Optimization of ship speed and fleet deployment under carbon emissions policies for container shipping
Abstract
In this paper, under the consideration of two carbon emissions policies, the issues of optimizing ship speed and fleet deployment for container shipping were addressed. A mixed-integer nonlinear programming model of ship speed and fleet deployment was established with the objective of minimising total weekly operating costs. A simulated annealing algorithm was proposed to solve the problem. An empirical analysis was conducted with the data selected from the benchmark suite. The applicability and effectiveness of the established model and its algorithm are verified by the results. According to the results, two policies of the cap-and-trade programme and the carbon tax can better optimize the results of the ship speed and fleet deployment problem to achieve the goal of reducing carbon emissions. The research remarks in this paper will provide a solution for container shipping companies to make optimized decisions under carbon emissions policies.
Keyword : ship speed, fleet deployment, non-linear programming, cap-and-trade programme policy, carbon tax policy, simulated annealing algorithm
How to Cite
Xing, Y., Yang, H., Ma, X., & Zhang, Y. (2019). Optimization of ship speed and fleet deployment under carbon emissions policies for container shipping. Transport, 34(2), 260-274. https://doi.org/10.3846/transport.2019.9317
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Aldy, J. E.; Pizer, W. A. 2015. The competitiveness impacts of climate change mitigation policies, Journal of the Association of Environmental and Resource Economists 2(4): 565–595. https://doi.org/10.1086/683305
Brouer, B. D.; Alvarez, J. F.; Plum, C. E. M.; Pisinger, D.; Sigurd, M. M. 2014. A base integer programming model and benchmark suite for liner-shipping network design, Transportation Science 48(2): 281–312. https://doi.org/10.1287/trsc.2013.0471
Carl, J.; Fedor, D. 2016. Tracking global carbon revenues: a survey of carbon taxes versus cap-and-trade in the real world, Energy Policy 96: 50–77. https://doi.org/10.1016/j.enpol.2016.05.023
Christiansen, M.; Fagerholt, K.; Nygreen, B.; Ronen, D. 2013. Ship routing and scheduling in the new millennium, European Journal of Operational Research 228(3): 467–483. https://doi.org/10.1016/j.ejor.2012.12.002
Corbett, J. J.; Wang, H.; Winebrake, J. J. 2009. The effectiveness and costs of speed reductions on emissions from international shipping, Transportation Research Part D: Transport and Environment 14(8): 593–598. https://doi.org/10.1016/j.trd.2009.08.005
Gelareh, S.; Meng, Q. 2010. A novel modeling approach for the fleet deployment problem within a short-term planning horizon, Transportation Research Part E: Logistics and Transportation Review 46(1): 76–89. https://doi.org/10.1016/j.tre.2009.06.004
Guo, J.; Zou, L.-L.; Wei, Y.-M. 2010. Impact of inter-sectoral trade on national and global CO2 emissions: an empirical analysis of China and US, Energy Policy 38(3): 1389–1397. https://doi.org/10.1016/j.enpol.2009.11.020
IAA PortNews. 2016. MEPC 70 Approves IMO ‘Roadmap’ for GHG Reduction Strategy. Information & Analytical Agency PortNews (IAA PortNews). Available from Internet: http://en.portnews.ru/news/229015
IMO. 2015. Third IMO Greenhouse Gas Study 2014. International Maritime Organization (IMO), London, UK. 327 p. Available from Internet: http://www.imo.org/en/OurWork/Environment/PollutionPrevention/AirPollution/Documents/Third%20Greenhouse%20Gas%20Study/GHG3%20Executive%20Summary%20and%20Report.pdf
Jahangiri, A.; Afandizadeh, S.; Kalantari, N. 2011. The optimization of traffic signal timing for emergency evacuation using the simulated annealing algorithm, Transport 26(2): 133–140. https://doi.org/10.3846/16484142.2011.584959
