Distributed Heterogeneous Co-Evolutionary Algorithm for Scheduling a Multistage Fine-Manufacturing System With Setup...

Area of Science:

• Manufacturing Systems Engineering
• Operations Research
• Computational Intelligence

Background:

• Modern manufacturing increasingly focuses on customization and personalization.
• Multistage fine-manufacturing systems are emerging to meet these demands.
• Scheduling these complex systems presents significant challenges.

Purpose of the Study:

• To address the novel distributed hybrid differentiation flowshop scheduling problem for the first time.
• To minimize the makespan (total completion time) in these customized manufacturing environments.
• To develop an effective and efficient algorithm for this complex scheduling task.

Main Methods:

• A novel distributed heterogeneous co-evolutionary algorithm (DHCA) was designed.
• The algorithm features heuristic initialization, dynamic subpopulation identity transit, and distributed heterogeneous global exploration.
• It incorporates adaptive problem-specific local exploitation strategies, including variable neighborhood and random block local search.

Main Results:

• Computational experiments validated the effectiveness of DHCA's specialized designs.
• DHCA demonstrated superior performance compared to state-of-the-art algorithms.
• The algorithm proved more effective and efficient in solving the distributed hybrid differentiation flowshop scheduling problem.

Conclusions:

• The proposed DHCA is a highly effective and efficient method for optimizing complex custom manufacturing schedules.
• The study successfully introduced and validated a novel approach to a previously unaddressed scheduling problem.
• DHCA offers a promising solution for minimizing makespan in multistage fine-manufacturing systems.