Work

Preference-Conditioned Reinforcement Learning for Energy-Aware Multi-Objective Flexible Job Shop Scheduling

Year: 2026

Type: article

Abstract: Rising energy costs and the increasing share of renewable generation create incentives to align production schedules with dynamic electricity prices and on-site solar generation. We address the resulting Multi-Objective Flexible Job Shop Scheduling Problem by proposing a deep reinforcement learning framework that jointly minimizes makespan and energy cost. A single preference-conditioned policy approximates the Pareto front at inference time, eliminating the need to train separate models for different objective weightings. The agent acts as a hyper-heuristic, selecting among heuristic actions at each decision point, including strategies that intentionally delay operations to exploit periods of lower electricity prices or higher solar generation. Preferences are integrated throughout the network via Feature-wise Linear Modulation, while a dual-critic architecture and a diversity loss preserve preference-specific policy behaviors. We evaluate the approach against NSGA-II and Joined Heuristics on synthetic instances ranging from 10×5×5 to 15×15×15 jobs, operations per job, and machines using normalized hypervolume and inverted generational distance. While NSGA-II performs best on the smallest instances, the proposed approach becomes increasingly competitive as problem size grows and achieves the best results on the largest evaluated instances. These findings indicate promising scalability within the investigated problem range.

Source: Applied Sciences

Authors: Dustin Moreira Simoes, Marvin Brune, Mehmet Ulrich, Hasan Tercan, Tobias Meisen

Institution: University of Wuppertal

Language: English

Topic: Scheduling and Optimization Algorithms

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,896

Funder: European Commission