Green vessel scheduling in liner shipping: Modeling carbon dioxide emission costs in sea and at ports of call

Maxim A. Dulebenets1
1Department of Civil & Environmental Engineering, Florida A&M University-Florida State University, 2525 Pottsdamer Street, Building A, Suite A124, Tallahassee, FL 32310-6046, USA

Tài liệu tham khảo

Alhrabi, 2015, Schedule design for sustainable container supply chain networks with port time windows, Adv. Eng. Inform., 29, 322, 10.1016/j.aei.2014.12.001 Chuang, 2010, Planning the route of container ships: A fuzzy genetic approach, Expert Syst. Appl., 37, 2948, 10.1016/j.eswa.2009.09.040 Du, 2011, Berth allocation considering fuel consumption and vessel emissions, Transp. Res. Part E, 47, 1021, 10.1016/j.tre.2011.05.011 Dulebenets, 2015, Bunker consumption optimization in liner shipping: A metaheuristic approach, Int. J. Recent Innovation Trend Comput. Commun., 3, 3766 Dulebenets, M.A., 2015b. Models and solution algorithms for improving operations in marine transportation. Dissertation, the University of Memphis. Dulebenets, 2016, Advantages and disadvantages from enforcing emission restrictions within emission control areas, Maritime Business Rev., 1, 107, 10.1108/MABR-05-2016-0011 Dulebenets, 2017, The green vessel scheduling problem with transit time requirements in a liner shipping route with emission control areas, Alexandria Eng. J., 1 Dulebenets, 2015, The green vessel schedule design problem: consideration of emissions constraints, Energy Syst., 1 EPA., 2017. Causes of Climate Change. https://www3.epa.gov/climatechange/science/causes.html. Accessed 02.12.17. Fagerholt, 2001, Ship scheduling with soft time windows: An optimization based approach, Eur. J. Oper. Res., 131, 559, 10.1016/S0377-2217(00)00098-9 Fagerholt, 2015, On two speed optimization problems for ships that sail in and out of emission control areas, Transp. Res. Part D, 39, 56, 10.1016/j.trd.2015.06.005 Fagerholt, 2015, Maritime routing and speed optimization with emission control areas, Transp. Res. Part C, 52, 57, 10.1016/j.trc.2014.12.010 Feroz, 2009, Global warming and environmental production efficiency ranking of the Kyoto Protocol nations, J. Environ. Manage., 90, 1178, 10.1016/j.jenvman.2008.05.006 GAMS., 2017. GAMS home page, https://www.gams.com/. Accessed 02.10.17. IMO., 2014. Prevention of GHG emissions from ships. Third IMO GHG Study 2014 – Final Report. IMO., 2017. Air Pollution, Energy Efficiency and Greenhouse Gas Emissions. http://www.imo.org/en/OurWork/Environment/PollutionPrevention/AirPollution. Accessed 02.15.17. Kontovas, 2014, The Green Ship Routing and Scheduling Problem (GSRSP): A conceptual approach, Transp. Res. Part D, 31, 61, 10.1016/j.trd.2014.05.014 Levin, 2017, Network-based model for predicting the effect of fuel price on transit ridership and greenhouse gas emissions, Int. J. Transport. Sci. Technol., 1 Lindstad, 2011, Reductions in greenhouse gas emissions and cost by shipping at lower speeds, Energy Policy, 39, 3456, 10.1016/j.enpol.2011.03.044 Lindstad, 2012, The importance of economies of scale for reductions in greenhouse gas emissions from shipping, Energy Policy, 46, 386, 10.1016/j.enpol.2012.03.077 Live Science, 2017. Global Warming: News, Facts, Causes & Effects. http://www.livescience.com/topics/global-warming. Accessed 02.16.17. Mansouri, 2015, Multi-objective decision support to enhance environmental sustainability in maritime shipping: A review and future directions, Transp. Res. Part E, 78, 3, 10.1016/j.tre.2015.01.012 Mathworks, 2016. Release 