Dynamic Carbon Intensity Indicator (CII) Management in Stochastic Tramp Shipping Market

TL;DR

This paper presents a stochastic programming model for fleet deployment in tramp shipping, integrating CII regulation and operational uncertainties, revealing counterintuitive effects of current regulations and advantages of stochastic approaches.

Contribution

It introduces the first unified tactical planning framework incorporating key uncertainties and CII regulation, along with an efficient heuristic algorithm for practical decision-making.

Findings

Uncovers the 'CII paradox' where current regulations may increase emissions and reduce profits.

Demonstrates stochastic modeling narrows revenue gaps compared to deterministic methods.

Provides insights into operational impacts of environmental regulations on maritime fleet management.

Abstract

In the maritime sector, tramp shipping companies manage fleets to maximize profit while navigating market uncertainties. The International Maritime Organization (IMO) recently introduced the Carbon Intensity Indicator (CII) to reduce greenhouse gas emissions, further complicating deployment decisions. This paper introduces a novel two-stage stochastic programming model for long-term fleet deployment under market uncertainty and CII regulation. It is the first to integrate key operational uncertainties such as fuel prices, freight rates, and cargo demand into a unified tactical planning framework under CII regulation, simultaneously optimizing routing, cargo allocation, and speed. Furthermore, we develop an novel efficient heuristic algorithm that reliably converges to solutions within a 5\% optimality gap, enabling practical decision-support under uncertainty. Numerical analysis highlights two key findings based on our model: (1) It uncovers the ``CII paradox,'' a critical counterintuitive phenomenon where the present Supply-based CII regulation may increase total emissions significantly and drastically reduce profits. This challenges the conventional wisdom that stricter carbon-intensity rules invariably reduce emissions. (2) It demonstrates the advantage of stochastic modeling, showing that accounting for future uncertainties significantly narrows the revenue gap with perfect-foresight solutions, thereby offering superior economic performance over deterministic approaches. Collectively, these results deepen the understanding of environmental regulation's operational impacts and pave the way for more effective and sustainable fleet management strategies.