Skip to main content

A multi-objective mathematical model to select fleets and maritime routes in short sea shipping: a case study in Chile

Abstract

This paper proposes a mathematical model for intermodal chains with seaborne transport, in which the optimization of a multi-objective model enables conflicting objectives to be handled simultaneously. Through the assessment of ‘door-to-door’ transport in terms of costs, time, and environmental impact, the most suitable maritime route and the optimized fleet are jointly proposed to maximize the opportunities for success of intermodal chains versus trucking. The NSGA-II algorithm is applied to resolve the model. The Pareto fronts obtained not only permit decision-making in the short-term but also enable long-term strategies to be defined according to the behaviour of these frontiers when sensitivity analysis is undertaken. A real-life case in Chile is studied to test the usefulness of the model. Aside from identifying the most suitable Motorway of the Sea with its optimized fleet for Chile, the application case has provided several significant findings to promote the intermodal option regardless of its location.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Notes

  1. 1.

    Ministry of Transportation of Chile (https://www.sepchile.cl/documentacion/estadisticas-portuarias/?no_cache=1).

  2. 2.

    National Institute of Statistics of Chile. 2013. Yearbook about Road Transport. https://www.ine.cl/canales/chile_estadistico/estadisticas_economicas/transporte_y_comunicaciones/encuesta-estructural-transporte-carretera/2013/infografia_transporte_por_carretera_2015.pdf

  3. 3.

    https://www.shipowners.dk/en/services/beregningsvaerktoejer/

  4. 4.

    Maritime Safety Committee.

  5. 5.

    DIRECTEMAR (2012); Ministry of Transport and Telecommunications of Chile and Ports (https://web.directemar.cl/estadisticas/puertos/default.htm).

  6. 6.

    Under Secretary of Transport, Ministry of Transport and Telecommunications of the Government of Chile. 2011. Analysis of the Costs and Competitiveness of the Modes of Inter-urban Land Transport (2011). https://www.subtrans.cl/subtrans/doc/Informefinalcorregido.pdf

  7. 7.

    https://www.saam.com/en/

References

  1. 1.

    Brooks MR, Frost JD (2004) Short sea shipping: a Canadian perspective. Maritime Policy Manag 31(4):393–407

    Article  Google Scholar 

  2. 2.

    Brooks MR, Sánchez R, Wilmsmeier G (2014) Developing short sea shipping in South America—looking beyond traditional perspectives. Ocean Yearb 28:495–526

    Article  Google Scholar 

  3. 3.

    Brooks M, Wilmsmeier G (2017) A Chilean maritime highway: is it a possible domestic transport option? Transp Res Rec 2611:32–40

    Article  Google Scholar 

  4. 4.

    Puckett S, Hensher D, Brooks M, Trifts V (2011) Preferences for alternative short sea shipping opportunities. Transp Res Part E 47(2011):182–189

    Article  Google Scholar 

  5. 5.

    Bendall H, Brooks M (2011) Short sea shipping: lessons for or from Australia. Int J Shipp Transp Log (IJSTL) 3(4):2011

    Google Scholar 

  6. 6.

    Chang Y, Lee P, Kim H (2010) Optimization model for transportation of container cargoes considering short sea shipping and external cost. South Korean Case’ Transp Res Rec J Transp Res Board 2166(1):99–108

    Article  Google Scholar 

  7. 7.

    Lee P, Hu KC, Chen T (2010) External costs of domestic container transportation: short-sea shipping versus trucking in Taiwan. Transp Rev A Transl Transdiscip 30(3):315–335

    Google Scholar 

  8. 8.

    Suárez-Alemán A, Trujillo L, Medda F (2015) Short Sea shipping and Intermodal competition’. Maritime Policy Manag 42(4):317–334

    Article  Google Scholar 

  9. 9.

    Baird A (2007) The economics of Motorways of the Sea. Maritime Policy & Management 34(4):287–310

    Article  Google Scholar 

  10. 10.

    Gesé X, Baird A (2013) Motorways of the sea policy in Europe. Maritime Policy Manag 40(1):10–26

    Article  Google Scholar 

  11. 11.

    Medda F, Trujillo L (2010) Short Sea Shipping: an analysis of its determinants. Maritime Policy Manag 37(3):285–303

    Article  Google Scholar 

  12. 12.

