2020/01/30 by Yuzhe Zhao, Jingmiao Zhou, Yujun Fan +1
Engineering · Environmental Science · Mathematics · #Bellman equation #Benchmark (surveying) #Computer science #Economics #Engineering #Fitness function #Function (biology) #Genetic algorithm #Loss aversion #Machine learning #Maritime Navigation and Safety #Maritime Transport Emissions and Efficiency #Mathematical optimization #Mathematics #Mechanism (biology) #Microeconomics #Sensitivity (control systems) #Simulation #Speedup #Value (mathematics) #Vehicle emissions and performance
paper · pdf · doi:10.1155/2020/2157945
crossref issued 2020/01/30 · crossref published 2020/01/30 · crossref published-print 2020/01/30 · openalex publication_date 2020/01/30 · crossref created 2020/01/30 · crossref deposited 2020/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01 · crossref indexed 2026/08/06
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, SO x 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.