Machine Learning
Mohammad Reza Nazabadi; Seyed Esmaeil Najafi; Ali Mohaghar; Farzad Movahedi Sobhani
Abstract
Adopting an integrated production, maintenance, and quality policy in production systems is of great importance due to their interconnected influence. Consequently, investigating these aspects in isolation may yield an infeasible solution. This paper aims to address the joint optimal policy of production, ...
Read More
Adopting an integrated production, maintenance, and quality policy in production systems is of great importance due to their interconnected influence. Consequently, investigating these aspects in isolation may yield an infeasible solution. This paper aims to address the joint optimal policy of production, maintenance, and quality in a two-machine-single-product production system with an intermediate buffer and final product storage. The production machines have degradation levels from as-good-as-new to the breakdown state. The failures increase the production machine's degradation level, and maintenance activities change the status to the initial state. Also, the quality of the final product depends on the level of degradation of the machines and the correlation between the degradation level of the production machines and the product's quality in the case that high degradation of the previous production machines leads to a high probability to produce wastage by the following machines is considered. The production system studied in this research has been modeled using the agent-based simulation, and the Reinforcement Learning (RL) algorithm has obtained the optimal integrated policy. The goal is to find an integrated optimal policy that minimizes production costs, maintenance costs, inventory costs, lost orders, breakdown of production machines, and low-quality production. The meta-heuristic technique evaluates the joint policy obtained by the decision-maker agent. The results show that the acquired joint policy by the RL algorithm offers acceptable performance and can be applied to the autonomous real-time decision-making process in manufacturing systems.
R. Hosseiny; V. Amirzadeh; M. A. Yaghoobi
Volume 3, Issue 1 , May 2014, , Pages 26-38
Abstract
Although a control chart can signal an out-of-control state in a process, but it does not always indicate when the process change has begun. Identifying the real time of the change in the process, called the change point, is very important for eliminating the source(s) of the change and assists process ...
Read More
Although a control chart can signal an out-of-control state in a process, but it does not always indicate when the process change has begun. Identifying the real time of the change in the process, called the change point, is very important for eliminating the source(s) of the change and assists process engineers in identifying the responsible special cause and ul t imately in improving the proces s. In this paper, we first introduce an estimator for a change point with linear trend in the Poisson process, based on the likelihood function using a slope parameter. Then we apply Monte Carlo simulation to evaluate the accuracy and the precision performance of the proposed change point estimator. Finally we compare, the proposed estimator with the MLE of the Poisson process change point derived under linear trend disturbance on the basis of cumulative sum (CUSUM) and Shewhart C control charts. The results show that the proposed procedure outperforms the MLE designed for drift time with regard to variance and is more effective in detecting drift time when the magnitude of change is relatively large.