Joint Production and Maintenance Decision-Making in Mixed-Model Assembly Systems

2017 ◽  
Author(s):  
Xi Gu ◽  
Weihong Guo
Keyword(s):  
Decision Making ◽  
Mixed Model ◽  
Joint Production ◽  
Assembly Systems ◽  
Markov Decision ◽  
Operational Decision Making ◽  
Maintenance Decision ◽  
Maintenance Policies ◽  
Mixed Model Assembly ◽  
Mass Personalization

Mixed-model assembly systems (MMASs) have been well recognized for their ability to handle product variants, and thus are particularly useful to meet the requirement brought by mass personalization. However, operational decision-making in MMASs is challenging due to the system complexity. Production selection and maintenance are two important operational decisions. In this paper, we investigate the joint production and maintenance policies in MMASs that consist of both common and variant operation stations. Markov Decision Process (MDP) is used to formulate the problem and numerical examples are presented to illustrate the structure of the policy in an MMAS that produces two types of product variants.

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.

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.

scholarly journals Framework for Evaluating Cognitive Support in Mixed Model Assembly Systems

2015 ◽  
Vol 48 (3) ◽  
pp. 924-929 ◽  
Author(s):  
A. Claeys ◽  
S. Hoedt ◽  
N. Soete ◽  
H. Van Landeghem ◽  
J. Cottyn
Keyword(s):  
Mixed Model ◽  
Assembly Systems ◽  
Cognitive Support ◽  
Mixed Model Assembly

Allocation of maintenance resources in mixed model assembly systems

2013 ◽  
Vol 32 (3) ◽  
pp. 473-479 ◽  
Author(s):  
Weihong Guo ◽  
Jionghua (Judy) Jin ◽  
S. Jack Hu
Keyword(s):  
Mixed Model ◽  
Assembly Systems ◽  
Mixed Model Assembly

Workforce Planning in Mixed Model Assembly Systems

1997 ◽  
Vol 45 (4) ◽  
pp. 553-567 ◽  
Author(s):  
Chung-Yee Lee ◽  
George L. Vairaktarakis
Keyword(s):  
Mixed Model ◽  
Workforce Planning ◽  
Assembly Systems ◽  
Mixed Model Assembly
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