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UID:pretalx-juliacon-2026-NAPBCA@pretalx.com
DTSTART;TZID=CET:20260814T101500
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DESCRIPTION:Our paper develops a nonlinear bi-objective optimization model 
 to analyze cost-emission\ntrade-offs in maritime fleet operations. The mod
 el minimizes total fleet cost and total fleet\nemissions through interacti
 ons between fuel share choices\, digitization adoption\, regulatory\nframe
 works\, and operational decisions. We implement the model using the Julia 
 program-\nming language with the JuMP modeling framework\, employing the 
 ε-constraint method\nto generate a discrete approximation of the Pareto f
 rontier. Results demonstrate that cost-\neffective maritime decarbonizatio
 n emerges from coordinated fuel transition\, universal adop-\ntion of digi
 tization technologies\, and regulatory-driven fleet reallocation\, rather 
 than from\nisolated interventions. Sensitivity analysis across different d
 igitization adoption modes re-\nveals that unconstrained digitization serv
 es as a low-cost enabler of emissions reduction.
DTSTAMP:20260502T104012Z
LOCATION:Room 2
SUMMARY:Modelling Cost-Sustainability Trade-offs in Maritime Logistics: EED
 I-Driven Multi-Objective Optimization - Jia Bhanushali
URL:https://pretalx.com/juliacon-2026/talk/NAPBCA/
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