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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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A Hyperheuristic With Q-Learning for the Multiobjective Energy-Efficient Distributed Blocking Flow Shop Scheduling

Fuqing Zhao, Shilu Di, Ling Wang

IEEE Transactions on Cybernetics

|August 22, 2022

Summary

This summary is machine-generated.

This study introduces a new method for energy-efficient distributed blocking flow shop scheduling. The developed hyperheuristic with Q-learning effectively optimizes energy consumption and production schedules for sustainable development.

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Area of Science:

Industrial Engineering
Operations Research
Sustainable Manufacturing

Background:

Production enterprises face increasing pressure to adopt sustainable development strategies, focusing on carbon peaking and carbon neutrality.
The distributed blocking flow shop scheduling problem (DBFSP) is a critical area for optimizing manufacturing processes.
Integrating energy consumption into DBFSP is essential for developing energy-efficient and environmentally responsible production systems.

Purpose of the Study:

To address the energy-efficient distributed blocking flow shop scheduling problem (EEDBFSP).
To develop a novel hyperheuristic approach that integrates Q-learning for optimizing both energy consumption and production schedules.
To improve the efficiency and sustainability of manufacturing operations.

Main Methods:

A hyperheuristic with Q-learning (HHQL) was developed to solve the EEDBFSP.
Q-learning was utilized to dynamically select low-level heuristics (LLHs) based on historical performance feedback.
An initialization method considering total tardiness (TTD) and total energy consumption (TEC) was proposed, alongside an epsilon-greedy strategy for LLH selection.
Job acceleration on critical paths and deceleration on non-critical paths were employed to optimize TTD and TEC, respectively.

Main Results:

The proposed HHQL algorithm demonstrated superior performance in solving the EEDBFSP compared to other algorithms.
The method effectively balances total tardiness and total energy consumption.
Extensive benchmark testing confirmed the efficiency and significance of the HHQL approach.

Conclusions:

The developed HHQL provides an effective solution for energy-efficient distributed blocking flow shop scheduling.
The integration of Q-learning and heuristic optimization strategies offers a promising direction for sustainable manufacturing.
The findings support the adoption of advanced scheduling techniques to meet national sustainable development goals.