The use of MOEA/D-DE for multi-period contingency planning in heterogeneous clusters

Abstract

With advancements in science and technology, disaster prediction methods have become more mature, enabling the forecasting of events like typhoons, tsunamis, and floods. However, unpredictable disasters such as earthquakes and car accidents remain challenging, particularly in densely populated areas where even minor incidents can cause significant losses. Emergency rescue operations are crucial after a disaster, but effectively utilizing limited resources to meet dynamic needs and prevent secondary disasters remains a critical issue. The key challenge lies in dynamically scheduling emergency resources across multiple periods to adapt to changing demands during post-disaster scenarios. Uncertainty in disaster situations and evolving needs make it difficult to create effective resource allocation plans. Traditional scheduling methods often fail to address these dynamic conditions, leading to inefficiencies in disaster relief efforts. This paper proposes a kernel function automatic multi-stream resource scheduling method based on a heterogeneous cluster scheduling algorithm. A fast decision-making approach and an improved multi-objective evolutionary algorithm (MOEA/D-DE-mdERS) were developed to optimize resource allocation under dynamic demand conditions. The solution incorporates three adjustment strategies to handle changes in resource needs across cycles and improve scheduling efficiency.Through simulations and a case study of the Taiwan Chi-Chi earthquake, the proposed method demonstrated superior performance compared to traditional algorithms like MOEA/D-SBX. For example, in one resource allocation scenario, demand at a disaster point increased significantly—such as a 6.3-unit rise in the second cycle and an 8-unit increase in the fourth cycle—highlighting the method's ability to adapt to dynamic changes. Additionally, the new algorithm achieved better computational efficiency and solution quality. The proposed method provides an effective solution for multi-period emergency resource scheduling, enhancing disaster response capabilities. By improving resource utilization and reducing losses, this approach supports timely and efficient disaster relief. Future research could extend the model by considering factors like road restoration, transportation modes, and priority grading to further enhance its practical applicability.