Shipping Schedule Agreements for Emissions and Service Frequency
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Solution Overview
Problem
Current shipping emission reduction techniques are inadequate to meet long-term greenhouse gas emission reduction targets, and existing collaborative agreements between shipping vessels and terminal operators are limited in scope and effectiveness.
Innovation Solution
A method and system for optimizing schedule design through real-time facilitation of collaborative agreements between shipping vessels and terminal operators, utilizing a computing device to calculate, model, and evaluate economic and environmental metrics to automatically facilitate optimal contract components, allowing for flexible and efficient agreement adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If collaborative agreements are facilitated between shipping vessels and terminal operators, then service frequency and economic performance are improved, but system complexity and negotiation time increase
Solution Approach 1:
An automated facilitation system acts as an intermediary between shipping vessels and terminal operators, managing the complex negotiation process. The system automatically generates, evaluates, and adjusts contract components based on multiple objectives, reducing the burden on human negotiators while improving service frequency through optimized scheduling.
Solution Approach 2:
The collaborative agreement system dynamically adjusts contract components in real-time based on changing conditions such as service frequency requirements, emission targets, and economic performance metrics. This dynamic adaptation allows the system to handle complexity while maintaining flexibility and responsiveness to operational needs.
2Object-affected harmful factors
If multiple objective preferences are incorporated into agreements, then environmental sustainability is improved, but agreement complexity and processing time increase
Solution Approach 1:
The system pre-establishes multiple objective preferences including environmental sustainability targets, economic performance criteria, and service frequency requirements before negotiations begin. These pre-defined objectives guide the automated facilitation process, reducing processing time by avoiding ad-hoc decision-making while still achieving comprehensive environmental goals.
Solution Approach 2:
The system manages multiple objective preferences by dynamically adjusting parameters such as emission reduction targets, service frequency levels, and economic performance thresholds. This parameter-based approach allows environmental sustainability to be improved through systematic optimization without proportionally increasing processing time, as the system efficiently balances competing parameters.
3Adaptability or versatility
If standard contract components are converted to objective preferences, then agreement flexibility is improved, but computational requirements and processing complexity increase
Solution Approach 1:
The system dynamically converts standard contract components into adjustable objective preferences during the negotiation process. This dynamic transformation allows agreement flexibility to be improved as the system can adapt contract terms based on real-time conditions, while computational complexity is managed through automated algorithms that efficiently handle the conversions.
Solution Approach 2:
Standard contract components are converted into parameter-based objective preferences that can be systematically adjusted. This approach improves agreement flexibility by allowing continuous optimization of terms such as service frequency, emission targets, and economic metrics, while computational requirements are managed through structured parameter optimization rather than unstructured negotiation.
Data Source
AI summary
Described herein relates to a system of and method for optimizing schedule design, via facilitation of agreements, such as environmentally sustainable and/or multi-objective collaborative agreements, between at least one shipping vessel and at least one terminal operator. The present disclosure may include at least one multi-objective mathematical model to capture the proposed collaborative agreements. The first objective of the model may minimize the cost components that are mostly driven by the economic perspectives, while the second objective may minimize emission release components driven by the environmental perspectives. The present disclosure comprises a new multi-objective optimization method comprising the features of the ε-constraint model method and the goal-programming model method. In addition, the present disclosure may facilitate am analysis of trade-offs amongst the economic and environmental perspectives in the ship schedule design. Furthermore, the importance of environmentally sustainable collaborative agreements amongst shipping lines and terminal operators may be showcased as well.