Jaramillo, D. I.; Perakis, A N. 1991. Fleet deployment optimization for liner shipping. Part 2. Implementation and results, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 18(4): 235–262. https://doi.org/10.1080/03088839100000028
Javadian, N.; Sayarshad, H. R.; Najafi, S. 2011. Using simulated annealing for determination of the capacity of yard stations in a railway industry, Applied Soft Computing 11(2): 1899–1907. https://doi.org/10.1016/j.asoc.2010.06.006
Ketabchi, H.; Ataie-Ashtiani, B. 2015. Evolutionary algorithms for the optimal management of coastal groundwater: A comparative study toward future challenges, Journal of Hydrology 520: 193–213. https://doi.org/10.1016/j.jhydrol.2014.11.043
Kim, H.-J.; Chang, Y.-T.; Kim, K.-T.; Kim, H.-J. 2012. An epsilon-optimal algorithm considering greenhouse gas emissions for the management of a ship’s bunker fuel, Transportation Research Part D: Transport and Environment 17(2): 97–103. https://doi.org/10.1016/j.trd.2011.10.001
Kirkpatrick, S.; Gelatt, C. D.; Vecchi, M. P. 1983. Optimization by simulated annealing, Science 220(4598): 671–680. https://doi.org/10.1126/science.220.4598.671
Lee, T.-C.; Chang, Y.-T.; Lee, P. T. W. 2013. Economy-wide impact analysis of a carbon tax on international container shipping, Transportation Research Part A: Policy and Practice 58: 87–102. https://doi.org/10.1016/j.tra.2013.10.002
Meng, Q.; Wang, T. 2010. A chance constrained programming model for short-term liner ship fleet planning problems, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 37(4): 329–346. https://doi.org/10.1080/03088839.2010.486635
Meng, Q.; Wang, S. 2011. Liner shipping service network design with empty container repositioning, Transportation Research Part E: Logistics and Transportation Review 47(5): 695–708. https://doi.org/10.1016/j.tre.2011.02.004
Miola, A.; Marra, M.; Ciuffo, B. 2011. Designing a climate change policy for the international maritime transport sector: Market-based measures and technological options for global and regional policy actions, Energy Policy 39(9): 5490–5498. https://doi.org/10.1016/j.enpol.2011.05.013
Ng, M. W. 2015. Container vessel fleet deployment for liner shipping with stochastic dependencies in shipping demand, Transportation Research Part B: Methodological 74: 79–87. https://doi.org/10.1016/j.trb.2015.01.004
Ng, M. W. 2014. Distribution-free vessel deployment for liner shipping, European Journal of Operational Research 238(3): 858–862. https://doi.org/10.1016/j.ejor.2014.04.019
Nia, A. R.; Hemmati Far, M.; Niaki, S. T. A. 2015. A hybrid genetic and imperialist competitive algorithm for green vendor managed inventory of multi-item multi-constraint EOQ model under shortage, Applied Soft Computing 30: 353–364. https://doi.org/10.1016/j.asoc.2015.02.004
Notteboom, T. E.; Vernimmen, B. 2009. The effect of high fuel costs on liner service configuration in container shipping, Journal of Transport Geography 17(5): 325–337. https://doi.org/10.1016/j.jtrangeo.2008.05.003
Pantuso, G.; Fagerholt, K.; Hvattum, L. M. 2014. A survey on maritime fleet size and mix problems, European Journal of Operational Research 235(2): 341–349. https://doi.org/10.1016/j.ejor.2013.04.058
Perakis, A. N.; Jaramillo, D. I. 1991. Fleet deployment optimization for liner shipping. Part 1. Background, problem formulation and solution approaches, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 18(3): 183–200. https://doi.org/10.1080/03088839100000022
Ronen, D. 2011. The effect of oil price on containership speed and fleet size, Journal of the Operational Research Society 62(1): 211–216. https://doi.org/10.1057/jors.2009.169
Schinas, O.; Stefanakos, C. N. 2012. Cost assessment of environmental regulation and options for marine operators, Transportation Research Part C: Emerging Technologies 25: 81–99. https://doi.org/10.1016/j.trc.2012.05.002
