Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

In the immediate aftermath of large-scale disasters, emergency logistics services play important roles in saving lives and reducing losses. Efficient relief logistics scheduling depends on the accurate transport time information for available routes. However, this information cannot be obtained precisely until a vehicle uses the road. Considering the correlation between information acquisition and logistics operations, this paper focuses on a multiperiod online decision-making problem to simulate the information acquiring process. This problem can be referenced for emergency resource scheduling scenarios in which previous decisions impact knowledge for future logistics plans. A multi-trip cumulative capacitated vehicle routing problem with uncertain transportation time is investigated as the basic model. The tradeoff between transportation efficiency and the unknown transport time discovery rate is considered in a multiobjective evolutionary algorithm (MOEA). A memetic algorithm (MA) and a robust optimization (RO) -MA for single-period post-disaster emergency logistics are also proposed to solve the problem for comparison. In these algorithms, evolutionary operators that benefit solution fixing and variation are proposed. In the experiments, a real-world instance is employed. A simulative experimental environment is established. Dynamic information gained within the process of logistics scheduling is highlighted via multi-period online optimization. Different scenarios corresponding to estimates in emergency situations are provided to validate the performance of the algorithms. The experimental results show that the hybrid strategy, MOEA+MA, can obtain the best result in more than half of the considered cases which demonstrates the necessary balance between obtaining information and transportation efficiency.

Automated summary

This paper focuses on a multiperiod online decision-making problem for emergency logistics, using a multi-trip cumulative capacitated vehicle routing problem with uncertain transportation time as the basic model. A multiobjective evolutionary algorithm (MOEA) is employed to consider the tradeoff between transportation efficiency and the unknown transport time discovery rate. Experimental results show that the hybrid strategy, MOEA+MA, can achieve the best result in more than half of the cases, demonstrating the necessary balance between obtaining information and transportation efficiency.