A goal-programming based optimal port docking scheme under COVID-19.

Area of Science:

• Operations Research
• Maritime Logistics
• Supply Chain Management

Background:

• The Covid-19 pandemic has severely impacted global ports, leading to significant congestion.
• This congestion increases docking costs, delays cargo operations, and affects product pricing and timely supply.
• Existing models lack the comprehensiveness to address large-scale, multi-objective port docking challenges.

Purpose of the Study:

• To develop a novel mixed integer programming model for optimizing ship docking in ports.
• To create a comprehensive model that incorporates multiple essential objectives for efficient port operations.
• To provide a scalable and applicable solution for real-world port docking and complex queuing problems.

Main Methods:

• Development of a mixed integer programming model for optimal ship docking.
• Incorporation of four key objectives: minimizing ship waiting time, balancing berth workload, ensuring prompt task completion, and meeting berthing time expectations.
• Demonstration of model scalability with potential for tens of thousands of decision variables and millions of constraints.

Main Results:

• The proposed model successfully generates optimal docking schemes tailored to various objectives.
• Validation through relevant examples confirms the model's effectiveness in different scenarios.
• The model's capacity to handle large-scale problems is demonstrated, indicating its suitability for real-world application.

Conclusions:

• The developed model offers an effective solution for improving port docking efficiency.
• It provides a viable approach for managing complex queuing systems with multiple service points.
• The model's adaptability makes it applicable to diverse logistical and operational challenges beyond port congestion.