Sys, C.; Vanelslander, T.; Adriaenssens, M.; Van Rillaer, I. 2016. International emission regulation in sea transport: Economic feasibility and impact, Transportation Research Part D: Transport and Environment 45: 139–151. https://doi.org/10.1016/j.trd.2015.06.009
Teghem, J.; Pirlot, M.; Antoniadis, C. 1995. Embedding of linear programming in a simulated annealing algorithm for solving a mixed integer production planning problem, Journal of Computational and Applied Mathematics 64(1–2): 91–102. https://doi.org/10.1016/0377-0427(95)00009-7
Vierth, I.; Karlsson, R.; Mellin, A. 2015. Effects of more stringent sulphur requirements for sea transports, Transportation Research Procedia 8: 125–135. https://doi.org/10.1016/j.trpro.2015.06.048
Wang, S.; Meng, Q. 2017. Container liner fleet deployment: a systematic overview, Transportation Research Part C: Emerging Technologies 77: 389–404. https://doi.org/10.1016/j.trc.2017.02.010
Wang, S.; Meng, Q. 2012. Sailing speed optimization for container ships in a liner shipping network, Transportation Research Part E: Logistics and Transportation Review 48(3): 701–714. https://doi.org/10.1016/j.tre.2011.12.003
Wang, S.; Meng, Q.; Liu, Z. 2013a. Bunker consumption optimization methods in shipping: a critical review and extensions, Transportation Research Part E: Logistics and Transportation Review 53: 49–62. https://doi.org/10.1016/j.tre.2013.02.003
Wang, T.; Meng, Q.; Wang, S.; Tan, Z. 2013b. Risk management in liner ship fleet deployment: a joint chance constrained programming model, Transportation Research Part E: Logistics and Transportation Review 60: 1–12. https://doi.org/10.1016/j.tre.2013.09.001
Wang, C.; Xu, C. 2015. Sailing speed optimization in voyage chartering ship considering different carbon emissions taxation, Computers & Industrial Engineering 89: 108–115. https://doi.org/10.1016/j.cie.2015.04.034
Xie, X.; Wang, T.; Chen, D. 2000. A dynamic model and algorithm for fleet planning, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 27(1): 53–63. https://doi.org/10.1080/030888300286680
Yang, H.; Ma, X.; Xing, Y. 2017. Trends in CO2 emissions from China-oriented international marine transportation activities and policy implications, Energies 10(7): 980. https://doi.org/10.3390/en10070980
Yao, Z.; Ng, S. H.; Lee, L. H. 2012. A study on bunker fuel management for the shipping liner services, Computers & Operations Research 39(5): 1160–1172. https://doi.org/10.1016/j.cor.2011.07.012
Zhao, F.; Zeng, X. 2006. Simulated annealing-genetic algorithm for transit network optimization, Journal of Computing in Civil Engineering 20(1): 57–68. https://doi.org/10.1061/(ASCE)0887-3801(2006)20:1(57)
Brouer, B. D.; Alvarez, J. F.; Plum, C. E. M.; Pisinger, D.; Sigurd, M. M. 2014. A base integer programming model and benchmark suite for liner-shipping network design, Transportation Science 48(2): 281–312. https://doi.org/10.1287/trsc.2013.0471
Carl, J.; Fedor, D. 2016. Tracking global carbon revenues: a survey of carbon taxes versus cap-and-trade in the real world, Energy Policy 96: 50–77. https://doi.org/10.1016/j.enpol.2016.05.023
Christiansen, M.; Fagerholt, K.; Nygreen, B.; Ronen, D. 2013. Ship routing and scheduling in the new millennium, European Journal of Operational Research 228(3): 467–483. https://doi.org/10.1016/j.ejor.2012.12.002
Corbett, J. J.; Wang, H.; Winebrake, J. J. 2009. The effectiveness and costs of speed reductions on emissions from international shipping, Transportation Research Part D: Transport and Environment 14(8): 593–598. https://doi.org/10.1016/j.trd.2009.08.005
Gelareh, S.; Meng, Q. 2010. A novel modeling approach for the fleet deployment problem within a short-term planning horizon, Transportation Research Part E: Logistics and Transportation Review 46(1): 76–89. https://doi.org/10.1016/j.tre.2009.06.004
