Published May 2012 | Version v1
Journal article

Preventive maintenance optimization for a multi-component system under changing job shop schedule

  • 1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240 (China)

Description

Variability and small lot size is a common feature for many discrete manufacturing processes designed to meet a wide array of customer needs. Because of this, job shop schedule often has to be continuously updated in reaction to changes in production plan. Generally, the aim of preventive maintenance is to ensure production effectiveness and therefore the preventive maintenance models must have the ability to be adaptive to changes in job shop schedule. In this paper, a dynamic opportunistic preventive maintenance model is developed for a multi-component system with considering changes in job shop schedule. Whenever a job is completed, preventive maintenance opportunities arise for all the components in the system. An optimal maintenance practice is dynamically determined by maximizing the short-term cumulative opportunistic maintenance cost savings for the system. The numerical example shows that the scheme obtained by the proposed model can effectively address the preventive maintenance scheduling problem caused by the changes in job shop schedule and is more efficient than the ones based on two other commonly used preventive maintenance models.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.ress.2012.01.005

Additional details

Identifiers

DOI
10.1016/j.ress.2012.01.005;
PII
S0951-8320(12)00007-5;

Publishing Information

Journal Title
Reliability Engineering and System Safety
Journal Volume
101
Journal Page Range
p. 14-20
ISSN
0951-8320
CODEN
RESSEP

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
43093239
Subject category
S42: ENGINEERING;
Descriptors DEI
COST; MAINTENANCE; MANUFACTURING; OPTIMIZATION; SCHEDULES

Optional Information

Copyright
Copyright (c) 2012 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.