Impact of condition based maintenance policies on the service level of multi-stage manufacturing systems

2018 ◽  
Vol 76 ◽  
pp. 65-78
Author(s):  
Alessio Angius ◽  
Marcello Colledani ◽  
Anteneh Yemane
Keyword(s):  
Manufacturing Systems ◽  
Service Level ◽  
Condition Based Maintenance ◽  
Multi Stage ◽  
Maintenance Policies

scholarly journals Impact of Preventive Maintenance on the Service Level of Multi-stage Manufacturing Systems with Degrading Machines

2016 ◽  
Vol 49 (12) ◽  
pp. 568-573 ◽  
Author(s):  
Alessio Angius ◽  
Marcello Colledani ◽  
Laura Silipo ◽  
Anteneh Yemane
Keyword(s):  
Preventive Maintenance ◽  
Manufacturing Systems ◽  
Service Level ◽  
Multi Stage

Real-Time Maintenance Policy in Manufacturing Systems With Intermediate Buffers

2015 ◽  
Author(s):  
Xi Gu ◽  
Xiaoning Jin ◽  
Jun Ni
Keyword(s):  
Real Time ◽  
Manufacturing Systems ◽  
Maintenance Policy ◽  
Discrete Time Markov Chain ◽  
Markov Decision ◽  
Maintenance Decision ◽  
Maintenance Policies ◽  
Time Buffer ◽  
Buffer Capacities ◽  
One Machine

Real-time maintenance decision making in large manufacturing system is complex because it requires the integration of different information, including the degradation states of machines, as well as inventories in the intermediate buffers. In this paper, by using a discrete time Markov chain (DTMC) model, we consider the real-time maintenance policies in manufacturing systems consisting of multiple machines and intermediate buffers. The optimal policy is investigated by using a Markov Decision Process (MDP) approach. This policy is compared with a baseline policy, where the maintenance decision on one machine only depends on its degradation state. The result shows how the structures of the policies are affected by the buffer capacities and real-time buffer levels.

A rolling horizon approach for production planning and condition-based maintenance under uncertain demand

2019 ◽  
Vol 233 (6) ◽  
pp. 1014-1028
Author(s):  
Lin Wang ◽  
Zhiqiang Lu ◽  
Yifei Ren
Keyword(s):  
Service Level ◽  
Condition Based Maintenance ◽  
Uncertain Demand ◽  
Programming Approach ◽  
Production Plan ◽  
Rolling Horizon ◽  
Decision Point ◽  
Total Cost ◽  
New Information ◽  
Maintenance Plan

In reality, the forecast of uncertainties often becomes more accurate with the approaching of the forecasted period. This article proposes a rolling horizon approach to dynamically determine the production plan and the maintenance plan for a degradation system under uncertain environment. In each rolling horizon, demand forecasts are updated with new information from customers, and the degradation level of system is confirmed by inspection. By taking advantage of the updated uncertainties, at each decision point, the maintenance plan is determined by an advance-postpone balancing approach and the production plan is optimized by a heuristic algorithm in a two-stage stochastic model. Numerical results validate that the rolling horizon approach has great superiority over traditional stochastic programming approach in terms of real total cost and service level.

Maintenance Decision Model for a Two-Machine Production Line With Multistage Degradation Machines

2018 ◽  
Author(s):  
Yunyi Kang ◽  
Feng Ju
Keyword(s):  
Decision Model ◽  
Production Systems ◽  
Maintenance Policy ◽  
Multi Stage ◽  
Markov Decision ◽  
Optimal Maintenance ◽  
Markov Decision Model ◽  
Maintenance Decision ◽  
Maintenance Policies ◽  
Optimal Maintenance Policy

In this work, we develop preventative maintenance policies on two-machine-and-one-buffer production systems with machines subject to multi-stage degradation. Condition-based maintenance policies are generated for both machines, with consideration on both the machine degradation stages and the buffer level. Moreover, the policies are flexible, allowing a machine to be recovered to any better operating state, while merely recovering to the best operating state is possible in many previous work. A Markov decision model is formulated to find the optimal maintenance policy and computational experiments show that the policies improve the performance of a system in finite production runs.

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