A Q-learning-based adaptive grouping policy for condition-based maintenance of a flow line manufacturing system

2011 ◽  
Vol 2 (4) ◽  
pp. 302 ◽  
Author(s):  
Yusuf Ozbek ◽  
Abe Zeid ◽  
Sagar Kamarthi
Keyword(s):  
Manufacturing System ◽  
Flow Line ◽  
Condition Based Maintenance ◽  
Q Learning

Dynamic Maintenance Strategy With Q-Learning for Workstations in a Flow Line Manufacturing System

2010 ◽  
Author(s):  
Sagar Kamarthi ◽  
Abe Zeid ◽  
Yusuf Ozbek
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Flow Line ◽  
Maintenance Cost ◽  
Q Learning ◽  
Intelligent Maintenance ◽  
Dynamic Maintenance ◽  
Maintenance Systems ◽  
Modern Manufacturing ◽  
Production Losses

Every machine or equipment in a manufacturing facility is subject to failure due to deterioration based on cumulative wear, crack growth, erosion, etc. This failure will cause production losses and delays resulting in high costs. As the modern manufacturing systems are getting more and more complex, intelligent maintenance schemes must replace the old labor intensive planned maintenance systems to ensure that equipment continues to function. If the maintenance decision is based on the state of the system rather than its age, this leads to the choice of a Condition Based Maintenance (CBM) policy to prevent catastrophic unexpected machine breakdowns and increase the availability of individual machines, but it also introduces randomness into the manufacturing operation. This paper presents a Q-Learning model to dynamically group maintenance actions on different machines and execute them simultaneously, so that one can reduce maintenance cost and increase the efficiency of the manufacturing system.

Genetic Modelling for Selecting Optimal Machine Configurations in Reconfigurable Manufacturing System

2015 ◽  
Vol 789-790 ◽  
pp. 1229-1239 ◽  
Author(s):  
Faisal Hasan ◽  
P.K. Jain
Keyword(s):  
Objective Function ◽  
Manufacturing System ◽  
Flow Line ◽  
Reconfigurable Manufacturing ◽  
Performance Parameters ◽  
Product Flow ◽  
Trade Offs ◽  
Machine Assignment ◽  
Genetic Modelling ◽  
Optimal Machine

Selecting the optimum machine configurations for any product flow line has direct implications on the system performance. In the present paper, an evolutionary algorithm based methodology has been proposed for optimal machine assignment based on a weighted objective function. The objective function includes reliability, cost, production time and operational capability as performance indicators. The methodology demonstrates how several performance parameters can be dealt with, in order to select optimal machine configurations for distinct stages across any serial product flow line. The proposed approach can possibly be employed in handling the RMS performance issues and optimal trade-offs among the various performance parameters.

Mixed integer programming models for concurrent configuration design and scheduling in a reconfigurable manufacturing system

2020 ◽  
Vol 28 (1) ◽  
pp. 32-46 ◽  
Author(s):  
Jianping Dou ◽  
Chun Su ◽  
Xia Zhao
Keyword(s):  
Mixed Integer Linear Programming ◽  
Manufacturing System ◽  
Programming Model ◽  
Flow Line ◽  
Mixed Integer ◽  
Configuration Design ◽  
Reconfigurable Manufacturing ◽  
Reconfigurable Manufacturing System ◽  
Line Configuration ◽  
Constraint Method

A reconfigurable manufacturing system can evolve its configuration to offer exactly the capacity and functionality needed for every demand period. For the reconfigurable manufacturing system with multi-part flow-line configuration simultaneously producing multiple parts within the same family, the production cost and the delivery time are closely related to its configuration and corresponding scheduling for certain demand period. Although studies on multi-part flow-line configuration design are abundant, studies on concurrent optimization of configuration design and scheduling for reconfigurable manufacturing system are scarce. First, a generic mixed integer nonlinear programming model for concurrent configuration design and scheduling is established to relax the limitation of the existing model, and then a mixed integer linear programming model is derived. The decisions of the two generalized models are to decide the amount of stations, the amount of identical machines and machines’ configuration for every station, and assign parts to machines along the multi-part flow line together with sequencing assigned parts for each machine. Based on the mixed integer linear programming model, an exact ε-constraint method is developed to obtain the Pareto optimal solutions with tradeoffs between cost and tardiness. The validation of two models and the ε-constraint method is verified against two cases adapted from the literature.

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