2026-08-14 –, Room 2
Our paper develops a nonlinear bi-objective optimization model to analyze cost-emission
trade-offs in maritime fleet operations. The model minimizes total fleet cost and total fleet
emissions through interactions between fuel share choices, digitization adoption, regulatory
frameworks, and operational decisions. We implement the model using the Julia program-
ming language with the JuMP modeling framework, employing the ε-constraint method
to generate a discrete approximation of the Pareto frontier. Results demonstrate that cost-
effective maritime decarbonization emerges from coordinated fuel transition, universal adop-
tion of digitization technologies, and regulatory-driven fleet reallocation, rather than from
isolated interventions. Sensitivity analysis across different digitization adoption modes re-
veals that unconstrained digitization serves as a low-cost enabler of emissions reduction.
The maritime shipping industry is responsible for approximately 3% of global GHG emissions, and 90% of world trade. The International Maritime Organization (IMO) has established an ambitious strategy targeting a 20% reduction in greenhouse gas (GHG) emissions by 2030, 70% by 2040, and full decarbonisation by 2050, relative to 2008 emission levels . However, by 2023 only a 3.6% reduction had been achieved. This gap motivates the need for optimization frameworks jointly optimizing fuel choices , digitization adoption, regulatory compliance while maintaining cost and emissions feasibility. We present a Julia-based mixed-integer non-linear programming (MINLP) model which optimizes fuel mix, digitization adoption binary variables, EEDI/EEXI constraints and regional assignment variables. The model is formulated as a bi-objective optimization model, minimizing both total systems costs and fleet emissions under given regulatory,demand,fuel and operational constraints.Optimization modelling has been carried out using Julia packages such as JuMP.jl, with Ipopt.jl for non-linear optimization, HiGHS.jl for mixed-integer components and MathOptInterface.jl as the solver interface .Dataframes has been used for data handling, and visualization of Pareto frontiers was carried out using Plots.jl has been used for plotting.A custom implementation of the augmented ε-constraint (AUGMECON) method is used to generate discrete approximations of cost–emissions Pareto frontiers. The talk focuses on Julia-implementation challenges in solving large scale MINLPs, and reproducible modeling workflows. Our work is primarily based on demonstrating the applications of the Julia programming language in the context of decarbonization in the transportation sector, highlighting the use of mathematical computing to solve real-world challenges.
I am a second-year undergraduate student at the Indian Institute of Management Ranchi, with a strong inclination towards Operations Research, logistics, and supply chain management, focusing on optimization modelling and applied mathematical computational methods.