Solving the Extended Job Shop Scheduling Problem with AGVs -- Classical
and Quantum Approaches
- URL: http://arxiv.org/abs/2109.04830v1
- Date: Fri, 10 Sep 2021 12:28:51 GMT
- Title: Solving the Extended Job Shop Scheduling Problem with AGVs -- Classical
and Quantum Approaches
- Authors: Marc Geitz, Cristian Grozea, Wolfgang Steigerwald, Robin St\"ohr, and
Armin Wolf
- Abstract summary: In this paper a use case is provided which deals with a sub-aspect of JSO, the Job Shop Scheduling Problem (JSSP)
The goal of the use case is to show how to create an optimized duty rooster for certain projects in a flexible organized machinery.
The results of a classical solution based on CP and on an Annealing model are presented and discussed.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The subject of Job Scheduling Optimisation (JSO) deals with the scheduling of
jobs in an organization, so that the single working steps are optimally
organized regarding the postulated targets. In this paper a use case is
provided which deals with a sub-aspect of JSO, the Job Shop Scheduling Problem
(JSSP or JSP). As many optimization problems JSSP is NP-complete, which means
the complexity increases with every node in the system exponentially. The goal
of the use case is to show how to create an optimized duty rooster for certain
workpieces in a flexible organized machinery, combined with an Autonomous
Ground Vehicle (AGV), using Constraint Programming (CP) and Quantum Computing
(QC) alternatively. The results of a classical solution based on CP and on a
Quantum Annealing model are presented and discussed. All presented results have
been elaborated in the research project PlanQK.
Related papers
- Parallel Strategies for Best-First Generalized Planning [51.713634067802104]
Generalized planning (GP) is a research area of AI that studies the automated synthesis of algorithmic-like solutions capable of solving multiple classical planning instances.
One of the current advancements has been the introduction of Best-First Generalized Planning (BFGP), a GP algorithm based on a novel solution space that can be explored with search.
This paper evaluates the application of parallel search techniques to BFGP, another critical component in closing the performance gap.
arXiv Detail & Related papers (2024-07-31T09:50:22Z) - Multi-objective Quantum Annealing approach for solving flexible job shop
scheduling in manufacturing [0.0]
This paper introduces Quantum Annealing-based solving algorithm (QASA) to address Flexible Job Shop Scheduling problem.
Experiments on benchmark problems show QASA, combining tabu search, simulated annealing, and Quantum Annealing, outperforms a classical solving algorithm (CSA) in solution quality.
arXiv Detail & Related papers (2023-11-16T07:45:57Z) - Residual Scheduling: A New Reinforcement Learning Approach to Solving
Job Shop Scheduling Problem [8.398387430247201]
Job-shop scheduling problem (JSP) is a mathematical optimization problem widely used in industries like manufacturing.
In this paper, we propose a new approach, named residual scheduling, to solving/FJSP.
Our approach even reaches zero gap for 49 among 50 instances whose job numbers are more than 150 on 20 machines.
arXiv Detail & Related papers (2023-09-27T09:33:56Z) - 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) - 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) - The Basis of Design Tools for Quantum Computing: Arrays, Decision
Diagrams, Tensor Networks, and ZX-Calculus [55.58528469973086]
Quantum computers promise to efficiently solve important problems classical computers never will.
A fully automated quantum software stack needs to be developed.
This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.
arXiv Detail & Related papers (2023-01-10T19:00:00Z) - Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time.
As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware.
We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
arXiv Detail & Related papers (2022-02-17T18:59:20Z) - Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep
Learning Method [44.4747903763245]
The Jobs shop Scheduling Problem (JSP) is a canonical optimization problem that is routinely solved for a variety of industrial purposes.
The problem is NP-hard and computationally challenging even for medium-sized instances.
This paper explores a deep learning approach to deliver efficient and accurate approximations to the problem.
arXiv Detail & Related papers (2021-10-12T21:15:19Z) - Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal
Constraints [52.58352707495122]
We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination.
We validate our results over a wide range of simulations on two distinct domains: multi-arm conveyor belt pick-and-place and multi-drone delivery dispatch in a city.
arXiv Detail & Related papers (2020-05-27T01:10:41Z) - Cross Entropy Hyperparameter Optimization for Constrained Problem
Hamiltonians Applied to QAOA [68.11912614360878]
Hybrid quantum-classical algorithms such as Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of near-term quantum computers in practical applications.
Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem.
In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical parameter to find better parameters more easily and hence results in an improved performance.
arXiv Detail & Related papers (2020-03-11T13:52:41Z)
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.