An Improved Dung Beetle Optimizer for the Twin Stacker Cranes' Scheduling Problem

Area of Science:

• Operations Research
• Robotics
• Industrial Engineering

Background:

• Automated Storage and Retrieval Systems (AS/RSs) in shipyards utilize twin stacker cranes for efficiency.
• Collision avoidance in these systems incurs significant time costs, reducing overall efficiency.
• The twin stacker cranes' scheduling problem (TSSP) requires collision-free constraints.

Purpose of the Study:

• To develop a novel approach for collision identification and avoidance in TSSP.
• To formulate a mixed-integer programming model for TSSP with collision-free constraints.
• To propose an efficient heuristic optimization algorithm for large-scale TSSPs.

Main Methods:

• Approximating stacker crane trajectories as triangular envelopes for collision identification.
• Formulating TSSP as a mixed-integer programming problem.
• Developing an Improved Dung Beetle Optimizer (IDBO) with a double-layer code mechanism and hybrid strategies for TSSP.

Main Results:

• The proposed triangular envelope method effectively identifies potential collisions.
• The IDBO demonstrated stable advantages over classical algorithms, achieving over 10% improvement in solving TSSPs.
• Comparative experiments validated the IDBO's enhanced performance and component effectiveness.

Conclusions:

• The novel collision identification and avoidance approach effectively addresses TSSP constraints.
• The IDBO is a robust and efficient heuristic optimizer for large-scale, collision-free TSSP.
• This research significantly enhances the operational efficiency of AS/RSs in shipyard environments.