    Cullinane K, Cullinane S (2013) Atmospheric emissions from shipping: The need for regulation and approaches to compliance. Transp Rev A Transnat Transdiscip 33(4):377–401

    Google Scholar 

  13. 13.

    Bengtsson S, Fridell E, Andersson K (2014) Fuels for short sea shipping: comparative assessment with focus on environment impact. Proc IMechE 228(1):44–54

    Article  Google Scholar 

  14. 14.

    Hjelle MH (2010) Short sea shipping’s green label at risk. Transp Rev A Transl Transdiscip 30(5):617–640

    Google Scholar 

  15. 15.

    Hjelle MH, Fridell E (2012) When is short sea shipping environmentally competitive? In: Oosthuizen J (ed) Environmental health – emerging issues and practice, Croatia: InTech

  16. 16.

    Daganzo C (2005) ‘Many to many distribution’ in Daganzo C. Logistic systems Analysis. Springer, Berlin

    Google Scholar 

  17. 17.

    Martínez-López A, Caamaño P, Castro L (2015) Definition of optimal fleets for Sea Motorways: the case of France and Spain on the Atlantic coast. Int J Shipp Transp Log 7(1):89–113

    Google Scholar 

  18. 18.

    Martínez-López A, Caamaño P, Míguez M (2016) Influence of external costs on the optimisation of container fleets by operating under motorways of the sea conditions. Int J Shipp Transp Log 8(6):653–686

    Google Scholar 

  19. 19.

    Wang S, Meng Q (2012) Sailing speed optimization for container ships in a liner shipping network. Transp Res Part E 48(2012):701–714

    Article  Google Scholar 

  20. 20.

    Kim J-G, Kim H-J, Lee PT-W (2013) Optimising containership speed and fleet size under a carbon tax and an emission trading scheme’. Int J Ship Transp Log 5(6):571–590

    Google Scholar 

  21. 21.

    Chandra S, Christiansen M, Fagerholt K (2016) Combined fleet deployment and inventory management in roll-on/roll-off shipping. Transp Res Part E 92(2016):43–55

    Article  Google Scholar 

  22. 22.

    Mansouri SA, Lee H, Aluko O (2015) Multi-objective decision supports to enhance environmental sustainability in maritime shipping: a review and future directions. Transp Res Part E 78(2015):3–18

    Article  Google Scholar 

  23. 23.

    Wong EY, Yeung HS, Lau HY (2009) Immunity-based hybrid evolutionary algorithm for multi-objective optimization in global container repositioning. Eng Appl Artif Intell 22(6):842–854

    Article  Google Scholar 

  24. 24.

    Wong EYC, Lau HY, Mak KL (2010) Immunity-based evolutionary algorithm for optimal global container repositioning in liner shipping. Oper Res Decis Theory OR Spectr 32(3):739–763

    MATH  Google Scholar 

  25. 25.

    Chen G, Govindan K, Golias MM (2013) Reducing truck emissions at container terminals in a low carbon economy: proposal of a queueing-based bio-objective model for optimizing truck arrival pattern. Transp Res Part E 55(2013):3–22

    Article  Google Scholar 

  26. 26.

    Hu Q, Hu Z, Du Y (2014) Berth and quay-crane allocation problem considering fuel consumption and emissions from vessels. Comput Ind Eng 70:1–10

    Article  Google Scholar 

  27. 27.

    Usabiaga J, Castells M, Martínez X, Olcer A (2013) A simulation model for road and maritime environmental performance assessment. J Environ Protect 4(7):683–693

    Article  Google Scholar 

  28. 28.

    Lupi M, Farina A, Orsi D, Pratelli A (2017) The capability of Motorways of the Sea of being competitive against road transport. The case of the Italian mainland and Sicily. J Transp Geograph 58:9–21

    Article  Google Scholar 

  29. 29.

    Baykasoglu A, Subulan K (2016) A multi-objective sustainable load planning model for intermodal transportation networks with a real-life application. Transp Res Part E 95(2016):207–247

    Article  Google Scholar 

  30. 30.

    Martínez-López A, Caamaño P, Chica M, Trujillo L (2018) Optimization of a container vessel fleet and its propulsion plant to articulate sustainable intermodal chains versus road transport. Transp Res Part D 59(2018):134–147

    Article  Google Scholar 

  31. 31.

