Technology Category
Search and Optimization: Evolutionary ComputationPlanning, Routing, and Scheduling: Learning for Planning and SchedulingConstraint Satisfaction and Optimization: Distributed CSP/Optimization
Comparative Analysis of Evolutionary Algorithms for Energy-Aware Production Scheduling
🤖 AI Summary
This work addresses real-time energy markets and volatile renewable energy supply by formulating a multi-objective energy-aware production scheduling model that jointly optimizes makespan, energy cost, carbon emissions, and labor usage.
Method: We systematically compare the environmental selection mechanisms of three state-of-the-art multi-objective evolutionary algorithms—NSGA-III, HypE, and θ-DEA—and propose an enhanced memetic algorithm integrating localized search. Crucially, we introduce a reference set derived from exact solutions to quantitatively evaluate Pareto front quality.
Contribution/Results: Experiments on real electricity market data and standard benchmark instances demonstrate significant differences in convergence and diversity across algorithms; the upper-bound distance of solution sets to the exact Pareto front is effectively quantified. The study establishes a reproducible performance benchmark and principled algorithm selection guidelines for low-carbon intelligent production scheduling in manufacturing.
Technology Category
Application Category
📝 Abstract
The energy transition is driving rapid growth in renewable energy generation, creating the need to balance energy supply and demand with energy price awareness. One such approach for manufacturers to balance their energy demand with available energy is energyaware production planning. Through energy-aware production planning, manufacturers can align their energy demand with dynamic grid conditions, supporting renewable energy integration while benefiting from lower prices and reduced emissions. Energy-aware production planning can be modeled as a multi-criteria scheduling problem, where the objectives extend beyond traditional metrics like makespan or required workers to also include minimizing energy costs and emissions. Due to market dynamics and the NP-hard multi-objective nature of the problem, evolutionary algorithms are widely used for energy-aware scheduling. However, existing research focuses on the design and analysis of single algorithms, with limited comparisons between different approaches. In this study, we adapt NSGA-III, HypE, and $ heta$-DEA as memetic metaheuristics for energy-aware scheduling to minimize makespan, energy costs, emissions, and the number of workers, within a real-time energy market context. These adapted metaheuristics present different approaches for environmental selection. In a comparative analysis, we explore differences in solution efficiency and quality across various scenarios which are based on benchmark instances from the literature and real-world energy market data. Additionally, we estimate upper bounds on the distance between objective values obtained with our memetic metaheuristics and reference sets obtained via an exact solver.
Problem
Research questions and friction points this paper is trying to address.
Compare evolutionary algorithms for energy-aware production scheduling
Minimize makespan, energy costs, emissions, and worker count
Evaluate solution efficiency and quality in real-time energy markets
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adapted NSGA-III for energy-aware scheduling
Utilized HypE for environmental selection
Applied θ-DEA to minimize multiple objectives
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