273 research outputs found
Scheduling Algorithms: Challenges Towards Smart Manufacturing
Collecting, processing, analyzing, and driving knowledge from large-scale real-time data is now realized with the emergence of Artificial Intelligence (AI) and Deep Learning (DL). The breakthrough of Industry 4.0 lays a foundation for intelligent manufacturing. However, implementation challenges of scheduling algorithms in the context of smart manufacturing are not yet comprehensively studied. The purpose of this study is to show the scheduling No.s that need to be considered in the smart manufacturing paradigm. To attain this objective, the literature review is conducted in five stages using publish or perish tools from different sources such as Scopus, Pubmed, Crossref, and Google Scholar. As a result, the first contribution of this study is a critical analysis of existing production scheduling algorithms\u27 characteristics and limitations from the viewpoint of smart manufacturing. The other contribution is to suggest the best strategies for selecting scheduling algorithms in a real-world scenario
Towards Standardising Reinforcement Learning Approaches for Production Scheduling Problems
Recent years have seen a rise in interest in terms of using machine learning,
particularly reinforcement learning (RL), for production scheduling problems of
varying degrees of complexity. The general approach is to break down the
scheduling problem into a Markov Decision Process (MDP), whereupon a simulation
implementing the MDP is used to train an RL agent. Since existing studies rely
on (sometimes) complex simulations for which the code is unavailable, the
experiments presented are hard, or, in the case of stochastic environments,
impossible to reproduce accurately. Furthermore, there is a vast array of RL
designs to choose from. To make RL methods widely applicable in production
scheduling and work out their strength for the industry, the standardisation of
model descriptions - both production setup and RL design - and validation
scheme are a prerequisite. Our contribution is threefold: First, we standardize
the description of production setups used in RL studies based on established
nomenclature. Secondly, we classify RL design choices from existing
publications. Lastly, we propose recommendations for a validation scheme
focusing on reproducibility and sufficient benchmarking
Curriculum Learning in Job Shop Scheduling using Reinforcement Learning
Solving job shop scheduling problems (JSSPs) with a fixed strategy, such as a priority dispatching rule, may yield satisfactory results for several problem instances but, nevertheless, insufficient results for others. From this single-strategy perspective finding a near optimal solution to a specific JSSP varies in difficulty even if the machine setup remains the same. A recent intensively researched and promising method to deal with difficulty variability is Deep Reinforcement Learning (DRL), which dynamically adjusts an agent's planning strategy in response to difficult instances not only during training, but also when applied to new situations. In this paper, we further improve DLR as an underlying method by actively incorporating the variability of difficulty within the same problem size into the design of the learning process. We base our approach on a state-of-the-art methodology that solves JSSP by means of DRL and graph neural network embeddings. Our work supplements the training routine of the agent by a curriculum learning strategy that ranks the problem instances shown during training by a new metric of problem instance difficulty. Our results show that certain curricula lead to significantly better performances of the DRL solutions. Agents trained on these curricula beat the top performance of those trained on randomly distributed training data, reaching 3.2% shorter average makespans
Application of a Reinforcement Learning-based Automated Order Release in Production
The importance of job shop production is increasing in order to meet the customer-driven greater demand
for products with a larger number of variants in small quantities. However, it also leads to higher
requirements for the production planning and control. In order to meet logistical target values and customer
needs, one approach is the focus on dynamic planning systems, which can reduce ad-hoc control
interventions in the running production. In particular, the release of orders at the beginning of the production
process has a high influence on the planning quality. Previous approaches used advanced methods such as
combinations of reinforcement learning (RL) and simulation to improve specific production environments,
which are sometimes highly simplified and not practical enough. This paper presents a practice-based
application of an automated order release procedure based on RL using the example of real-world production
scenarios. Both, the training environment, and the data processing method are introduced. Primarily, three
aspects to achieve a higher practical orientation are addressed: A more realistic problem size compared to
previous approaches, a higher customer orientation by means of an objective regarding adherence to delivery
date and a control application for development and performance evaluation of the considered algorithms
against known order release strategies. Follow-up research will refine the objective function, continue to
scale-up the problem size and evaluate the algorithm’s scheduling results in case of changes in the system
On The Effectiveness Of Bottleneck Information For Solving Job Shop Scheduling Problems Using Deep Reinforcement Learning
Job shop scheduling problems (JSSPs) have been the subject of intense studies for decades because they are often at the core of significant industrial planning challenges and have a high optimization potential. As a result, the scientific community has developed clever heuristics to approximate optimal solutions. A prominent example is the shifting bottleneck heuristic, which iteratively identifies bottlenecks in the current schedule and uses this information to apply targeted optimization steps. In recent years, deep reinforcement learning (DRL) has gained increasing attention for solving scheduling problems in job shops and beyond. One design decision when applying DRL to JSSPs is the observation, i.e., the descriptive representation of the current problem and solution state. Interestingly, DRL solutions do not make use of explicit notions of bottlenecks that have been developed in the past when designing the observation. In this paper, we investigate ways to leverage a definition of bottlenecks inspired by the shifting bottleneck heuristic for JSSPs with DRL to increase the effectiveness and efficiency of model training. To this end, we train two different DRL base models with and without bottleneck features. However, our results indicate that previously developed bottleneck definitions neither increase training efficiency nor final model performance
Solving large flexible job shop scheduling instances by generating a diverse set of scheduling policies with deep reinforcement learning
The Flexible Job Shop Scheduling Problem (FJSSP) has been extensively studied
in the literature, and multiple approaches have been proposed within the
heuristic, exact, and metaheuristic methods. However, the industry's demand to
be able to respond in real-time to disruptive events has generated the
necessity to be able to generate new schedules within a few seconds. Among
these methods, under this constraint, only dispatching rules (DRs) are capable
of generating schedules, even though their quality can be improved. To improve
the results, recent methods have been proposed for modeling the FJSSP as a
Markov Decision Process (MDP) and employing reinforcement learning to create a
policy that generates an optimal solution assigning operations to machines.
