Multiobjective Route Selection Based on LASSO Regression: When Will the Suez Canal Lose Its Importance?

With coronavirus disease 2019 reshaping the global shipping market, many ships in the Europe-Asia trades that need to sail through the Suez Canal begun to detour via the much longer route, the Cape of Good Hope In order to explain and predict the route choice, this paper employs the least absolute shrinkage and selection operator regression to estimate fuel consumption based on the automatic identification system and ocean dataset and designed a multiobjective particle swarm optimization to find Pareto optimal solutions that minimize the total voyage cost and total voyage time After that, the weighted sum method was introduced to deal with the route selection Finally, a case study was conducted on the real data from CMA CGM, a leading worldwide shipping company, and four scenarios of fuel prices and charter rates were built and analyzed The results show that the detour around the Cape of Good Hope is preferred only in the scenario of low fuel price and low charter In addition, the paper suggests that the authority of Suez Canal should cut down the canal toll according to our result to win back the ships because we have verified that offering a discount on the canal roll is effective [ABSTRACT FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )

[1]  Inge Norstad,et al.  Reducing fuel emissions by optimizing speed on shipping routes , 2010, J. Oper. Res. Soc..

[2]  Christos Gkerekos,et al.  A novel, data-driven heuristic framework for vessel weather routing , 2020, Ocean Engineering.

[3]  Qiang Meng,et al.  Optimal vessel speed and fleet size for industrial shipping services under the emission control area regulation , 2019, Transportation Research Part C: Emerging Technologies.

[4]  Kjetil Fagerholt,et al.  Ship routing and scheduling in the new millennium , 2013, Eur. J. Oper. Res..

[5]  Lu Zhen,et al.  The effects of emission control area regulations on cruise shipping , 2018, Transportation Research Part D: Transport and Environment.

[6]  Christos A. Kontovas,et al.  A multiple ship routing and speed optimization problem under time, cost and environmental objectives , 2017 .

[7]  Kyung-Keun Lee,et al.  An optimization-based decision support system for ship scheduling , 1997 .

[8]  Dongfang Ma,et al.  Method for simultaneously optimizing ship route and speed with emission control areas , 2020 .

[9]  Loo Hay Lee,et al.  A study on bunker fuel management for the shipping liner services , 2012, Comput. Oper. Res..

[10]  Jie Yang,et al.  A probabilistic model for latent least squares regression , 2015, Neurocomputing.

[11]  Sadeque Hamdan,et al.  Liner shipping network design with emission control areas: A genetic algorithm-based approach , 2018, Transportation Research Part D: Transport and Environment.

[12]  Osman Turan,et al.  An artificial neural network based decision support system for energy efficient ship operations , 2016, Comput. Oper. Res..

[13]  Dun-Wei Gong,et al.  A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch , 2012, Inf. Sci..

[14]  Voratas Kachitvichyanukul,et al.  Movement Strategies for Multi-Objective Particle Swarm Optimization , 2010, Int. J. Appl. Metaheuristic Comput..

[15]  Xiaoyan Sun,et al.  Variable-Size Cooperative Coevolutionary Particle Swarm Optimization for Feature Selection on High-Dimensional Data , 2020, IEEE Transactions on Evolutionary Computation.

[16]  Wei Liu,et al.  Predicting ship fuel consumption based on LASSO regression , 2017, Transportation Research Part D: Transport and Environment.

[17]  Christos A. Kontovas,et al.  Speed models for energy-efficient maritime transportation: A taxonomy and survey , 2013 .

[18]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[19]  Ran Yan,et al.  Route and speed optimization for liner ships under emission control policies , 2020 .

[20]  Voratas Kachitvichyanukul,et al.  A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery , 2009, Comput. Oper. Res..

[21]  Dun-Wei Gong,et al.  Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer , 2011, Expert Syst. Appl..

[22]  Hartmut Stadtler,et al.  A Liner Shipping Network Design - Routing and Scheduling Impacted by Environmental Influences , 2011, INOC.

[23]  Qiang Meng,et al.  Sailing speed optimization for container ships in a liner shipping network , 2012 .

[24]  A. Belloni,et al.  Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming , 2010, 1009.5689.

[25]  Kjetil Fagerholt,et al.  Maritime routing and speed optimization with emission control areas , 2015 .

[26]  Biagio Palumbo,et al.  A comparison of advanced regression techniques for predicting ship CO2 emissions , 2017, Qual. Reliab. Eng. Int..

[27]  H. Zou The Adaptive Lasso and Its Oracle Properties , 2006 .

[28]  Vasant Dhar,et al.  Editorial - Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research , 2014, Inf. Syst. Res..

[29]  Xinping Yan,et al.  Real-time optimization of ship energy efficiency based on the prediction technology of working condition , 2016 .

[30]  Yongtu Liang,et al.  A voyage with minimal fuel consumption for cruise ships , 2019, Journal of Cleaner Production.

[31]  Jacqueline Moore,et al.  Multiobjective particle swarm optimization , 2000, ACM-SE 38.

[32]  M. R. Osborne,et al.  On the LASSO and its Dual , 2000 .

[33]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[34]  Habin Lee,et al.  Multi-objective decision support to enhance environmental sustainability in maritime shipping: A review and future directions , 2015 .

[35]  Zahir Irani,et al.  A decision support system for vessel speed decision in maritime logistics using weather archive big data , 2017, Comput. Oper. Res..