    Ntziabchristos and Samaras (2012) EMEP/EEA air pollutant emission inventory guidebook 2009 (updated May, 2012). Chapter: Passenger cars, light duty trucks, heavy duty vehicles including buses and motorbikes. The European Environment Agency

  32. 32.

    Jiang L, Kronbak J (2012) The model of maritime external costs Work Package 1, report nº06, June 2012. Project n 2010 56:Emissionsbeslutingsstottesystem.Copenhagen: University of Southern Denmark

  33. 33.

    Maibach M, Schreyer C, Sutter D, Essen HPV, Boon BH, Smokers R, Schrote A, Doll C, Pawlowska BBM (2008) Handbook on estimation of external costs in the transport sector. CE Delft, Delft

    Google Scholar 

  34. 34.

    Kristensen H (2012) Energy demand and exhaust gas emissions of ships. Work Package 2 of Project Emissionsbeslutningsstottesystem. Copenhagen: Technical University of Denmark

  35. 35.

    Lützen M, Kristensen H (2012) A model for prediction of propulsion power and emissions tankers and bulk carriers. In: Proceedings of World Maritime Technology Conference, 2012. Saint-Petersburg, Russia

  36. 36.

    Caamaño P, Tedin R, Paz-Lopez A, Becerra JA (2010) JEAF: a Java evolutionary algorithm framework. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp 1–8

  37. 37.

    Zitzler E, Deb K, Thiele L (2000) Comparison of multi-objective evolutionary algorithms: empirical results. Evolut Comput 8(2):173–195

    Article  Google Scholar 

  38. 38.

    Grosso M, Lynce A, Silla A (2010) Short Sea Shipping, intermodality and parameters influencing pricing policies: the Mediterranean case. NETNOMICS Econ Res Electron Netw 11(1):47–67

    Article  Google Scholar 

  39. 39.

    Hjelle MH (2014) Atmospheric emissions of short sea shipping compared to road transport though the peaks and troughs of short-term market cycles. Transp Rev A Transl Transdiscip 34(3):379–395

    Google Scholar 

  40. 40.

    Jiang L, Kronbak J, Christensen L (2014) The cost and benefits of sulphur reduction measures: sulphur scrubbers versus marine gas oil. Transp Res Part D 28:19–27

  41. 41.

    Lagoudis IN, Fragkos SN, Litinas NA (2010) Estimating optimum container and vessel fleet sizes in a cyclic liner service using a holistic approach. Int J Shipp Transp Log 2(1):4–21

    Google Scholar 

  42. 42.

    Martínez-López A, Munín DA, García-Alonso L (2015) A multi-criteria decision method for the analysis of the motorways of the sea: the application to the case of France and Spain on the Atlantic coast. Maritime Policy Manag 42(6):608–631

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Alba Martínez-López.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix A

Subscripts

A = {1,...,a}: Different legs for the intermodal chains: capillary hauls (road haulage in both costs) and the trunk haul (maritime route).

BB = {1,...,b}: Installation of bow thruster: yes or no.

C = {1,...,c}: Cost inputs to reach the minimum required freight: depreciation costs, financing costs, insurance costs, maintenance costs, crew costs, fuel costs, and port tariff costs (ship dues, cargo dues, pilot tariff, towing tariff, mooring dues, and loading/unloading costs).

DD = {1,...,d}: Final destinations on land (nodes). For the transport network of the application case, Iquique and Antofagasta are used in the northern region and Concepción and Temuco are used in the southern region.

EE = {1,...,e}: Kind of main engines: diesel engines and turbines.

GG = {1,...,g}: Cargo-handling systems: vessel cranes or port cranes.

H = {1,...,h}: Possible propellers: screws or waterjets.

I = {1,...,i}: Number of main engines.

J = {1,...,j}: Direction for the transport (north–south and south–north).

K = {1,...,k}: Possible spoke ports. For the application case, they are Arica, Iquique, Mejillones, and Antofagasta in the northern region and San Vicente and Coronel in the southern region.

M = {1,...,m}: Possible hub ports. For the application case, they are Valparaíso and San Antonio in the V region.

N = {1,...,n}: Number of shaft lines in the machine room.

PP = {1,...,p}: Types of cargo units for container vessels: TEUs and FEUs.

SS = {1,...,s}: Stages during maritime transport: free sailing, maneuvering (pilotage time, towing time, and mooring time), and berthing (loading and unloading operations).

