In recent years, deep reinforcement learning (DRL) models have demonstrated potential in effectively addressing the Agile Earth Observation Satellite Scheduling Problem (AEOSSP). However, these policy models prioritize optimizing overall policy expectations over individual decision accuracy, resulting in decision errors in certain scenes. To mitigate this issue, we propose a DRL-based self-repair construction method (SRCM), which is a two-stage method that includes an improved construction model (ICM) and a self-repair process (SRP). The ICM, an encoder-decoder based neural policy model, is specifically designed to construct initial solutions for the AEOSSP. The SRP incorporates two mechanisms Relaxation-Insertion (RI) and Relaxation-Masking (RM) to investigate and repair decision-making errors in DRL model solutions. Comparative experiments demonstrate that the proposed SRCM surpasses the state-of-the-art problem-specific meta-heuristics in both solution quality and timeliness. The results from our model study indicate that the ICM within SRCM outperforms other neural policy models in extensive validations. Moreover, the mechanism study of the SRP shows that decision-making errors are prevalent across all test neural policy models and confirms the effectiveness of the SRP in rectifications.

In a local search algorithm, one of its most important features is the definition of its neighborhood which is crucial to the algorithm’s performance. In this paper, we present an analysis of neighborhood combination search for solving the single-machine scheduling problem with sequence-dependent setup time with the objective of minimizing total weighted tardiness (SMSWT). First, We propose a new neighborhood structure named Block Swap (B1) which can be considered as an extension of the previously widely used Block Move (B2) neighborhood, and a fast incremental evaluation technique to enhance its evaluation efficiency. Second, based on the Block Swap and Block Move neighborhoods, we present two kinds of neighborhood structures: neighborhood union (denoted by B1 $\cup$B2) and token-ring search (denoted by B1 $\to$B2), both of which are combinations of B1 and B2. Third, we incorporate the neighborhood union and token-ring search into two representative metaheuristic algorithms: the Iterated Local Search Algorithm ( ${\mathrm{I}\mathrm{L}\mathrm{S}}_{\mathrm{n}\mathrm{e}\mathrm{w}}$) and the Hybrid Evolutionary Algorithm (HEA ${}_{\mathrm{n}\mathrm{e}\mathrm{w}}$) to investigate the performance of the neighborhood union and token-ring search. Extensive experiments show the competitiveness of the token-ring search combination mechanism of the two neighborhoods. Tested on the 120 public benchmark instances, our HEA ${}_{\text{new}}$ has a highly competitive performance in solution quality and computational time compared with both the exact algorithms and recent metaheuristics. We have also tested the HEA ${}_{\text{new}}$ algorithm with the selected neighborhood combination search to deal with the 64 public benchmark instances of the single-machine scheduling problem with sequence-dependent setup time. HEA ${}_{\text{new}}$ is able to match the optimal or the best known results for all the 64 instances. In particular, the computational time for reaching the best well-known results for five challenging instances is reduced by at least 61.25%.

An agile earth-observing satellite equipped with multimode cameras capable of transmitting observation data to other satellites is developed to rapidly respond to requests with multiple observation modes. This gives rise to the Multisatellite Multimode Crosslink Scheduling (MMCS) problem, which involves allocating observation requests to agile satellites, selecting appropriate timing and observation modes for the requests, and transmitting the data to the ground station via the satellite communication system. Herein, a mixed integer programming model is introduced to include all complex time and operation constraints. To solve the MMCS problem, a two-stage heuristic method, called Fast insertion Tabu Search with Conflict-avoidance (FTS-C) heuristic, is developed. In the first stage, a conflict-avoidance insertion algorithm is designed to generate a high-quality initial solution by considering the requests transmission and download. Further, the tabu search-based second stage optimizes the initial solution. Finally, an extensive empirical study based on a real-world situation demonstrates that FTS-C can generate a solution with higher quality in less time than other state-of-the-art algorithms and the CPLEX solver.