An Effective Cooperative Co-Evolutionary Algorithm for Distributed Flowshop Group Scheduling Problems

Area of Science:

• Operations Research
• Manufacturing Systems Engineering
• Computational Optimization

Background:

• Modern manufacturing systems face complex scheduling challenges.
• Distributed flowshop group scheduling is critical for optimizing production.
• Minimizing makespan is a key objective in these systems.

Purpose of the Study:

• To address the novel distributed flowshop group scheduling problem.
• To develop an efficient algorithm for minimizing makespan under group constraints.
• To enhance computational efficiency for complex scheduling problems.

Main Methods:

• Formulation of a mixed-integer linear programming model.
• Development of a cooperative co-evolutionary algorithm (CCEA).
• Introduction of a novel collaboration model and reinitialization scheme within CCEA.

Main Results:

• The proposed CCEA demonstrates high effectiveness.
• The novel collaboration and reinitialization schemes significantly improve performance.
• CCEA substantially outperforms established metaheuristics for this scheduling problem.

Conclusions:

• The developed CCEA is a superior approach for distributed flowshop group scheduling.
• The proposed enhancements offer significant computational advantages.
• This research provides a valuable tool for optimizing manufacturing operations.