Towards net-zero manufacturing: carbon-aware scheduling for GHG emissions reduction

2025-03-03

AI Summary

To address the challenge of reducing Scope 2 emissions (indirect emissions from grid electricity consumption) in manufacturing’s net-zero transition, this paper proposes a carbon-aware flow shop scheduling method. The approach jointly models real-time grid carbon intensity dynamics and on-site distributed renewable energy generation within a mixed-integer linear programming (MILP) scheduling framework. A dedicated memetic algorithm—integrating evolutionary optimization with local search—is developed to efficiently solve the resulting complex optimization problem. Experimental results demonstrate that, while maintaining near-identical production efficiency, the proposed method reduces Scope 2 emissions by up to 27.4% compared to conventional energy- or makespan-oriented scheduling policies. This work constitutes the first effort to co-model grid carbon intensity and facility-level renewable generation and embed them directly into production scheduling. It provides a theoretically grounded, practically implementable methodology and technical pathway for low-carbon production planning in manufacturing.

Technology Category

• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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 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.

Problem

• Reducing Scope 2 GHG emissions in manufacturing
• Optimizing scheduling with grid carbon intensity
• Integrating on-site renewable electricity in scheduling

Innovation

• Carbon-aware scheduling reduces Scope 2 emissions.
• Mixed-integer linear model optimizes day-ahead schedules.
• Memetic algorithm combines evolutionary and local search.