Modeling the speed-based vessel schedule recovery problem using evolutionary multiobjective optimization

Liner shipping is vulnerable to many disruptive factors such as port congestion or harsh weather, which could result to delay in arriving at the ports. It could result in both financial and reputation losses. The vessel schedule recovery problem (VSRP) is concerned with different possible actions to reduce the effect of disruption. In this work, we are concerned with speeding up strategy in VSRP, which is called the speed-based vessel schedule recovery problem (S-VSRP). We model S-VSRP as a multiobjective optimization (MOO) problem and resort to several multiobjective evolutionary algorithms (MOEAs) to approximate the optimal Pareto set, which provides vessel route-based speed profiles. It gives the stakeholders the ability to tradeoff between two conflictive objectives: total delay and financial loss. We evaluate the problem in three scenarios (i.e., scalability analysis, vessel steaming policies, and voyage distance analysis) and statistically validate their performance significance. According to experiments, the problem complexity varies in different scenarios, and NSGAII performs better than other MOEAs in all scenarios.