Optimal allocation model of port emergency resources based on the improved multi-objective particle swarm algorithm...

Area of Science:

• Maritime Safety and Environmental Management
• Optimization Algorithms
• Operations Research

Background:

• Increasing maritime traffic and accidents necessitate effective emergency resource allocation in ports.
• Port emergency resource allocation is crucial for ensuring maritime safety and mitigating environmental damage.

Purpose of the Study:

• To develop an optimized allocation model for port emergency resources using an improved multi-objective particle swarm optimization (IMOPSO) algorithm.
• To enhance the efficiency and robustness of emergency resource allocation strategies in port environments.

Main Methods:

• Implementation of an improved multi-objective particle swarm optimization (IMOPSO) algorithm, incorporating crowding distance, improved archive update strategies, adjusted inertia weight, and a dynamic mutation operator.
• Application of the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method for optimal solution selection.
• Performance evaluation using generational distance (GD), spacing (SP), and delta indicator (Δ) metrics, with comparisons to the standard MOPSO algorithm.

Main Results:

• The proposed IMOPSO algorithm demonstrated superior performance and robustness compared to MOPSO, achieving average GD = 0.0386, SP = 0.0023, and Δ = 0.6468 on ZDT test functions.
• A case study on oil spill dispersant configuration at Zhanjiang Port yielded seven alternative schemes, with the optimal scheme identified via entropy-weighted TOPSIS.
• The optimal allocation scheme has the potential to reduce overall costs by approximately 33.03%.

Conclusions:

• The developed IMOPSO-based model provides an effective approach for optimizing port emergency resource allocation.
• The study offers valuable insights for water pollutant control and environmental management in port waters.
• The findings support enhanced maritime safety and cost-efficiency in emergency response planning.