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A Hierarchical Optimization Algorithm With Dual-Cache Synced Tuning Mechanism for Distributed Flexible Job Shop

Fuqing Zhao, Junang Zhou, Ling Wang

IEEE Transactions on Cybernetics

|December 24, 2025

Summary

This summary is machine-generated.

A new hierarchical optimization algorithm with a dual-cache synced tuning mechanism (HOA-DSTM) effectively solves the complex distributed flexible job shop scheduling problem (DFJSP). This method enhances optimization efficiency and outperforms existing algorithms on benchmark datasets.

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Area of Science:

Operations Research
Industrial Engineering
Computer Science

Background:

Distributed manufacturing is a growing production paradigm.
The distributed flexible job shop scheduling problem (DFJSP) is a complex, NP-hard optimization challenge.
Existing methods struggle to efficiently solve large-scale DFJSP instances.

Purpose of the Study:

To propose a novel algorithm, the hierarchical optimization algorithm with a dual-cache synced tuning mechanism (HOA-DSTM), for solving the DFJSP.
To enhance the efficiency and effectiveness of optimization for distributed manufacturing systems.
To address the NP-hard nature of the DFJSP through a structured, two-stage approach.

Main Methods:

The HOA-DSTM employs a two-stage approach: an evolutionary stage and an optimization stage.
The evolutionary stage uses an elite retention strategy and a dual-reinforcement learning (dual-RL) mechanism to dynamically adjust crossover and mutation probabilities.
The optimization stage incorporates a local search with seven operators and a dual-cache synced tuning mechanism (DSTM) to refine the optimal solution by adjusting operation sequence and factory assignment.

Main Results:

The HOA-DSTM demonstrated superior performance compared to state-of-the-art algorithms on benchmark DFJSP datasets.
The dual-RL mechanism effectively improved optimization efficiency by adaptively tuning probabilities.
The DSTM component successfully refined solutions by leveraging the DFJSP encoding scheme's coupling characteristics.

Conclusions:

The proposed HOA-DSTM is a highly effective algorithm for solving the distributed flexible job shop scheduling problem.
The integration of elite retention, adaptive probability tuning via dual-RL, and targeted local search significantly enhances optimization capabilities.
HOA-DSTM offers a promising solution for optimizing complex scheduling tasks in distributed manufacturing environments.