Simulation-based Methods for Studying Reliability and Preventive Maintenance of Public Infrastructure

This chapter initially presents a brief information about production systems. At these systems, different types of maintenance policies are developed to cope with wear out failures. Mainly used maintenance policies can be classified as corrective, preventive, and condition-based maintenance. In the corrective maintenance, repair or replacement is applied whenever components of the machine breakdown. In the preventive maintenance approach maintenance activities are applied to the critical components on a periodic basis. On the other hand, maintenance activities are applied whenever critical reliability level is reached or exceeded. These types of maintenance policies are modeled using mathematical modeling techniques such as linear programming, goal programming, dynamic programming, and simulation. A review of current literature about the mathematical models, the simulation-based optimization studies examining these maintenance policies are categorized and explained. Besides, the solution methodologies are discussed. Finally, the opportunities for future research are presented.

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Joint Optimization of Preventive Maintenance, Spare Parts Inventory and Transportation Options for Systems of Geographically Distributed Assets

Machines ◽

10.3390/machines6040055 ◽

2018 ◽

Vol 6 (4) ◽

pp. 55 ◽

Cited By ~ 1

Author(s):

Keren Wang ◽

Dragan Djurdjanovic

Keyword(s):

Decision Making ◽

Preventive Maintenance ◽

Spare Part ◽

Spare Parts ◽

Joint Optimization ◽

Joint Decision ◽

Geographically Dispersed ◽

Simulation Based ◽

The Right ◽

Maintenance Schedules

Maintenance scheduling for geographically dispersed assets intricately and closely depends on the availability of maintenance resources. The need to have the right spare parts at the right place and at the right time inevitably calls for joint optimization of maintenance schedules and logistics of maintenance resources. The joint decision-making problem becomes particularly challenging if one considers multiple options for preventive maintenance operations and multiple delivery methods for the necessary spare parts. In this paper, we propose an integrated decision-making policy that jointly considers scheduling of preventive maintenance for geographically dispersed multi-part assets, managing inventories for spare parts being stocked in maintenance facilities, and choosing the proper delivery options for the spare part inventory flows. A discrete-event, simulation-based meta-heuristic was used to optimize the expected operating costs, which reward the availability of assets and penalizes the consumption of maintenance/logistic resources. The benefits of joint decision-making and the incorporation of multiple options for maintenance and logistic operations into the decision-making framework are illustrated through a series of simulations. Additionally, sensitivity studies were conducted through a design-of-experiment (DOE)-based analysis of simulation results. In summary, considerations of concurrent optimization of maintenance schedules and spare part logistic operations in an environment in which multiple maintenance and transpiration options are available are a major contribution of this paper. This large optimization problem was solved through a novel simulation-based meta-heuristic optimization, and the benefits of such a joint optimization are studied via a unique and novel DOE-based sensitivity analysis.

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