2016a, http://www.mathworks.com/. Accessed 02.12.17. Meng, 2014, Containership routing and scheduling in liner shipping: overview and future research directions, Transport. Sci., 48, 265, 10.1287/trsc.2013.0461 NYK, 2017. Container Service Network. https://www.nykline.com. Accessed 02.12.17. OOCL, 2017. e-Services, Sailing Schedule, Schedule by Service Loops, Asia-Europe (AET). http://www.oocl.com. Accessed 02.18.17. Psaraftis, 2013, Speed models for energy-efficient maritime transportation: A taxonomy and survey, Transp. Res. Part C, 26, 331, 10.1016/j.trc.2012.09.012 Psaraftis, 2014, Ship speed optimization: Concepts, models and combined speed-routing scenarios, Transp. Res. Part C, 44, 52, 10.1016/j.trc.2014.03.001 Qi, 2012, Minimizing fuel emissions by optimizing vessel schedules in liner shipping with uncertain port times, Transp. Res. Part E, 48, 863, 10.1016/j.tre.2012.02.001 Schroten, A., Van Essen, H., Anthes, R., 2011. External Cost Calculator – Methodology report. CE Delft and IVE mbH. Sharma, 2012, Optimizing performance of at-grade intersection with bus rapid transit corridor and heterogeneous traffic, Int. J. Transport. Sci. Technol., 1, 131, 10.1260/2046-0430.1.2.131 Song, 2015, Multi-objective optimization for planning liner shipping service with uncertain port times, Transp. Res. Part E, 84, 1, 10.1016/j.tre.2015.10.001 Stewart, 2015, Assessing the carbon impact of ICT measures: a case study investigation using Latis Model, Int. J. Transport. Sci. Technol., 4, 277, 10.1260/2046-0430.4.3.277 Sun, 1996, Global warming and global dioxide emission: An empirical study, J. Environ. Manage., 46, 327, 10.1006/jema.1996.0025 The Port Authority of New York and New Jersey, 2017. Marine Terminal Tariffs. http://www.panynj.gov/port/tariffs.html. Accessed 02.12.17. Tran, 2016, Container shipping route design incorporating the costs of shipping, inland/feeder transport, inventory and CO2 emission, Maritime Econo. Logistics, 1 Tseng, 2014, A strategic decision-making model considering the social costs of carbon dioxide emissions for sustainable supply chain management, J. Environ. Manage., 133, 315, 10.1016/j.jenvman.2013.11.023 UNCTAD, 2015. Review of Maritime Transport 2015. United Nations Conference on Trade and Development, New York and Geneva. Wang, 2012, Liner ship route schedule design with sea contingency time and port time uncertainty, Transp. Res. Part B, 46, 615, 10.1016/j.trb.2012.01.003 Wang, 2012, Sailing speed optimization for container ships in a liner shipping network, Transp. Res. Part E, 48, 701, 10.1016/j.tre.2011.12.003 Wang, 2012, Robust schedule design for liner shipping services, Transp. Res. Part E, 48, 1093, 10.1016/j.tre.2012.04.007 Wang, 2013, Bunker consumption optimization methods in shipping: A critical review and extensions, Transp. Res. Part C, 53, 49, 10.1016/j.tre.2013.02.003 Wang, 2014, Liner ship route schedule design with port time windows, Transp. Res. Part C, 41, 1, 10.1016/j.trc.2014.01.012 Wang, 2015, Estimation of the perceived value of transit time for containerized cargoes, Transp. Res. Part A, 78, 298 World Bank, 2017. Cost to import (US$ per container). http://data.worldbank.org/indicator/IC.IMP.COST.CD. Accessed 02.15.17. World Shipping Council, 2017. Top 50 World Container Ports. www.worldshipping.org. Accessed 01.17.17. Zampelli, S., Vergados, Y., Schaeren, R., Dullaert, W., Raa, B., 2014. The berth allocation and quay crane assignment problem using a CP approach. Principles and Practice of Constraint Programming (pp. 880–896), Springer, Berlin Heidelberg.