Guo, J.; Zou, L.-L.; Wei, Y.-M. 2010. Impact of inter-sectoral trade on national and global CO2 emissions: an empirical analysis of China and US, Energy Policy 38(3): 1389–1397. https://doi.org/10.1016/j.enpol.2009.11.020
IAA PortNews. 2016. MEPC 70 Approves IMO ‘Roadmap’ for GHG Reduction Strategy. Information & Analytical Agency PortNews (IAA PortNews). Available from Internet: http://en.portnews.ru/news/229015
IMO. 2015. Third IMO Greenhouse Gas Study 2014. International Maritime Organization (IMO), London, UK. 327 p. Available from Internet: http://www.imo.org/en/OurWork/Environment/PollutionPrevention/AirPollution/Documents/Third%20Greenhouse%20Gas%20Study/GHG3%20Executive%20Summary%20and%20Report.pdf
Jahangiri, A.; Afandizadeh, S.; Kalantari, N. 2011. The optimization of traffic signal timing for emergency evacuation using the simulated annealing algorithm, Transport 26(2): 133–140. https://doi.org/10.3846/16484142.2011.584959
Jaramillo, D. I.; Perakis, A N. 1991. Fleet deployment optimization for liner shipping. Part 2. Implementation and results, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 18(4): 235–262. https://doi.org/10.1080/03088839100000028
Javadian, N.; Sayarshad, H. R.; Najafi, S. 2011. Using simulated annealing for determination of the capacity of yard stations in a railway industry, Applied Soft Computing 11(2): 1899–1907. https://doi.org/10.1016/j.asoc.2010.06.006
Ketabchi, H.; Ataie-Ashtiani, B. 2015. Evolutionary algorithms for the optimal management of coastal groundwater: A comparative study toward future challenges, Journal of Hydrology 520: 193–213. https://doi.org/10.1016/j.jhydrol.2014.11.043
Kim, H.-J.; Chang, Y.-T.; Kim, K.-T.; Kim, H.-J. 2012. An epsilon-optimal algorithm considering greenhouse gas emissions for the management of a ship’s bunker fuel, Transportation Research Part D: Transport and Environment 17(2): 97–103. https://doi.org/10.1016/j.trd.2011.10.001
Kirkpatrick, S.; Gelatt, C. D.; Vecchi, M. P. 1983. Optimization by simulated annealing, Science 220(4598): 671–680. https://doi.org/10.1126/science.220.4598.671
Lee, T.-C.; Chang, Y.-T.; Lee, P. T. W. 2013. Economy-wide impact analysis of a carbon tax on international container shipping, Transportation Research Part A: Policy and Practice 58: 87–102. https://doi.org/10.1016/j.tra.2013.10.002
Meng, Q.; Wang, T. 2010. A chance constrained programming model for short-term liner ship fleet planning problems, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 37(4): 329–346. https://doi.org/10.1080/03088839.2010.486635
Meng, Q.; Wang, S. 2011. Liner shipping service network design with empty container repositioning, Transportation Research Part E: Logistics and Transportation Review 47(5): 695–708. https://doi.org/10.1016/j.tre.2011.02.004
Miola, A.; Marra, M.; Ciuffo, B. 2011. Designing a climate change policy for the international maritime transport sector: Market-based measures and technological options for global and regional policy actions, Energy Policy 39(9): 5490–5498. https://doi.org/10.1016/j.enpol.2011.05.013
Ng, M. W. 2015. Container vessel fleet deployment for liner shipping with stochastic dependencies in shipping demand, Transportation Research Part B: Methodological 74: 79–87. https://doi.org/10.1016/j.trb.2015.01.004
Ng, M. W. 2014. Distribution-free vessel deployment for liner shipping, European Journal of Operational Research 238(3): 858–862. https://doi.org/10.1016/j.ejor.2014.04.019
Nia, A. R.; Hemmati Far, M.; Niaki, S. T. A. 2015. A hybrid genetic and imperialist competitive algorithm for green vendor managed inventory of multi-item multi-constraint EOQ model under shortage, Applied Soft Computing 30: 353–364. https://doi.org/10.1016/j.asoc.2015.02.004
