Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Energy Systems
Erschienen in: Energy Systems 4/2017

23.12.2015 | Original Paper

The green vessel schedule design problem: consideration of emissions constraints

verfasst von: M. A. Dulebenets, M. M. Golias, S. Mishra

Erschienen in: Energy Systems | Ausgabe 4/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The fluctuating demand of international seaborne trade, overcapacity, and increasing attention to environmental issues force liner shipping companies to improve efficiency of their operations, while complying with the established emissions limitations. This paper proposes a mathematical model for the green vessel scheduling problem, imposing constraints on emissions produced by a vessel at each voyage leg of the liner shipping route. The original mixed integer non-linear mathematical model is linearized and solved efficiently using CPLEX. Numerical experiments are conducted to demonstrate changes in the vessel schedule that a liner shipping company has to make for a given shipping route, when emissions constraints are imposed.
Vorheriger Artikel Energy and environmental efficiency analysis of China’s regional transportation sectors: a slack-based DEA approach
Nächster Artikel Non-dominated sorting differential evolution algorithm for the minimization of route based fuel consumption multiobjective vehicle routing problems

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
1
Set of handling rates contains indexes of available handling rates (i.e., if a terminal operator at port i offers two handling rates 75 TEUs/hr. and 50 TEUs/hr., then \(S_i =\{1,2\})\).
 
4
This study considered the large size problem instances, where VSDPL had 1,889 variables and 5,273 equations, while GVSDPL had 1,940 variables and 5,374 equations.
 
