### Abstract

The dynamic nature of an operating environment, such as machine utilization and breakdown frequency results in changing preventive maintenance (PM) needs for manufacturing equipment. In this paper, we present an approach to generate an adaptive PM schedule which maximizes the net savings from PM subject to workforce constraints. The approach consists of two components: (a) task prioritization based on a multi-logit regression model for each type of PM task, and (b) task rescheduling based on a binary integer programming (BIP) model with constraints on single-skilled and multi-skilled workforce availability. The task priontization component develops a multi-logit regression for machine failure probability associated with each type of PM task at the beginning of the year, using historical data on machine utilization, PM, and machine breakdowns. At the start of each PM time-bucket (e.g., a month), we use the updated machine failure probability for each candidate PM task to compute its current contribution to net PM savings, which indicates its current priority. The task rescheduling BIP model incorporates the priorities in selecting tasks for the current bucket to maximize PM effectiveness subject to workforce availability, yielding an adaptive and effective PM schedule for each time-bucket of the master PM schedule. We examine the effect of using multi-skilled workforce on the overall PM effectiveness, and also provide an illustration from a newspaper publishing environment to explain the use of the approach. We have developed four heuristic algorithms to yield good solutions to large scale versions of this scheduling problem. The heuristics perform extremely well, and the best heuristic solution is within 1.4% of optimality on an average.

Original language | English (US) |
---|---|

Pages (from-to) | 827-840 |

Number of pages | 14 |

Journal | Management Science |

Volume | 43 |

Issue number | 6 |

State | Published - Jun 1997 |

Externally published | Yes |

### Fingerprint

### Keywords

- Binary Integer Programming
- Multi-logit Regression
- Preventive Maintenance

### ASJC Scopus subject areas

- Management of Technology and Innovation
- Strategy and Management
- Management Science and Operations Research

### Cite this

*Management Science*,

*43*(6), 827-840.

**Maximizing the effectiveness of a preventive maintenance system : An adaptive modeling approach.** / Gopalakrishnan, Mohan; Ahire, Sanjay L.; Miller, David M.

Research output: Contribution to journal › Article

*Management Science*, vol. 43, no. 6, pp. 827-840.

}

TY - JOUR

T1 - Maximizing the effectiveness of a preventive maintenance system

T2 - An adaptive modeling approach

AU - Gopalakrishnan, Mohan

AU - Ahire, Sanjay L.

AU - Miller, David M.

PY - 1997/6

Y1 - 1997/6

N2 - The dynamic nature of an operating environment, such as machine utilization and breakdown frequency results in changing preventive maintenance (PM) needs for manufacturing equipment. In this paper, we present an approach to generate an adaptive PM schedule which maximizes the net savings from PM subject to workforce constraints. The approach consists of two components: (a) task prioritization based on a multi-logit regression model for each type of PM task, and (b) task rescheduling based on a binary integer programming (BIP) model with constraints on single-skilled and multi-skilled workforce availability. The task priontization component develops a multi-logit regression for machine failure probability associated with each type of PM task at the beginning of the year, using historical data on machine utilization, PM, and machine breakdowns. At the start of each PM time-bucket (e.g., a month), we use the updated machine failure probability for each candidate PM task to compute its current contribution to net PM savings, which indicates its current priority. The task rescheduling BIP model incorporates the priorities in selecting tasks for the current bucket to maximize PM effectiveness subject to workforce availability, yielding an adaptive and effective PM schedule for each time-bucket of the master PM schedule. We examine the effect of using multi-skilled workforce on the overall PM effectiveness, and also provide an illustration from a newspaper publishing environment to explain the use of the approach. We have developed four heuristic algorithms to yield good solutions to large scale versions of this scheduling problem. The heuristics perform extremely well, and the best heuristic solution is within 1.4% of optimality on an average.

AB - The dynamic nature of an operating environment, such as machine utilization and breakdown frequency results in changing preventive maintenance (PM) needs for manufacturing equipment. In this paper, we present an approach to generate an adaptive PM schedule which maximizes the net savings from PM subject to workforce constraints. The approach consists of two components: (a) task prioritization based on a multi-logit regression model for each type of PM task, and (b) task rescheduling based on a binary integer programming (BIP) model with constraints on single-skilled and multi-skilled workforce availability. The task priontization component develops a multi-logit regression for machine failure probability associated with each type of PM task at the beginning of the year, using historical data on machine utilization, PM, and machine breakdowns. At the start of each PM time-bucket (e.g., a month), we use the updated machine failure probability for each candidate PM task to compute its current contribution to net PM savings, which indicates its current priority. The task rescheduling BIP model incorporates the priorities in selecting tasks for the current bucket to maximize PM effectiveness subject to workforce availability, yielding an adaptive and effective PM schedule for each time-bucket of the master PM schedule. We examine the effect of using multi-skilled workforce on the overall PM effectiveness, and also provide an illustration from a newspaper publishing environment to explain the use of the approach. We have developed four heuristic algorithms to yield good solutions to large scale versions of this scheduling problem. The heuristics perform extremely well, and the best heuristic solution is within 1.4% of optimality on an average.

KW - Binary Integer Programming

KW - Multi-logit Regression

KW - Preventive Maintenance

UR - http://www.scopus.com/inward/record.url?scp=0031152504&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031152504&partnerID=8YFLogxK

M3 - Article

VL - 43

SP - 827

EP - 840

JO - Management Science

JF - Management Science

SN - 0025-1909

IS - 6

ER -