A Memetic NSGA-III for Green Flexible Production with Real-Time Energy Costs & Emissions

TL;DR

This paper introduces a memetic NSGA-III algorithm that optimizes production scheduling considering real-time energy costs and emissions, enabling industries to adapt to dynamic energy markets for sustainable operations.

Contribution

It presents a novel multi-objective optimization method integrating real-time energy market data for flexible production scheduling, addressing multiple sustainability goals simultaneously.

Findings

Demonstrates potential savings in energy costs and emissions.

Shows effective trade-offs between makespan, costs, and emissions.

Validates approach with real market data and benchmark instances.

Abstract

The use of renewable energies strengthens decarbonization strategies. To integrate volatile renewable sources, energy systems require grid expansion, storage capabilities, or flexible consumption. This study focuses on industries that adapt production to real-time energy markets, offering flexible consumption to the grid. Flexible production considers not only traditional goals like minimizing production time, but also minimizing energy costs and emissions, thereby enhancing the sustainability of businesses. However, existing research focuses on single goals, neglects the combination of makespan, energy costs, and emissions, or assumes constant or periodic tariffs instead of a dynamic energy market. We present a novel memetic NSGA-III to minimize makespan, energy cost, and emissions, integrating real energy market data, and allowing manufacturers to adapt energy consumption to current grid conditions. Evaluating it with benchmark instances from literature and real energy market data, we explore the trade-offs between objectives, showcasing potential savings in energy costs and emissions on estimated Pareto fronts.