An Improved Artificial Bee Colony Algorithm for Solving Hybrid Flexible Flowshop With Dynamic Operation Skipping.

Area of Science:

• Operations Research
• Artificial Intelligence
• Manufacturing Systems Engineering

Background:

• The hybrid flexible flowshop scheduling problem (HFFSP) presents significant complexity in manufacturing systems.
• Dynamic operation skipping features in molten iron systems add further challenges to traditional scheduling approaches.

Purpose of the Study:

• To develop an improved discrete artificial bee colony (DABC) algorithm tailored for HFFSP with dynamic operation skipping.
• To enhance solution quality and computational efficiency for realistic production scenarios in molten iron systems.

Main Methods:

• A two-vector-based solution representation and a dynamic encoding mechanism were developed.
• A flexible decoding strategy and a problem-specific right-shift strategy were implemented.
• Skipping and scheduling neighborhood structures and an enhanced local search were integrated to balance exploration and exploitation.

Main Results:

• The proposed DABC algorithm achieved higher solution quality compared to existing algorithms.
• The algorithm demonstrated improved computational efficiency in solving the HFFSP.
• Statistical analysis confirmed the effectiveness of the DABC algorithm on realistic production instances.

Conclusions:

• The improved DABC algorithm is a highly effective approach for the hybrid flexible flowshop scheduling problem.
• The developed strategies significantly enhance the performance of the artificial bee colony algorithm for complex scheduling tasks.
• The findings offer practical improvements for scheduling in molten iron production systems.