Towards net-zero manufacturing: carbon-aware scheduling for GHG emissions reduction
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917005944160256 |
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| author | Mencaroni, Andrea Leyman, Pieter Raa, Birger De Vuyst, Stijn Claeys, Dieter |
| author_facet | Mencaroni, Andrea Leyman, Pieter Raa, Birger De Vuyst, Stijn Claeys, Dieter |
| contents | 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 manufacturing towards reducing the carbon footprint of its operations. Scope 2 emissions, which are the indirect emissions related to the production and consumption of grid electricity, 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-dependent carbon intensity of the grid and the availability of on-site renewable electricity.
This study introduces a carbon-aware permutation 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 generation mix and available on-site renewable electricity, along with the set of jobs to be scheduled and their corresponding power 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 computational experiments confirm that by considering the dynamic carbon intensity of the grid and on-site renewable electricity availability, substantial reductions in carbon emissions can be achieved. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01325 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards net-zero manufacturing: carbon-aware scheduling for GHG emissions reduction Mencaroni, Andrea Leyman, Pieter Raa, Birger De Vuyst, Stijn Claeys, Dieter Optimization and Control Neural and Evolutionary Computing 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 manufacturing towards reducing the carbon footprint of its operations. Scope 2 emissions, which are the indirect emissions related to the production and consumption of grid electricity, 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-dependent carbon intensity of the grid and the availability of on-site renewable electricity. This study introduces a carbon-aware permutation 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 generation mix and available on-site renewable electricity, along with the set of jobs to be scheduled and their corresponding power 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 computational experiments confirm that by considering the dynamic carbon intensity of the grid and on-site renewable electricity availability, substantial reductions in carbon emissions can be achieved. |
| title | Towards net-zero manufacturing: carbon-aware scheduling for GHG emissions reduction |
| topic | Optimization and Control Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2503.01325 |