[Paper Review] An adaptive Simulated Annealing-based satellite observation scheduling method combined with a dynamic task clustering strategy

Guohua Wu, Huilin Wang+5 more|arXiv (Cornell University)|Jan 14, 2014

Satellite Communication Systems32 references17 citations

TL;DR

This paper proposes ASA-DTC, an adaptive Simulated Annealing algorithm integrated with dynamic task clustering for satellite observation scheduling. By dynamically grouping observation tasks and using adaptive temperature control, tabu-list revisiting avoidance, and hybrid neighborhood structures, ASA-DTC significantly improves scheduling efficiency, especially for large or densely distributed target sets, outperforming static clustering approaches in simulation studies.

ABSTRACT

Efficient scheduling is of great significance to rationally make use of scarce satellite resources. Task clustering has been demonstrated to realize an effective strategy to improve the efficiency of satellite scheduling. However, the previous task clustering strategy is static. That is, it is integrated into the scheduling in a two-phase manner rather than in a dynamic fashion, without expressing its full potential in improving the satellite scheduling performance. In this study, we present an adaptive Simulated Annealing based scheduling algorithm aggregated with a dynamic task clustering strategy (or ASA-DTC for short) for satellite observation scheduling problems (SOSPs). First, we develop a formal model for the scheduling of Earth observing satellites. Second, we analyze the related constraints involved in the observation task clustering process. Thirdly, we detail an implementation of the dynamic task clustering strategy and the adaptive Simulated Annealing algorithm. The adaptive Simulated Annealing algorithm is efficient, with the endowment of some sophisticated mechanisms, i.e. adaptive temperature control, tabu-list based revisiting avoidance mechanism, and intelligent combination of neighborhood structures. Finally, we report on experimental simulation studies to demonstrate the competitive performance of ASA-DTC. Moreover, we show that ASA-DTC is especially effective when SOSPs contain a large number of targets or these targets are densely distributed in a certain area.

Motivation & Objective

• Address the inefficiency of static task clustering in satellite observation scheduling by introducing dynamic clustering.
• Improve scheduling performance for Earth observation satellites under complex constraints and high task volumes.
• Develop an adaptive Simulated Annealing algorithm with intelligent mechanisms to enhance convergence and solution quality.
• Optimize resource utilization in satellite scheduling by integrating dynamic task grouping with metaheuristic search.
• Demonstrate superior performance of the proposed method on large-scale and densely distributed observation tasks.

Proposed method

• Formalize the satellite observation scheduling problem using a mathematical model that captures orbital dynamics, task constraints, and resource availability.
• Introduce a dynamic task clustering strategy that groups observation tasks based on spatial and temporal proximity during the optimization process, rather than a priori.
• Implement an adaptive Simulated Annealing algorithm with dynamic temperature reduction to balance exploration and exploitation.
• Incorporate a tabu-list mechanism to prevent revisiting previously explored solutions and improve search efficiency.
• Combine multiple neighborhood structures intelligently to enhance local search effectiveness and solution diversification.
• Integrate dynamic clustering within the Simulated Annealing framework to allow real-time adaptation of task groupings during optimization.

Experimental results

Research questions

• RQ1 How does dynamic task clustering compare to static clustering in improving satellite scheduling efficiency?
• RQ2 Can an adaptive Simulated Annealing algorithm with intelligent mechanisms outperform standard SA in solving complex satellite scheduling problems?
• RQ3 What is the impact of dynamic clustering on solution quality when scheduling a large number of observation tasks?
• RQ4 How does the proposed method perform under dense spatial distribution of observation targets?
• RQ5 To what extent does the integration of adaptive temperature control and tabu-list mechanisms enhance convergence and solution quality?

Key findings

• ASA-DTC significantly improves scheduling performance compared to traditional static clustering methods, particularly in complex scenarios.
• The dynamic task clustering strategy enhances solution quality by enabling more effective grouping of spatially and temporally proximate tasks during optimization.
• The adaptive Simulated Annealing algorithm with temperature control and tabu-list mechanisms reduces redundant search and accelerates convergence.
• ASA-DTC demonstrates superior performance when scheduling a large number of targets or when targets are densely clustered in specific regions.
• Simulation results confirm that the integration of dynamic clustering with adaptive SA leads to higher-quality schedules with improved resource utilization.