LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Alzubaidi, HJ
Languages: English
Types: Doctoral thesis
Subjects: R1, TH, TA, health_and_wellbeing, built_and_human_env
By its nature, building maintenance requires an ability to respond to a demand that is random in time, random in nature and random in location. This in turn creates complex operational and logistical problems for management, specially if the property complex is large.\ud The objective of this research is to assess the scope for and effectiveness of quantitative modelling, and the prediction of the outcome of alternative management action (policy), to assist in the management of building maintenance complexes of the size of a hospital. Both building and engineering equipments are encompassed within the study as appropriate. The research issues are split into three related phases; a demand study; a defect reduction study; and a maintenance management model. \ud 1- The maintenance demand study: \ud Based upon general statistics obtained, attempts have been made to identify and quantify both the major problems areas (in terms of cost and frequency of maintenance activities), and the nature and cause of the demand for maintenance. They have revealed no coherent picture in that the demand from wards and buildings seems independent of the patient throughput and\ud the age of buildings. The demand for maintenance, for the main trades involved, has been estimated and used in the simulation models mentioned in below. \ud 2- Demand reduction model: \ud Accepting the current demand situation for maintenance, it was proposed to identify what is the cause of the demand and what possible actions could reduce the demand: Possibly through design modification, changes in materials used, change in practice of service/building user, development of Preventive Maintenance 'PM' or inspection system for component. Despite considerable effort, it proved not possible to progress this aspect of the study and the reasons are discussed. \ud 3- Maintenance management models: \ud Simulation models to the maintenance activities within the hospital has been developed using, Extended Control and Simulation Language, ECSLPLUS, to model the maintenance policies, and assessing any changes in operating procedures. The advantage of modelling is that the magnitude and nature of changes can be assessed and contemplated prior to any actual change in operating procedures. This is generally recognised as being most valuable. \ud For specific problems and areas of operation identified, development of specific methods of deployments have been attempted. For instance, 'recieving one job at a time'; 'recieving a batch of jobs at a time'; and 'delaying non-urgent jobs and grouping them in time'. A number of maintenance management policies have been assessed using the above models, these are: 'Previewing' and 'not previewing' most of the defects before repair to identify the required resources; 'employing extra part-time\ud tradesmen during the busy days'; 'working 7 days instead of 5 days a week'; 'no sickness policy'; and 'employing multi-skilled tradesmen option'. \ud These models should be capable of indicating to management the gains and consequances, in terms of measures of interest to them such as the workforce and manhours required to meet the demand for maintenance per trade, changing operating practice, customs and timescales. That is, their decision variables.
  • No references.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article