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, ...
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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.
M. Rabbani; H. Yousefnejad; H. Rafiei
Volume 3, Issue 1 , May 2014, , Pages 49-54
Abstract
This article attempts to cope with one of the most vital strategic decisions in the supply chain design in terms of manufacturing context. The issues of finding the best position of Customer Order Decoupling Point (CODP) in a production line have been taken into consideration by many researchers in recent ...
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This article attempts to cope with one of the most vital strategic decisions in the supply chain design in terms of manufacturing context. The issues of finding the best position of Customer Order Decoupling Point (CODP) in a production line have been taken into consideration by many researchers in recent years, but locating CODP along a supply chain has not yet been completely investigated. Here we present a novel combined DEA/AHP method to tackle the problem of positioning CODP in a supply chain. Then in order to prove the applicability of the proposed structure in a real case, the model is implemented in a food processing supply chain.
