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

• URL: http://arxiv.org/abs/2103.05847v1
• Date: Wed, 10 Mar 2021 03:16:12 GMT
• Title: A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems
• Authors: Yongming He, Guohua Wu, Yingwu Chen and Witold Pedrycz
• Abstract summary: We develop a two-stage framework, in which reinforcement learning (RL) and traditional operations research (OR) algorithms are combined together. The scheduling problem is solved in two stages, including a finite Markov decision process (MDP) and a mixed-integer programming process, respectively. Results show that the proposed algorithms could stably and efficiently obtain satisfactory scheduling schemes for agile Earth observation satellite scheduling problems.
• Score: 54.61091936472494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Accelerating Exact Combinatorial Optimization via RL-based Initialization -- A Case Study in Scheduling [1.3053649021965603]
  This research aims to develop an innovative approach that employs machine learning (ML) for addressing optimization problems. We introduce a novel two-phase RL-to-ILP scheduling framework, which includes three steps: 1) solver as coarse-grain scheduler, 2) solution relaxation and 3) exact solving via ILP. Our framework demonstrates the same scheduling performance compared with using exact scheduling methods while achieving up to 128 $times$ speed improvements.
  arXiv  Detail & Related papers  (2023-08-19T15:52:43Z)
• Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates [49.84541884653309]
  A current standard approach to solving convex discrete optimization problems is the use of cutting-plane algorithms. Despite the existence of a number of general-purpose cut-generating algorithms, large-scale discrete optimization problems continue to suffer from intractability. We propose a method for accelerating cutting-plane algorithms via reinforcement learning.
  arXiv  Detail & Related papers  (2023-07-17T20:11:56Z)
• Deep reinforcement learning applied to an assembly sequence planning problem with user preferences [1.0558951653323283]
  We propose an approach to the implementation of DRL methods in assembly sequence planning problems. The proposed approach introduces in the RL environment parametric actions to improve training time and sample efficiency. The results support the potential for the application of deep reinforcement learning in assembly sequence planning problems with human interaction.
  arXiv  Detail & Related papers  (2023-04-13T14:25:15Z)
• Hybrid ACO-CI Algorithm for Beam Design problems [0.4397520291340694]
  A novel hybrid version of the Ant colony optimization (ACO) method is developed using the sample space reduction technique of the Cohort Intelligence (CI) algorithm. The proposed work could be investigate for real world applications encompassing domains of engineering, and health care problems.
  arXiv  Detail & Related papers  (2023-03-29T04:37:14Z)
• On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network [55.56019538079826]
  Bilevel optimization has been applied to a wide variety of machine learning models. Most existing algorithms restrict their single-machine setting so that they are incapable of handling distributed data. We develop novel decentralized bilevel optimization algorithms based on a gradient tracking communication mechanism and two different gradients.
  arXiv  Detail & Related papers  (2022-06-30T05:29:52Z)
• An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem [49.04543375851723]
  Vehicle routing problem (VRP) is a typical discrete optimization problem. Many studies consider learning-based optimization algorithms to solve VRP. This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches.
  arXiv  Detail & Related papers  (2021-07-15T02:13:03Z)
• Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
  This paper investigates the classical integer least-squares problem which estimates signals integer from linear models. The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning. We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
  arXiv  Detail & Related papers  (2021-01-07T08:00:02Z)
• Parallel Scheduling Self-attention Mechanism: Generalization and Optimization [0.76146285961466]
  We propose a general scheduling algorithm, which is derived from the optimum scheduling for small instances solved by a satisfiability checking(SAT) solver. Strategies for further optimization on skipping redundant computations are put forward as well, with which reductions of almost 25% and 50% of the original computations are respectively achieved. The proposed algorithms are applicable regardless of problem sizes, as long as the number of input vectors is divisible to the number of computing units available in the architecture.
  arXiv  Detail & Related papers  (2020-12-02T12:04:16Z)
• Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling [8.14784681248878]
  In this paper, we propose a reinforcement learning approach to solve a realistic scheduling problem. We apply it to an algorithm commonly executed in the high performance computing community, the Cholesky factorization. Our algorithm uses graph neural networks in combination with an actor-critic algorithm (A2C) to build an adaptive representation of the problem on the fly.
  arXiv  Detail & Related papers  (2020-11-09T10:57:21Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.