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- Title
Reinforcement Learning-Based Multi-Objective of Two-Stage Blocking Hybrid Flow Shop Scheduling Problem.
- Authors
Xu, Ke; Ye, Caixia; Gong, Hua; Sun, Wenjuan
- Abstract
Consideration of upstream congestion caused by busy downstream machinery, as well as transportation time between different production stages, is critical for improving production efficiency and reducing energy consumption in process industries. A two-stage hybrid flow shop scheduling problem is studied with the objective of the makespan and the total energy consumption while taking into consideration blocking and transportation restrictions. An adaptive objective selection-based Q-learning algorithm is designed to solve the problem. Nine state characteristics are extracted from real-time information about jobs, machines, and waiting processing queues. As scheduling actions, eight heuristic rules are used, including SPT, FCFS, Johnson, and others. To address the multi-objective optimization problem, an adaptive objective selection strategy based on t-tests is designed for making action decisions. This strategy can determine the optimization objective based on the confidence of the objective function under the current job and machine state, achieving coordinated optimization for multiple objectives. The experimental results indicate that the proposed algorithm, in comparison to Q-learning and the non-dominated sorting genetic algorithm, has shown an average improvement of 4.19% and 22.7% in the makespan, as well as 5.03% and 9.8% in the total energy consumption, respectively. The generated scheduling solutions provide theoretical guidance for production scheduling in process industries such as steel manufacturing. This contributes to helping enterprises reduce blocking and transportation energy consumption between upstream and downstream.
- Subjects
FLOW shop scheduling; ENERGY consumption in transportation; FLOW shops; INDUSTRIAL energy consumption; REINFORCEMENT learning; PRODUCTION scheduling; STEEL manufacture
- Publication
Processes, 2024, Vol 12, Issue 1, p51
- ISSN
2227-9717
- Publication type
Article
- DOI
10.3390/pr12010051