Nonetheless, there is still room for improvement, particularly in the larger
FJSSP instances which are common in real-world scenarios. Therefore, the
objective of this paper is to propose a method capable of robustly solving
large instances of the FJSSP. To achieve this, we propose a novel way of
modeling the FJSSP as an MDP using graph neural networks. We also present two
methods to make inference more robust: generating a diverse set of scheduling
policies that can be parallelized and limiting them using DRs. We have tested
our approach on synthetically generated instances and various public benchmarks
and found that our approach outperforms dispatching rules and achieves better
results than three other recent deep reinforcement learning methods on larger
FJSSP instances
Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning
Flexible manufacturing has given rise to complex scheduling problems such as
the flexible job shop scheduling problem (FJSP). In FJSP, operations can be
processed on multiple machines, leading to intricate relationships between
operations and machines. Recent works have employed deep reinforcement learning
(DRL) to learn priority dispatching rules (PDRs) for solving FJSP. However, the
quality of solutions still has room for improvement relative to that by the
exact methods such as OR-Tools. To address this issue, this paper presents a
novel end-to-end learning framework that weds the merits of self-attention
models for deep feature extraction and DRL for scalable decision-making. The
complex relationships between operations and machines are represented precisely
and concisely, for which a dual-attention network (DAN) comprising several
interconnected operation message attention blocks and machine message attention
blocks is proposed. The DAN exploits the complicated relationships to construct
production-adaptive operation and machine features to support high-quality
decisionmaking. Experimental results using synthetic data as well as public
benchmarks corroborate that the proposed approach outperforms both traditional
PDRs and the state-of-the-art DRL method. Moreover, it achieves results
comparable to exact methods in certain cases and demonstrates favorable
generalization ability to large-scale and real-world unseen FJSP tasks
A dynamic scheduling method with Conv-Dueling and generalized representation based on reinforcement learning
In modern industrial manufacturing, there are uncertain dynamic disturbances between processing machines and jobs which will disrupt the original production plan. This research focuses on dynamic multi-objective flexible scheduling problems such as the multi-constraint relationship among machines, jobs, and uncertain disturbance events. The possible disturbance events include job insertion, machine breakdown, and processing time change. The paper proposes a conv-dueling network model, a multidimensional state representation of the job processing information, and multiple scheduling objectives for minimizing makespan and delay time, while maximizing the completion punctuality rate. We design a multidimensional state space that includes job and machine processing information, an efficient and complete intelligent agent scheduling action space, and a compound scheduling reward function that combines the main task and the branch task. The unsupervised training of the network model utilizes the dueling-double-deep Q-network (D3QN) algorithm. Finally, based on the multi-constraint and multi-disturbance production environment information, the multidimensional state representation matrix of the job is used as input and the optimal scheduling rules are output after the feature extraction of the conv-dueling network model and decision making. This study carries out simulation experiments on 50 test cases. The results show the proposed conv-dueling network model can quickly converge for DQN, DDQN, and D3QN algorithms, and has good stability and universality. The experimental results indicate that the scheduling algorithm proposed in this paper outperforms DQN, DDQN, and single scheduling algorithms in all three scheduling objectives. It also demonstrates high robustness and excellent comprehensive scheduling performance
A deep reinforcement learning based scheduling policy for reconfigurable manufacturing systems
Reconfigurable manufacturing systems (RMS) is one of the trending paradigms toward a digitalised factory. With its rapid reconfiguring capability, finding a far-sighted scheduling policy is challenging. Reinforcement learning is well-equipped for finding highly efficient production plans that would bring near-optimal future rewards. For minimising reconfiguring actions, this paper uses a deep reinforcement learning agent to make autonomous decision with a built-in discrete event simulation model of a generic RMS. Aiming at the completion of the assigned order lists while minimising the reconfiguration actions, the agent outperforms the conventional first-in-first-out dispatching rule after self-learning
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Intelligent Scheduling Based on Reinforcement Learning Approaches: Applying Advanced Q-Learning and State–Action–Reward–State–Action Reinforcement Learning Models for the Optimisation of Job Shop Scheduling Problems
Data Availability Statement: The data that support the findings of this study are available from the corresponding author, M.A., upon reasonable request.Copyright © 2023 by the authors. Flexible job shop scheduling problems (FJSPs) have attracted significant research interest because they can considerably increase production efficiency in terms of energy, cost and time; they are considered the main part of the manufacturing systems which frequently need to be resolved to manage the variations in production requirements. In this study, novel reinforcement learning (RL) models, including advanced Q-learning (QRL) and RL-based state–action–reward–state–action (SARSA) models, are proposed to enhance the scheduling performance of FJSPs, in order to reduce the total makespan. To more accurately depict the problem realities, two categories of simulated single-machine job shops and multi-machine job shops, as well as the scheduling of a furnace model, are used to compare the learning impact and performance of the novel RL models to other algorithms. FJSPs are challenging to resolve and are considered non-deterministic polynomial-time hardness (NP-hard) problems. Numerous algorithms have been used previously to solve FJSPs. However, because their key parameters cannot be effectively changed dynamically throughout the computation process, the effectiveness and quality of the solutions fail to meet production standards. Consequently, in this research, developed RL models are presented. The efficacy and benefits of the suggested SARSA method for solving FJSPs are shown by extensive computer testing and comparisons. As a result, this can be a competitive algorithm for FJSPs.There is not funding for this research. The APC was funded by Brunel University London
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