Reinforcement Learning-Based and Parametric Production-Maintenance Control Policies for a Deteriorating Manufacturing System

Xanthopoulos, A. S.; Kiatipis, Athanasios; Koulouriotis, Dimitrios E.; Stieger, Sepp

doi:10.1109/access.2017.2771827

Cited by 64 publications

(17 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reinforcement learning explores the space of feasible solutions efficiently and effectively [7] and therefore has been actively applied to solve various optimization problems as alternatives of metaheuristic and rule-based approaches [8][9][10][11][12][13][14]. For example, Dou et al [8] proposed a path planning method for mobile robots in intelligent warehouses based on Q-learning.…”

Section: Introductionmentioning

confidence: 99%

Interactive Q‐Learning Approach for Pick‐and‐Place Optimization of the Die Attach Process in the Semiconductor Industry

Ahn

Park

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In semiconductor back-end production, the die attach process is one of the most critical steps affecting overall productivity. Optimization of this process can be modeled as a pick-and-place problem known to be NP-hard. Typical approaches are rule-based and metaheuristic methods. The two have high or low generalization ability, low or high performance, and short or long search time, respectively. The motivation of this paper is to develop a novel method involving only the strengths of these methods, i.e., high generalization ability and performance and short search time. We develop an interactive Q-learning in which two agents, a pick agent and a place agent, are trained and find a pick-and-place (PAP) path interactively. From experiments, we verified that the proposed approach finds a shorter path than the genetic algorithm given in previous research.

show abstract

Section: Introductionmentioning

confidence: 99%

Interactive Q‐Learning Approach for Pick‐and‐Place Optimization of the Die Attach Process in the Semiconductor Industry

Ahn

Park

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…As maintenance work orders can be modeled as a sequential decisionmaking problem given machine states and system states, RL has also been used in Refs. [56][57][58][59][60] to obtain near-optimal maintenance policies for manufacturing systems. Many studies [57,58,60] integrate multi-agent-based learning and control in their methods, helping dismantle complex structural and operational dependencies among components.…”

Section: Job Dispatching and Schedulingmentioning

confidence: 99%

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

Arinez

Chang

Gao

et al. 2020

Journal of Manufacturing Science and Engineering

290

View full text Add to dashboard Cite

Today's manufacturing systems are becoming increasingly complex, dynamic and connected. The factory operation faces challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in Artificial Intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) Review the State-of-the-Art applications of AI to representative manufacturing problems, (2) Provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) Identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human robotic collaboration, process monitoring, diagnosis and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.

show abstract

“…Their findings show that the combined heuristic provides better prediction results and training times than existing algorithms. Similarly, two conflicting objective functions, inventory level and the number of backorders, were combined in a reinforcement learning model to obtain optimal production/maintenance control policy [28].…”

Section: Related Literature a Inventory Models With Backordersmentioning

confidence: 99%

A Profit Function-Maximizing Inventory Backorder Prediction System Using Big Data Analytics

Hájek

Abedin

2020

IEEE Access

View full text Add to dashboard Cite

Inventory backorder prediction is widely recognized as an important component of inventory models. However, backorder prediction is traditionally based on stochastic approximation, thus neglecting the substantial amount of useful information hidden in historical inventory data. To provide those inventory models with a big data-driven backorder prediction, we propose a machine learning model equipped with an undersampling procedure to maximize the expected profit of backorder decisions. This is achieved by integrating the proposed profit-based measure into the prediction model and optimizing the decision threshold to identify the optimal backorder strategy. We show that the proposed inventory backorder prediction model shows better prediction and profit function performance than the state-of-the-art machine learning methods used for large imbalanced data. Notably, the proposed model is computationally effective and robust to variation in both warehousing/inventory cost and sales margin. In addition, the model predicts both major (non-backorder items) and minor (backorder items) classes in a benchmark dataset. INDEX TERMS Big data, inventory backorder, machine learning, prediction.

show abstract

Reinforcement Learning-Based and Parametric Production-Maintenance Control Policies for a Deteriorating Manufacturing System

Cited by 64 publications

References 27 publications

Interactive Q‐Learning Approach for Pick‐and‐Place Optimization of the Die Attach Process in the Semiconductor Industry

Interactive Q‐Learning Approach for Pick‐and‐Place Optimization of the Die Attach Process in the Semiconductor Industry

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

A Profit Function-Maximizing Inventory Backorder Prediction System Using Big Data Analytics

Contact Info

Product

Resources

About