Literatur
1.
Alhrabi, A., Wang, S., Davy, P.: Schedule design for sustainable container supply chain networks with port time windows. Adv. Eng. Inf. 29, 322–331 (2015)CrossRef
2.
Cariou, P.: Is slow steaming a sustainable means of reducing CO\(_{2}\) emissions from container shipping? Transp. Res. D 16, 260–264 (2011)CrossRef
3.
Cariou, P., Cheaitou, A.: The effectiveness of a European speed limit versus an international Bunker-Levy to reduce CO\(_{2}\) emissions from container shipping. Transp. Res. D 17, 116–123 (2012)CrossRef
4.
Chang, C., Wang, C.: Evaluating the effects of speed reduce for shipping costs and CO\(_{2}\) emission. Transp. Res. D 31, 110–115 (2014)CrossRef
5.
Chuang, T., Lin, C., Kung, J., Lin, M.: Planning the route of container ships: a fuzzy genetic approach. Expert Syst. Appl. 37, 2948–2956 (2010)CrossRef
6.
Corbett, J., Wang, H., Winebrake, J.: The effectiveness and costs of speed reductions on emissions from international shipping. Transp. Res. D 14, 593–598 (2009)CrossRef
7.
Cullinane, K., Bergqvist, R.: Emission control areas and their impact on maritime transport. Transp. Res. D 28, 1–5 (2014)CrossRef
8.
Dulebenets, M., Golias, M., Mishra, S., Heaslet, W.: Evaluation of the floaterm concept at marine container terminals via simulation. Simul. Model. Pract. Theory 54, 19–35 (2015)CrossRef
9.
Du, Y., Chen, Q., Quan, X., Long, L., Fung, R.: Berth allocation considering fuel consumption and vessel emissions. Transp. Res. E 47, 1021–1037 (2011)CrossRef
10.
Eyring, V., Isaksen, I., Berntsen, T., Collins, W., Corbett, J., Endresen, O., Grainger, R., Moldanova, J., Schlanger, H., Stevenson, D.: Transport impacts on atmosphere and climate: shipping. Atmos. Environ. 44, 4735–4771 (2010)CrossRef
11.
Fagerholt, K.: Ship scheduling with soft time windows: an optimization based approach. Eur. J. Oper. Res. 131, 559–571 (2001)MathSciNetCrossRefMATH
12.
Fagerholt, K., Gausel, N.T., Rakke, J.G., Psaraftis, H.N.: Maritime routing and speed optimization with emission control areas. Transp. Res. C 52, 57–73 (2015)CrossRef
13.
Golias, M., Boile, M., Theofanis, S., Efstathiou, C.: The berth scheduling problem: maximizing berth productivity and minimizing fuel consumption and emissions production. Transp. Res. Record. J Transp. Res. Board 2166, 20–27 (2010)CrossRef
14.
Golias, M., Haralambides, H.: Berth scheduling with variable cost functions. Marit. Econ. Logist. 13, 174–189 (2011)CrossRef
15.
Heitmann, N., Khalilian, S.: Accounting for carbon dioxide emissions from international shipping: burden sharing under different UNFCCC allocation options and regime scenarios. Mar. Policy 35, 682–691 (2011)CrossRef
16.
17.
Huang, Y., Rebennack, S., Zheng, Q.: Techno-economic analysis and optimization models for carbon capture and storage: a survey. Energy Syst. 4, 315–353 (2013)CrossRef
18.
IMO.: Prevention of GHG emissions from ships. Third IMO GHG Study 2014—Final Report (2014)
19.
20.
21.
Kontovas, C.: The green ship routing: a scheduling problem (GSRSP): a conceptual approach. Transp. Res. D 31, 61–69 (2014)CrossRef
22.
Lindstad, H., Asbjornslett, B., Stromann, A.: Reductions in greenhouse gas emissions and cost by shipping at lower speeds. Energy Policy. 39, 3456–3464 (2011)
23.
Maloni, M., Paul, J., Gligor, D.: Slow steaming impacts on ocean carriers and shippers. Marit. Econ. Logist. 15, 151–171 (2013)CrossRef
24.
Meng, Q., Wang, S., Andersson, H., Thun, K.: Containership routing and scheduling in liner shipping: overview and future research directions. Transp. Sci. 48, 265–280 (2014)CrossRef
25.
Miola, A., Ciuffo, B.: Estimating air emissions from ships: meta-analysis of modelling approaches and available data sources. Atmos. Environ. 45, 2242–2251 (2011)CrossRef
26.
Norstad, I., Fagerholt, K., Laporte, G.: Tramp ship routing and scheduling with speed optimization. Transp. Res. C 19, 853–865 (2011)CrossRef
27.
28.
Psaraftis, H.: Market-based measures for greenhouse gas emissions from ships: a review. WMU J. Marit. Aff. 11, 211–232 (2012)CrossRef
29.
Psaraftis, H., Kontovas, C.: CO\(_{2}\) emission statistics for the world commercial fleet. WMU J. Marit. Aff. 8, 1–25 (2009)CrossRef
30.
Psaraftis, H., Kontovas, C.: Balancing the economic and environmental performance of maritime transportation. Transp. Res. D 15, 458–462 (2010)CrossRef
31.
Psaraftis, H., Kontovas, C.: Speed models for energy-efficient maritime transportation: a taxonomy and survey. Transp. Res. C 26, 331–351 (2013)CrossRef
32.
Psaraftis, H., Kontovas, C.: Ship speed optimization: concepts, models and combined speed-routing scenarios. Transp. Res. C 44, 52–69 (2014)CrossRef
33.
Psaraftis, H., Kontovas, C.: Slow steaming in maritime transportation: fundamentals, trade-offs, and decision models. Handbook of ocean container transport logistics. Springer, Berlin (2015)
34.
Rudra, S., Rosendahl, L., Kumar, A.: Development of net energy ratio and emission factor for quad-generation pathways. Energy Syst. 5, 719–735 (2014)CrossRef
35.
Singham, D., Cai, W., White, J.: Optimal carbon capture and storage contracts using historical CO\(_{2}\) emissions levels. Energy Syst. 6, 331–360 (2015)CrossRef
36.
37.
38.
Qi, X., Song, D.: Minimizing fuel emissions by optimizing vessel schedules in liner shipping with uncertain port times. Transp. Res. E 48, 863–880 (2012)CrossRef
39.
40.
Wang, H., Wang, S., Meng, Q.: Simultaneous optimization of schedule coordination and cargo allocation for liner container shipping networks. Transp. Res. E 70, 261–273 (2014)CrossRef
41.
Wang, S., Alharbi, A., Davy, P.: Liner ship route schedule design with port time windows. Transp. Res. C 41, 1–17 (2014)CrossRef
42.
Wang, S., Meng, Q.: Liner ship route schedule design with sea contingency time and port time uncertainty. Transp. Res. B 46, 615–633 (2012)CrossRef
43.
Wang, S., Meng, Q.: Sailing speed optimization for container ships in a liner shipping network. Transp. Res. E 48, 701–714 (2012)CrossRef
44.
Wang, S., Meng, Q.: Robust schedule design for liner shipping services. Transp. Res. E 48, 1093–1106 (2012)CrossRef
45.
Wang, S., Meng, Q., Liu, Z.: Containership scheduling with transit-time-sensitive container shipment demand. Transp. Res. B 54, 68–83 (2013)CrossRef
46.
Wang, S., Meng, Q., Liu, Z.: Bunker consumption optimization methods in shipping: a critical review and extensions. Transp. Res. C 53, 49–62 (2013)
47.
48.
Yao, Z., Ng, S., Lee, L.: A study on bunker fuel management for the shipping liner services. Comput. Oper. Res. 39, 1160–1172 (2012)CrossRef
49.
Zampelli, S., Vergados, Y., Schaeren, R., Dullaert, W., Raa, B.: The berth allocation and quay crane assignment problem using a CP approach. Principles and practice of constraint programming. Springer, Berlin Heidelberg (2014)
50.
Zis, T., North, R.J., Angeloudis, P., Ochieng, W.Y., Bell, M.G.H.: Evaluation of cold ironing and speed reduction policies to reduce ship emissions near and at ports. Marit. Econ. Logist. 16, 371–398 (2014)CrossRef
51.
Zis, T., North, R.J., Angeloudis, P., Ochieng, W.Y., Bell, M.G.H.: Environmental balance of shipping emissions reduction strategies. Transp. Res. Rec. 2479, 25–33 (2015)CrossRef
Metadaten
Titel
The green vessel schedule design problem: consideration of emissions constraints
verfasst von
M. A. Dulebenets
M. M. Golias
S. Mishra
Publikationsdatum
23.12.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Energy Systems / Ausgabe 4/2017
Print ISSN: 1868-3967
Elektronische ISSN: 1868-3975
DOI
https://doi.org/10.1007/s12667-015-0183-3

Weitere Artikel der Ausgabe 4/2017

Energy Systems 4/2017 Zur Ausgabe

Original Paper

Neural computation based vector controlled asynchronous motor fed by three levels NPC

Preface

Special volume on operational research on the interrelated application areas of energy, transportation and the environment

Original Paper

Non-dominated sorting differential evolution algorithm for the minimization of route based fuel consumption multiobjective vehicle routing problems

Original Paper

Environmental Externalities Score: a new emission factor to model green vehicle routing problem

Original Paper

Air traffic management and energy efficiency: the free flight concept

Original Paper

-intensive power generation and REDD-based emission offsets with a benefit-sharing mechanism