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

TL;DR

This work tackles reducing scope 2 GHG emissions in manufacturing by introducing carbon-aware permutation flow-shop scheduling that accounts for time-varying grid carbon intensity and on-site renewable generation. It formulates the problem as a MILP with decision variables for operation starts, sequencing, and renewable usage, and defines emissions through $E_{im}^t$ and $C_t$ to capture grid and on-site dynamics. To solve large instances, the authors develop a dual random-key memetic algorithm (MA-CAS-PFSP) that encodes schedules with job-sequence and idle-time keys, uses crossover/mutation/local search, and applies penalties for infeasible schedules. Computational experiments on four benchmark-like datasets show that carbon-aware scheduling achieves substantial GHG reductions (up to $47.6\%$) with modest increases in makespan, outperforming MILP in larger instances and basic cost-focused objectives. The study demonstrates practical potential for integrating forecasted energy data into manufacturing scheduling, while outlining future directions like extending to flexible job shops and adaptive, real-time rescheduling. All mathematical notation is presented with $...$ delimiters.

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.

Figures (14)

• Figure 1: Example of five jobs with corresponding processing time and power requirements
• Figure 2: Grid carbon intensity and power generation mix
• Figure 3: Single-machine example
• Figure 4: Encoded and decoded individual for the single-machine example of Figure \ref{['fig:example_pauses_2']}
• Figure 5: Crossover on a single-machine example with $N=5$, $\chi_j=0.4$, and $\chi_p=0.5$