Abstract
This paper analyses the operator's risk-based decision (RBD) company for slow steaming, and creates a sailing speed optimization model for slow steaming (SSOM-SS), aiming to balance the expected utility-based objectives (EUO) of fuel consumption, SOx emissions and delivery delay. Considering the limitations of existing theoretical fuel consumption functions under uncertainties in voyages, the authors applies big data analytics (BDA) techniques like data fusion and feature selection to provide the SSOM-SS with accurate and suitable data on fuel consumption. In addition, a solver is built based on the genetic algorithm (GA) to solve the SSOM-SS. The effectiveness of the SSOM-SS is verified through a case study on the RBD for slow steaming of an Orient Overseas Container Line (OOCL) containership sailing across the sulphur emission control areas (SECAs) in Chinese coastal regions. The results show that the SSOM-SS can facilitate the RBD for slow steaming, and provide a novel tool for sailing speed optimization.
Highlights
The health of marine industry hinges on the environmentally sustainable operations in maritime shipping [1]
Since the exact algorithms are unable to deal with the 3Vs of big data, a genetic algorithm (GA)-based solver is designed to generate the approximate optimal solutions, which reflect the trade-off between fuel consumption, SOx emissions and delivery delay for sailing speed
FUEL CONSUMPTION BASED ON big data analytics (BDA) TECHNIQUES the BDA-based fuel consumption estimation approach and the sailing speed optimization model for slow steaming (SSOM-SS) with GA-based solver are verified with the risk-based decision (RBD) for slow steaming of an Orient Overseas Container Line (OOCL) containership sailing between Dalian and Kaohsiung
Summary
The health of marine industry hinges on the environmentally sustainable operations in maritime shipping [1]. To optimize the sailing speed, the shipping company must make a trade-off, or riskbased decision (RBD), between different operational objectives, namely, fuel consumption, SOx emissions, and delivery delay. This research makes the following contributions: First, a sailing speed optimization model for slow steaming (SSOM-SS) is established to examine the operator’s RBD for slow steaming, aiming to balance the EUOs of fuel consumption, SOx emissions and delivery delay. Three operational objectives are often addressed in existing slow steaming models, including minimal fuel consumption, minimal SOx emissions and minimal delivery delay. The BDA techniques of data parser and data miner were adopted to create data-driven models on the decision support system for sailing speed These models could effectively learn the impact of weather on fuel consumption based on the massive data on historical weather conditions [44]. The operator should determine the proper range of sailing speed V (knots) for slow steaming
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