U = {1,...,u}: Group of evaluated pollutants: SO2, NOx, PM2.5, and CO2.

V = {1,...,v}: Classification of the zones according to the harmful impact of the emissions: metropolitan zone and urban zone.

WW = {1,...,w}: Port services for the maneuvering stage: pilotage, towing, and mooring services.

Z = {1,...,z}: The origins on land (nodes). For the transport network of the application case, in the central region, Santiago, Valparaíso hub port (Valparaiso or San Antonio), La Serena, and Rancagua are used.


Superscripts.

ST = {c}: The MoS analyzed: MoS North and MoS South.

DIS = {d}: Compulsory driving with two drivers (yes and no).


Variables.

CF1u: Unitary costs for the pollutants during free sailing (€/); ∀u ∈ U.

CFsuv: Unitary costs for the pollutants considering the maritime stages and the affected zones (€/); ∀s ∈ SS∧ ∀u ∈ U∧ ∀v ∈ V.

CTc: Cost of the items for the maritime required freight of the trunk haul (€); ∀c ∈ C.

CKdp: Unitary cost per kilometre by road (unimodal; the value is dependent on the required number of drivers and the cargo unit transported (€/km)); ∀p ∈ PP∧ ∀d ∈ DIS.

CMU: Overall transport costs for the intermodal chain (€/(t × trip)).

CU: Overall transport costs for the unimodal alternative (€/(t × trip)).

DMmk: Maritime distance for the trunk haul (km); ∀m ∈ M ∧∀k ∈ K.

DRazd: Road distance for the unimodal alternative (km); ∀z ∈ Z ∧ ∀d ∈ DD.

DRbzm: Road distance for the capillary hauls in the intermodal chains from/to hub ports (km); ∀z ∈ Z ∧ ∀m ∈ M.

DRbkd: Road distance for the capillary hauls in the intermodal chains from/to spoke ports (km); ∀k ∈ K ∧ ∀d ∈ DD.

EGsu: Emission coefficients for container vessels during the different maritime stages (kg/nm and in kg/h); ∀s ∈ SS ∧ ∀u ∈ U.

EGUu: Emission coefficients for trucking (gr of pollutant/kg of fuel consumed); ∀u ∈ U.

FCp: Fuel consumption for trucks by considering the cargo unit transported (gr fuel/km); ∀p ∈ PP.

CNk: Number of cranes per vessel ∀k ∈ K.

CNm: Number of cranes per vessel ∀m ∈ M.

MUE: Environmental costs for the intermodal chains (€/(t × trip)).

RE: Environmental costs (€/(t × trip)) for the road transport.

REa: Environmental costs (€/(t × trip)) for the stretches of the intermodal chain ∀a ∈ A.

Xd: Relative probability of delivering/receiving a load for each node of the northern and southern regions (%) regarding other alternative nodes ∀d ∈ DDMoS North: Iquique (Xd = X1 = 43%) and Antofagasta (Xd = X2 = 57%).

MoS South: Concepción (Xd = X3 = 75.95%) and Temuco (Xd = X4 = 24.04%).

Xcjz: Relative probability of delivering/receiving a load for each node of the central region (%) regarding other alternative nodes for each MoS (MoS North and MoS South) and direction (north–south and south–north) ∀z ∈ Z∧∀c ∈ ST∧∀j ∈ J.

TSw: Time for every port operation during the maneuvering stage (h); ∀w ∈ WW.

TVM: Time invested in intermodal transport (h).

TVU: Time invested in the unimodal alternative (road haulage) (h).

Vk: Average speed of the cranes for the spoke ports ∀k ∈ K (cycles/h).

Vm: Average speed of the cranes for the hub ports ∀m ∈ M (cycles/h).

VT: Speed of the truck (km/h).

Appendix B

See Table 12.

Table 12 Unitary port costs for the preliminary and current scenarios

About this article

Verify currency and authenticity via CrossMark

Cite this article

Martínez-López, A. A multi-objective mathematical model to select fleets and maritime routes in short sea shipping: a case study in Chile. J Mar Sci Technol 26, 673–692 (2021). https://doi.org/10.1007/s00773-020-00757-y

Download citation

Keywords

  • Short sea shipping
  • Motorways of the sea
  • Intermodal chains
  • Multi-objective optimization
  • Analysis of sensitivity
  • Decision support tool