Dynamic multi-objective scheduling for flexible job shop by deep reinforcement learning

Luo, Shouhua; Zhang, Linxuan; Fan, Yushun

doi:10.1016/j.cie.2021.107489

Cited by 100 publications

(53 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In total, we performed experimental comparisons on five pairs of parameters of n t and τ t , including (5,5), (5,10), (5,15), (5,20), and (10,5). Table 5 shows the DMIGD values for each algorithm on all tested functions.…”

Section: Results On Dmigd Metricmentioning

confidence: 99%

“…Table 6. Mean and SD of MIGD indicator obtained by seven algorithms for (n t , τ t ) = (5, 10), (5,15), and (5,20). From Table 9, we can obviously notice that the performance of KPTHP is significantly better than the other two versions, which suggests that each component of KPTHP has an indispensable influence.…”

Section: Analysis Of the Different Components Of Kpthpmentioning

confidence: 96%

“…Accordingly, dynamic multi-objective optimization algorithms (DMOAs) are powerful tools for solving DMOPs and are widely employed to solve many real-life problems. Common application areas include scheduling [15][16][17], control [18], chemistry [19], industry [20], and energy design [21]. For example, when solving vehicle routing problems for package delivery, not only do the number of vehicles, path lengths, and other objectives need to be optimized in a static manner, but the time window of customers and the dynamically changing topology of customers should be considered to satisfy the needs of practical scenarios [22].…”

Section: Introductionmentioning

confidence: 99%

“…Mean and SD of HVD indicator obtained by seven algorithms for (n t , τ t ) = (5,10),(5,15), and(5,20).…”

mentioning

confidence: 99%

See 3 more Smart Citations

Combining Key-Points-Based Transfer Learning and Hybrid Prediction Strategies for Dynamic Multi-Objective Optimization

Wang

2022

Mathematics

View full text Add to dashboard Cite

Dynamic multi-objective optimization problems (DMOPs) have been of interest to many researchers. These are problems in which the environment changes during the evolutionary process, such as the Pareto-optimal set (POS) or the Pareto-optimal front (POF). This kind of problem imposes more challenges and difficulties for evolutionary algorithms, mainly because it demands population to track the changing POF efficiently and accurately. In this paper, we propose a new approach combining key-points-based transfer learning and hybrid prediction strategies (KPTHP). In particular, the transfer process combines predictive strategy with obtaining anticipated key points depending on the previous moments to acquire the optimal individuals at the new instance during the evolution. Additionally, center-point-based prediction is used to complement transfer learning to comprehensively generate initial populations. KPTHP and six state-of-the-art algorithms are tested on various test functions for MIGD, DMIGD, MMS, and HVD metrics. KPTHP obtains superior results on most of the tested functions, which shows that our algorithm performs excellently in both convergence and diversity, with more competitiveness in addressing dynamic problems.

show abstract

Section: Results On Dmigd Metricmentioning

confidence: 99%

Section: Analysis Of the Different Components Of Kpthpmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

“…Mean and SD of HVD indicator obtained by seven algorithms for (n t , τ t ) = (5,10),(5,15), and(5,20).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Combining Key-Points-Based Transfer Learning and Hybrid Prediction Strategies for Dynamic Multi-Objective Optimization

Wang

2022

Mathematics

View full text Add to dashboard Cite

show abstract

“…However, most RL-based scheduling algorithms are inefficient at learning to optimize the decision-making policies when multiple objectives are considered in a smart manufacturing factory. 7 The overall performances of manufacturing systems are influenced by many factors such as order requirements, machine properties, and supply chain profits, which can be transformed into composite reward functions in RL-based scheduling systems. This paper realizes online scheduling based on RL with composite reward functions, which makes manufacturing systems to be more efficient and robust.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning for online optimization of job-shop scheduling in a smart manufacturing factory

Zhou

Zhu

Tang

et al. 2022

Advances in Mechanical Engineering

View full text Add to dashboard Cite

The job-shop scheduling problem (JSSP) is a complex combinatorial problem, especially in dynamic environments. Low-volume-high-mix orders contain various design specifications that bring a large number of uncertainties to manufacturing systems. Traditional scheduling methods are limited in handling diverse manufacturing resources in a dynamic environment. In recent years, artificial intelligence (AI) arouses the interests of researchers in solving dynamic scheduling problems. However, it is difficult to optimize the scheduling policies for online decision making while considering multiple objectives. Therefore, this paper proposes a smart scheduler to handle real-time jobs and unexpected events in smart manufacturing factories. New composite reward functions are formulated to improve the decision-making abilities and learning efficiency of the smart scheduler. Based on deep reinforcement learning (RL), the smart scheduler autonomously learns to schedule manufacturing resources in real time and improve its decision-making abilities dynamically. We evaluate and validate the proposed scheduling model with a series of experiments on a smart factory testbed. Experimental results show that the smart scheduler not only achieves good learning and scheduling performances by optimizing the composite reward functions, but also copes with unexpected events (e.g. urgent or simultaneous orders, machine failures) and balances between efficiency and profits.

show abstract

Stochastic parallel machine scheduling using reinforcement learning

Julaiti

Park

Das

et al. 2022

J Adv Manuf & Process

View full text Add to dashboard Cite

In a high‐mix and low‐volume manufacturing facility, heterogeneous jobs introduce frequent reconfiguration of machines which increases the chance of unplanned machine breakdowns. As machines are often nonidentical and their performance degrades over time, it is critical to consider the heterogeneity and non‐stationarity of the machines during scheduling. We propose a reinforcement learning‐based framework with a novel sampling method to train the agent to schedule heterogeneous jobs on non‐stationary unreliable parallel machines to minimize weighted tardiness. The results indicate that the new sampling approach expedites the learning process and the resulting policy significantly outperforms static dispatching rules.

show abstract

Dynamic multi-objective scheduling for flexible job shop by deep reinforcement learning

Cited by 100 publications

References 35 publications

Combining Key-Points-Based Transfer Learning and Hybrid Prediction Strategies for Dynamic Multi-Objective Optimization

Combining Key-Points-Based Transfer Learning and Hybrid Prediction Strategies for Dynamic Multi-Objective Optimization

Reinforcement learning for online optimization of job-shop scheduling in a smart manufacturing factory

Stochastic parallel machine scheduling using reinforcement learning

Contact Info

Product

Resources

About