3021. A bi-objective tramp ship routing and scheduling problem to accelerate shipping sustainability transition
Invited abstract in session TB-59: Transportation applications, stream Transportation.
Tuesday, 10:30-12:00Room: Liberty 1.14
Authors (first author is the speaker)
| 1. | Alberto Tamburini
|
| Management Engineering, DTU | |
| 2. | David Pisinger
|
| Management, DTU | |
| 3. | Aurore Wendling
|
| NTNU | |
| 4. | Nina Lange
|
| Department of Mathematical Sciences, University of Copenhagen |
Abstract
We consider a bi-objective Tramp Ship Routing and Scheduling Problem in which we plan routes for a fleet of tramp shipping vessels using a rich model. The two objective functions are maximizing profit and minimizing greenhouse gas (GHG) emissions. The rich model can handle speed optimization, chartering costs, and bunker planning. The single-objective model is efficiently solved using column generation, where the columns represent the possible routes of a vessel, while the master problem keeps track of the binding constraints. The pricing problems are solved using time-space graphs. The bi-objective model is solved iteratively using the ϵ-constraint method. The mathematical model is mainly composed of perfect matrices. We use this property to design an efficient solution method.
We compute the Pareto front for 10 different historical scenarios between 2021 and 2023, showing that emissions can be reduced on average by more than 10% with less than a 2% profit decrease and by 20% with a 5% profit decrease. Then, we compute the Pareto front with alternative fuels-engine combinations, obtaining emissions reduction of over 40% with less than a 5% profit decrease. For smaller vessels, we tested wind-assisted and wind-propelled alternatives. Finally, we compare the results with GHG tax levies, showing that these solutions are strongly dominated by the Pareto fronts we compute.
Keywords
- Multi-Objective Decision Making
- Column Generation
- Sustainable Development
Status: accepted
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