Towards net-zero manufacturing: carbon-aware scheduling for GHG emissions reduction
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.
Code (0)
등록된 구현이 없습니다.
Tasks
SchedulingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling
Training large-scale machine learning models incurs substantial carbon emissions. Federated Learning (FL), by distributing computation across geographically dispersed clients, offers a natural framework to leverage regio…
Federated LearningCarbonEdge: Carbon-Aware Deep Learning Inference Framework for Sustainable Edge Computing
Deep learning applications at the network edge lead to a significant growth in AI-related carbon emissions, presenting a critical sustainability challenge. The existing edge computing frameworks optimize for latency and …
A Framework for SLO, Carbon, and Wastewater-Aware Sustainable FaaS Cloud Platform Management
Function-as-a-Service (FaaS) is a growing cloud computing paradigm that is expected to reduce the user cost of service over traditional serverful approaches. However, the environmental impact of FaaS has not received muc…
Cloud ComputingManagementSchedulingA Knowledge-driven Memetic Algorithm for the Energy-efficient Distributed Homogeneous Flow Shop Scheduling Problem
The reduction of carbon emissions in the manufacturing industry holds significant importance in achieving the national "double carbon" target. Ensuring energy efficiency is a crucial factor to be incorporated into future…
SchedulingDiffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization
This paper introduces DiffCarl, a diffusion-modeled carbon- and risk-aware reinforcement learning algorithm for intelligent operation of multi-microgrid systems. With the growing integration of renewables and increasing …
Reinforcement Learning