Investigating Constraint Programming and Hybrid Methods for Real World Industrial Test Laboratory Scheduling

• URL: http://arxiv.org/abs/1911.04766v4
• Date: Wed, 14 Aug 2024 19:34:51 GMT
• Title: Investigating Constraint Programming and Hybrid Methods for Real World Industrial Test Laboratory Scheduling
• Authors: Tobias Geibinger, Florian Mischek, Nysret Musliu,
• Abstract summary: We present different constraint programming models and search strategies for this problem. Our models are evaluated using CP solvers and a MIP solver both on real-world test laboratory data and on a set of generated instances of different sizes.
• Score: 10.568851068989973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we deal with a complex real world scheduling problem closely related to the well-known Resource-Constrained Project Scheduling Problem (RCPSP). The problem concerns industrial test laboratories in which a large number of tests has to be performed by qualified personnel using specialised equipment, while respecting deadlines and other constraints. We present different constraint programming models and search strategies for this problem. Furthermore, we propose a Very Large Neighborhood Search approach based on our CP methods. Our models are evaluated using CP solvers and a MIP solver both on real-world test laboratory data and on a set of generated instances of different sizes based on the real-world data. Further, we compare the exact approaches with VLNS and a Simulated Annealing heuristic. We could find feasible solutions for all instances and several optimal solutions and we show that using VLNS we can improve upon the results of the other approaches.

Related papers

• Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem [1.3927943269211593]
  This paper aims to integrate constraint programming (CP) within a deep learning (DL) based methodology, leveraging the benefits of both. We introduce a method that involves training a DL model using optimal solutions generated by CP, ensuring the model learns from high-quality data. Our hybrid approach has been extensively tested on three public FJSSP benchmarks, demonstrating superior performance over five state-of-the-art DRL approaches.
  arXiv  Detail & Related papers  (2024-03-14T10:16:57Z)
• Combinatorial Optimization with Policy Adaptation using Latent Space Search [44.12073954093942]
  We present a novel approach for designing performant algorithms to solve complex, typically NP-hard, problems. We show that our search strategy outperforms state-of-the-art approaches on 11 standard benchmarking tasks.
  arXiv  Detail & Related papers  (2023-11-13T12:24:54Z)
• An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint Programming [5.070542698701157]
  This paper proposes a novel end-to-end approach to solving scheduling problems by means of CP and Reinforcement Learning (RL) Our approach leverages existing CP solvers to train an agent learning a Priority Dispatching Rule (PDR) that generalizes well to large instances, even from separate datasets.
  arXiv  Detail & Related papers  (2023-06-09T08:24:56Z)
• Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets [86.43523688236077]
  Combinatorial optimization (CO) problems are often NP-hard and out of reach for exact algorithms. GFlowNets have emerged as a powerful machinery to efficiently sample from composite unnormalized densities sequentially. In this paper, we design Markov decision processes (MDPs) for different problems and propose to train conditional GFlowNets to sample from the solution space.
  arXiv  Detail & Related papers  (2023-05-26T15:13:09Z)
• Factorization of Multi-Agent Sampling-Based Motion Planning [72.42734061131569]
  Modern robotics often involves multiple embodied agents operating within a shared environment. Standard sampling-based algorithms can be used to search for solutions in the robots' joint space. We integrate the concept of factorization into sampling-based algorithms, which requires only minimal modifications to existing methods. We present a general implementation of a factorized SBA, derive an analytical gain in terms of sample complexity for PRM*, and showcase empirical results for RRG.
  arXiv  Detail & Related papers  (2023-04-01T15:50:18Z)
• A Reinforcement Learning Approach for Scheduling Problems With Improved Generalization Through Order Swapping [0.0]
  JSSP falls into the category of NP-hard COP, in which solving the problem through exhaustive search becomes unfeasible. In recent years, the research towards using DRL to solve COP has gained interest and has shown promising results in terms of solution quality and computational efficiency. In particular, we employ the PPO algorithm that adopts the policy-gradient paradigm that is found to perform well in the constrained dispatching of jobs.
  arXiv  Detail & Related papers  (2023-02-27T16:45:04Z)
• GLUECons: A Generic Benchmark for Learning Under Constraints [102.78051169725455]
  In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. We model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints.
  arXiv  Detail & Related papers  (2023-02-16T16:45:36Z)
• Minimizing Entropy to Discover Good Solutions to Recurrent Mixed Integer Programs [0.0]
  Current solvers for mixed-integer programming (MIP) problems are designed to perform well on a wide range of problems. Recent works have shown that machine learning (ML) can be integrated with an MIP solver to inject domain knowledge and efficiently close the optimality gap. This paper proposes an online solver that uses the notion of entropy to efficiently build a model with minimal training data and tuning.
  arXiv  Detail & Related papers  (2022-02-07T18:52:56Z)
• Learning Proximal Operators to Discover Multiple Optima [66.98045013486794]
  We present an end-to-end method to learn the proximal operator across non-family problems. We show that for weakly-ized objectives and under mild conditions, the method converges globally.
  arXiv  Detail & Related papers  (2022-01-28T05:53:28Z)
• A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems [54.61091936472494]
  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.
  arXiv  Detail & Related papers  (2021-03-10T03:16:12Z)
• 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.