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East, E W and Liu, L Y (2006) Multiproject Planning and Resource Controls for Facility Management. Journal of Construction Engineering and Management, 132(12), 1294–305.

  • Type: Journal Article
  • Keywords: Project management; Facilities; Resource management; Construction industry;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2006)132:12(1294)
  • Abstract:
    Facility managers face the challenges of managing many different types of small, geographically dispersed construction projects. Depending on the complexity and distribution of projects, the time required to prepare for production consumes a large percentage of the total time required to complete the job. Increasing crews’ productive hours is a key objective when planning multiproject schedules. Existing methods, however, lack the effective means to explicitly model, analyze, and optimize resource utilization for these multiple concurrent projects. As a result, few facility managers fully exploit the potential to better manage their often limited budget and resources. This paper presents an explicit model of the mobilization requirements of multiple crews performing a variety of different activities over a geographic space. The model allows the facility manager to explicitly investigate the impact of crew composition, crew specialization, and depot locations. Using work rule decisions regarding alternative crew allocations, facility managers may dynamically allocate resources to optimize resources and to complete projects in a minimum amount of time. To verify and validate this new model, a computerized system, called FIRS (Facility/Infrastructure Resource Scheduler), was created to analyze the multiproject resource plans with data from two military organizations and a university campus. FIRS utilizes a new genetic algorithm that was developed specifically to work with multiproject scheduling. Using FIRS, facility managers can develop and test alternative crew allocations based on the qualifications of the crews available and the type of operation being performed.

Elazouni, A M (2006) Classifying Construction Contractors Using Unsupervised-Learning Neural Networks. Journal of Construction Engineering and Management, 132(12), 1242–53.

Elmisalami, T, Walters, R and Jaselskis, E J (2006) Construction IT Decision Making Using Multiattribute Utility Theory for Use in a Laboratory Information Management System. Journal of Construction Engineering and Management, 132(12), 1275–83.

Menches, C L and Hanna, A S (2006) Conceptual Planning Process for Electrical Construction. Journal of Construction Engineering and Management, 132(12), 1306–13.

Menches, C L and Hanna, A S (2006) Quantitative Measurement of Successful Performance from the Project Manager’s Perspective. Journal of Construction Engineering and Management, 132(12), 1284–93.

Moussa, M, Ruwanpura, J and Jergeas, G (2006) Decision Tree Modeling Using Integrated Multilevel Stochastic Networks. Journal of Construction Engineering and Management, 132(12), 1254–66.

Sharma, V, Al-Hussein, M and AbouRizk, S M (2006) Residential Construction Lot Grading Approval Process Optimization: Case Study of City of Edmonton. Journal of Construction Engineering and Management, 132(12), 1225–33.

Song, Y and Chua, D K H (2006) Modeling of Functional Construction Requirements for Constructability Analysis. Journal of Construction Engineering and Management, 132(12), 1314–26.

Su, Y Y, Hashash, Y M A and Liu, L Y (2006) Integration of Construction As-Built Data Via Laser Scanning with Geotechnical Monitoring of Urban Excavation. Journal of Construction Engineering and Management, 132(12), 1234–41.

Zhang, H, Tam, C M, Li, H and Shi, J J (2006) Particle Swarm Optimization-Supported Simulation for Construction Operations. Journal of Construction Engineering and Management, 132(12), 1267–74.