۱- Introduction

۲- Literature review

۳- Problem formulation

۴- Decision support system for big data based speed optimization

۵- Computational study

۶- Conclusion

References

Abstract

Speed optimization of liner vessels has significant economic and environmental impact for reducing fuel cost and Green House Gas (GHG) emission as the shipping over maritime logistics takes more than 70% of world transportation. While slow steaming is widely used as best practices for liner shipping companies, they are also under the pressure to maintain service level agreement (SLA) with their cargo clients. Thus, deciding optimal speed that minimizes fuel consumption while maintaining SLA is managerial decision problem. Studies in the literature use theoretical fuel consumption functions in their speed optimization models but these functions have limitations due to weather conditions in voyages. This paper uses weather archive data to estimate the real fuel consumption function for speed optimization problems. In particular, Copernicus data set is used as the source of big data and data mining technique is applied to identify the impact of weather conditions based on a given voyage route. Particle swarm optimization, a metaheuristic optimization method, is applied to find Pareto optimal solutions that minimize fuel consumption and maximize SLA. The usefulness of the proposed approach is verified through the real data obtained from a liner company and real world implications are discussed.

Introduction

Speed optimization in liner shipping has significant economic and environmental impact for reducing fuel cost and Green House Gas (GHG) emission as the shipping over maritime logistics takes more than 70% of world transportation (UNCTAD, 2010; Psaraftis and Kontovas, 2013). While slow steaming is widely used as best practices for liner shipping companies, they are also under the pressure to maintain service level agreement (SLA) with their cargo clients (Lee et al., 2015; Parthibaraj et al., 2016). Thus, deciding optimal sailing speed which minimizes fuel consumption while maintaining SLA is an important managerial decision problem for liner companies. Sailing speed decision mainly depends on the vessel schedule and it is a challenging problem due to the uncertainties imposed in maritime logistics such as stochastic port times and weather conditions. Port time uncertainty significantly affects the time that vessels spend at ports in anchorage, berthing, unberthing or drifting status. Increased port congestion and delays can negatively affect service level of shipping lines to their customers and put pressure on schedule reliability and might incur logistics costs to the customer (Notteboom, 2006). On the other hand, weather conditions including current and wind affect journey times and the routing decisions (Kontovas, 2014). The majority the literature work on the speed optimization problem based on a theoretical fuel consumption function. For example, Fagerholt et al., (2010) and Yao et al., (2012) propose a fuel consumption function which is based on the empirical data from a shipping company. However, these functions do not reflect the actual fuel consumption of vessels that are affected by weather conditions. In reality, certain routes may encounter harsher weather conditions than others and speed optimization needs to consider such different voyage environments.