A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems

Abstract

There hardly exists a general solver that is efficient for scheduling problems due to their diversity and complexity. In this study, we develop a two-stage framework, in which reinforcement learning (RL) and traditional operations research (OR) algorithms are combined together to efficiently deal with complex scheduling problems. The scheduling problem is solved in two stages, including a finite Markov decision process (MDP) and a mixed-integer programming process, respectively. This offers a novel and general paradigm that combines RL with OR approaches to solving scheduling problems, which leverages the respective strengths of RL and OR: The MDP narrows down the search space of the original problem through an RL method, while the mixed-integer programming process is settled by an OR algorithm. These two stages are performed iteratively and interactively until the termination criterion has been met. Under this idea, two implementation versions of the combination methods of RL and OR are put forward. The agile Earth observation satellite scheduling problem is selected as an example to demonstrate the effectiveness of the proposed scheduling framework and methods. The convergence and generalization capability of the methods are verified by the performance of training scenarios, while the efficiency and accuracy are tested in 50 untrained scenarios. The results show that the proposed algorithms could stably and efficiently obtain satisfactory scheduling schemes for agile Earth observation satellite scheduling problems. In addition, it can be found that RL-based optimization algorithms have stronger scalability than non-learning algorithms. This work reveals the advantage of combining reinforcement learning methods with heuristic methods or mathematical programming methods for solving complex combinatorial optimization problems.

An LEO Constellation Early Warning System Decision-Making Method Based on Hierarchical Reinforcement Learning

“…It can effectively solve difficult sample data acquisition problems in the constellation’s early warning system by continuously updating its decision network via the interaction between the intelligence and the environment. Commonly used deep-reinforcement-learning algorithms currently include the following: deep Q-network (DQN) [ 17 , 18 , 19 , 20 ], deep deterministic policy gradient (DDPG) [ 21 , 22 , 23 ], proximal policy optimization (PPO) [ 24 , 25 , 26 ], and soft actor–critic (SAC). These algorithms are widely used in the cooperative positioning of moving targets, agile Earth observation satellite mission scheduling, and the collaborative scheduling of ground satellites.…”

“…Adding local search algorithms to reinforcement learning can further enhance the search capability of the algorithm based on the above advantages. He et al developed a two-stage scheduling algorithm framework that combines Q-learning and traditional mixed-integer programming to achieve multi-satellite task scheduling [ 18 ].…”