Rossi, Pier Cesare
(2017)
A Liner Shipping Speed Optimization Model to Reduce Bunker Cost and Pollutants Emitted.
[Laurea magistrale], Università di Bologna, Corso di Studio in
Ingegneria gestionale [LM-DM270]
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Abstract
Environmental impact has become one of the most relevant issues in liner shipping during the
recent years. Maritime shipping is responsible for the 2.7 per cent of the world CO2 emissions,
of which 25 per cent is attributable to container ships. This business also produces a significant
quantity of sulphur, a very dangerous substance for human health, especially if it is emitted in
areas next to the coast. At the same time, bunker cost represents the biggest portion of the
operational cost of a shipping company. Slow steaming is a cheap and effective strategy from both
save pollutants emissions and bunker cost. Moreover, it can be immediately put into practice. This
report introduces a Mixed Integer Programming Model to solve the Liner Shipping Routing and
Speed Optimization Problem (LSRSOP). The final goal is to find the best route and to optimize
the the sailing speed of the vessel considering the Emission Control Areas and maximum transit
times between ports. Two Heuristic Methods -the 2-Steps Method and the Simulated Annealingare
proposed to solve big instances that would require too much running time to be solved until
optimality. Both of them use a Hill-Climbing Algorithm that generates a slight different route from
a given one. A Bi-Objective Function Model has been designed for instances whose the optimal
solution can be found in reasonable time. It considers the operative cost of the vessel and the
external cost of emissions. The results show efficient solutions that are the "golden line" between
the most convenient solution for the company and the most sustainable solution.
Abstract
Environmental impact has become one of the most relevant issues in liner shipping during the
recent years. Maritime shipping is responsible for the 2.7 per cent of the world CO2 emissions,
of which 25 per cent is attributable to container ships. This business also produces a significant
quantity of sulphur, a very dangerous substance for human health, especially if it is emitted in
areas next to the coast. At the same time, bunker cost represents the biggest portion of the
operational cost of a shipping company. Slow steaming is a cheap and effective strategy from both
save pollutants emissions and bunker cost. Moreover, it can be immediately put into practice. This
report introduces a Mixed Integer Programming Model to solve the Liner Shipping Routing and
Speed Optimization Problem (LSRSOP). The final goal is to find the best route and to optimize
the the sailing speed of the vessel considering the Emission Control Areas and maximum transit
times between ports. Two Heuristic Methods -the 2-Steps Method and the Simulated Annealingare
proposed to solve big instances that would require too much running time to be solved until
optimality. Both of them use a Hill-Climbing Algorithm that generates a slight different route from
a given one. A Bi-Objective Function Model has been designed for instances whose the optimal
solution can be found in reasonable time. It considers the operative cost of the vessel and the
external cost of emissions. The results show efficient solutions that are the "golden line" between
the most convenient solution for the company and the most sustainable solution.
Tipologia del documento
Tesi di laurea
(Laurea magistrale)
Autore della tesi
Rossi, Pier Cesare
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
Liner Shipping,Heuristic,Metaheuristic,Travelling Salesman Problem,Bunker Cost,Cost of Emissions,Multiocbjective Function,Simulated Annealing,Speed Optimization Problem
Data di discussione della Tesi
6 Ottobre 2017
URI
Altri metadati
Tipologia del documento
Tesi di laurea
(NON SPECIFICATO)
Autore della tesi
Rossi, Pier Cesare
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
Liner Shipping,Heuristic,Metaheuristic,Travelling Salesman Problem,Bunker Cost,Cost of Emissions,Multiocbjective Function,Simulated Annealing,Speed Optimization Problem
Data di discussione della Tesi
6 Ottobre 2017
URI
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