Sailing Speed Optimization Model for Slow Steaming Considering Loss Aversion Mechanism

This paper analyses loss aversion mechanism (LAM) of the shipping company’s decision-makers about the risk-based decision (RBD) for slow steaming and generalizes a novel optimization model for the sailing speed through the trade-off between fuel consumption, SOx emissions and delivery delay. The value functions against the benchmark speed were constructed based on physiological expected utility (PEU) to reveal the features of loss aversion, and the objective function was derived from these value functions with the aim to optimize the sailing speed. After that, a Genetic Algorithm (GA) solution with fitness function and special operators was built to solve the proposed model. Finally, the model was applied to pinpoint the PEU for the optimal sailing speed against the benchmark speed, and the sensitivity of the model was discussed with different benchmark speeds, value function weights and input parameters. The analysis shows that the proposed model can assist the slow steaming RBD based on the inner feelings of the shipping company’s decision-makers, offering a novel tool for sailing speed optimization.

[1]  Christos A. Kontovas,et al.  Ship speed optimization: Concepts, models and combined speed-routing scenarios , 2014 .

[2]  Konstantinos G. Gkonis,et al.  Modeling tankers' optimal speed and emissions , 2012 .

[3]  Qiang Meng,et al.  Budgeting Fuel Consumption of Container Ship over Round-Trip Voyage through Robust Optimization , 2015 .

[4]  James J. Corbett,et al.  The effectiveness and costs of speed reductions on emissions from international shipping , 2009 .

[5]  Carlo Giacomo Prato,et al.  Myopic loss aversion in the response of electric vehicle owners to the scheduling and pricing of vehicle charging , 2017 .

[6]  Christos A. Kontovas,et al.  Slow Steaming in Maritime Transportation: Fundamentals, Trade-offs, and Decision Models , 2015 .

[7]  H. Arkes,et al.  My Loss Is Your Loss … Sometimes: Loss Aversion and the Effect of Motivational Biases , 2008, Risk analysis : an official publication of the Society for Risk Analysis.

[8]  N. Ramnani,et al.  Distinct portions of anterior cingulate cortex and medial prefrontal cortex are activated by reward processing in separable phases of decision-making cognition , 2004, Biological Psychiatry.

[9]  Paolo Pasquariello,et al.  Prospect Theory and Market Quality , 2013, J. Econ. Theory.

[10]  Sabrina M. Tom,et al.  The Neural Basis of Loss Aversion in Decision-Making Under Risk , 2007, Science.

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

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

[13]  Mohammed Abdellaoui,et al.  Loss Aversion Under Prospect Theory: A Parameter-Free Measurement , 2007, Manag. Sci..

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

[15]  D. Kahneman,et al.  Functional Imaging of Neural Responses to Expectancy and Experience of Monetary Gains and Losses tasks with monetary payoffs , 2001 .

[16]  Claus Schittenhelm,et al.  What is loss aversion , 2016 .

[17]  Joshua W. Brown,et al.  Decision making in the Balloon Analogue Risk Task (BART): Anterior cingulate cortex signals loss aversion but not the infrequency of risky choices , 2012, Cognitive, Affective, & Behavioral Neuroscience.

[18]  J. Schall,et al.  Neural Control of Voluntary Movement Initiation , 1996, Science.

[19]  Colin Camerer,et al.  Violations of the betweenness axiom and nonlinearity in probability , 1994 .

[20]  T.C.E. Cheng,et al.  Green Shipping Management , 2015 .

[21]  S. Cappa,et al.  The Functional and Structural Neural Basis of Individual Differences in Loss Aversion , 2013, The Journal of Neuroscience.

[22]  Zheng Fang,et al.  Does everyone exhibit loss aversion? Evidence from a panel quantile regression analysis of subjective well-being in Japan , 2017 .

[23]  Gilbert Laporte,et al.  The Pollution-Routing Problem , 2011 .

[24]  D. Hensher,et al.  Analyzing loss aversion and diminishing sensitivity in a freight transport stated choice experiment , 2010 .

[25]  J. R. Jaramillo,et al.  The Green Vehicle Routing Problem , 2011 .

[26]  Y. Qian,et al.  Variability of solar radiation under cloud‐free skies in China: The role of aerosols , 2007 .

[27]  A. Tversky,et al.  Prospect Theory. An Analysis of Decision Making Under Risk , 1977 .

[28]  Liyan Yang,et al.  Loss Aversion, Survival and Asset Prices , 2015, J. Econ. Theory.

[29]  Sarah Mander,et al.  Slow steaming and a new dawn for wind propulsion: A multi-level analysis of two low carbon shipping transitions , 2017 .

[30]  A. Tversky,et al.  Advances in prospect theory: Cumulative representation of uncertainty , 1992 .

[31]  Chuanxu Wang,et al.  Strategies of refueling, sailing speed and ship deployment of containerships in the low-carbon background , 2017, Comput. Ind. Eng..

[32]  J. L. Pinto,et al.  Loss aversion and scale compatibility in two-attribute trade-offs , 2002 .

[33]  Zhiyuan Liu,et al.  Bunker consumption optimization methods in shipping: A critical review and extensions , 2013 .

[34]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.