop scheduling model designed to reduce scope 2 emissions. The model is formulated as a mixed-integer linear problem, taking into account the forecasted grid energy generation mix and available on-site renewable energy, along with the set of jobs to be scheduled and their corresponding energy requirements. The objective is to find an optimal day-ahead schedule that minimizes scope 2 emissions. The problem is addressed using a dedicated memetic algorithm, combining evolutionary strategy and local search.
Results from a comprehensive case study confirm that by considering the dynamic carbon content of grid energy and on-site renewable energy availability, significant reductions in carbon emissions can be achieved.
Keywords
- OR in Environment and Climate change
- Combinatorial Optimization
- Scheduling
Status: accepted
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