Notteboom, T. E.; Vernimmen, B. 2009. The effect of high fuel costs on liner service configuration in container shipping, Journal of Transport Geography 17(5): 325–337. https://doi.org/10.1016/j.jtrangeo.2008.05.003
Pantuso, G.; Fagerholt, K.; Hvattum, L. M. 2014. A survey on maritime fleet size and mix problems, European Journal of Operational Research 235(2): 341–349. https://doi.org/10.1016/j.ejor.2013.04.058
Perakis, A. N.; Jaramillo, D. I. 1991. Fleet deployment optimization for liner shipping. Part 1. Background, problem formulation and solution approaches, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 18(3): 183–200. https://doi.org/10.1080/03088839100000022
Ronen, D. 2011. The effect of oil price on containership speed and fleet size, Journal of the Operational Research Society 62(1): 211–216. https://doi.org/10.1057/jors.2009.169
Schinas, O.; Stefanakos, C. N. 2012. Cost assessment of environmental regulation and options for marine operators, Transportation Research Part C: Emerging Technologies 25: 81–99. https://doi.org/10.1016/j.trc.2012.05.002
Sys, C.; Vanelslander, T.; Adriaenssens, M.; Van Rillaer, I. 2016. International emission regulation in sea transport: Economic feasibility and impact, Transportation Research Part D: Transport and Environment 45: 139–151. https://doi.org/10.1016/j.trd.2015.06.009
Teghem, J.; Pirlot, M.; Antoniadis, C. 1995. Embedding of linear programming in a simulated annealing algorithm for solving a mixed integer production planning problem, Journal of Computational and Applied Mathematics 64(1–2): 91–102. https://doi.org/10.1016/0377-0427(95)00009-7
Vierth, I.; Karlsson, R.; Mellin, A. 2015. Effects of more stringent sulphur requirements for sea transports, Transportation Research Procedia 8: 125–135. https://doi.org/10.1016/j.trpro.2015.06.048
Wang, S.; Meng, Q. 2017. Container liner fleet deployment: a systematic overview, Transportation Research Part C: Emerging Technologies 77: 389–404. https://doi.org/10.1016/j.trc.2017.02.010
Wang, S.; Meng, Q. 2012. Sailing speed optimization for container ships in a liner shipping network, Transportation Research Part E: Logistics and Transportation Review 48(3): 701–714. https://doi.org/10.1016/j.tre.2011.12.003
Wang, S.; Meng, Q.; Liu, Z. 2013a. Bunker consumption optimization methods in shipping: a critical review and extensions, Transportation Research Part E: Logistics and Transportation Review 53: 49–62. https://doi.org/10.1016/j.tre.2013.02.003
Wang, T.; Meng, Q.; Wang, S.; Tan, Z. 2013b. Risk management in liner ship fleet deployment: a joint chance constrained programming model, Transportation Research Part E: Logistics and Transportation Review 60: 1–12. https://doi.org/10.1016/j.tre.2013.09.001
Wang, C.; Xu, C. 2015. Sailing speed optimization in voyage chartering ship considering different carbon emissions taxation, Computers & Industrial Engineering 89: 108–115. https://doi.org/10.1016/j.cie.2015.04.034
Xie, X.; Wang, T.; Chen, D. 2000. A dynamic model and algorithm for fleet planning, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 27(1): 53–63. https://doi.org/10.1080/030888300286680
Yang, H.; Ma, X.; Xing, Y. 2017. Trends in CO2 emissions from China-oriented international marine transportation activities and policy implications, Energies 10(7): 980. https://doi.org/10.3390/en10070980
Yao, Z.; Ng, S. H.; Lee, L. H. 2012. A study on bunker fuel management for the shipping liner services, Computers & Operations Research 39(5): 1160–1172. https://doi.org/10.1016/j.cor.2011.07.012
Zhao, F.; Zeng, X. 2006. Simulated annealing-genetic algorithm for transit network optimization, Journal of Computing in Civil Engineering 20(1): 57–68. https://doi.org/10.1061/(ASCE)0887-3801(2006)20:1(57)