Use of float consumption rate in resource leveling of construction projects

Abstract

The management of resources has been claimed to be as important as scheduling methods. Inefficiency in managing resources may bring about severe delays and cost overruns caused by resource shortages in some cases and/or idle resources in others. Therefore, resources should be utilized efficiently to prevent project failures. Resource leveling is one of the approaches that are used for the management of resources. It aims to minimize fluctuations, peaks, and valleys in resource utilization without changing the completion time of a project and the number of resources required. Although the main principle behind traditional resource leveling is achieving an even flow of resources while the original project duration remains unchanged, the literature supports the need to develop an efficient model that discriminates among the activities that are selected for participation in resource leveling. For this purpose, this study has developed a model that considers the float consumption rates of activities in resource leveling. The float consumption rate is the percentage that is set to determine the maximum amount of float which will be consumed to shift the start time of the activity. The proposed model allows a scheduler to assign float consumption rates to each activity that can be used during the resource leveling procedure. When the required information is inputted, the proposed model automatically changes the required daily resources as it shifts the noncritical activities along their available total float times. The proposed model is expected to minimize the likelihood of severe delays and cost overruns. The model is demonstrated by constructing a network and its resource utilization histograms.

Graphical abstract

Keywords

resource management / resource leveling / float consumption rate / scheduling

References

[1] Ahuja H N (1976). Construction Performance Control by Networks. New York: John Wiley and Sons

[2] Al-Gahtani K (2009). Float allocation using the total risk approach. Journal of Construction Engineering and Management, 135(2): 88–95

[3] Ammar M (2003). Float analysis of non-serial repetitive activities. Construction Management and Economics, 21(5): 535–542

[4] Anagnostopoulos K P, Koulinas G K (2010). A simulated annealing hyperheuristic for construction resource levelling. Construction Management and Economics, 28(2): 163–175

[5] Arditi D, Tokdemir O B, Suh K (2001). Effect of learning on line of balance scheduling. International Journal of Project Management, 19(5): 265–277

[6] Arditi D, Tokdemir O B, Suh K (2002). Challenges in line-of-balance scheduling. Journal of Construction Engineering and Management, 128(6): 545–556

[7] Benjaoran V, Tabyang W, Sooksil N (2015). Precedence relationship options for the resource levelling problem using a genetic algorithm. Construction Management and Economics, 33(9): 711–723

[8] Burgess A R, Killebrew J B (1962). Variation in activity level on a cyclical arrow diagram. Journal of Industrial Engineering, 13(2): 76–83

[9] Christodoulou S E, Ellinas G, Aslani P (2009). Entropy-based scheduling of resource-constrained construction projects. Automation in Construction, 18(7): 919–928

[10] Christodoulou S E, Ellinas G, Michaelidou-Kamenou A (2010). Minimum moment method for resource leveling using entropy maximization. Journal of Construction Engineering and Management, 136(5): 518–527

[11] Damci A, Arditi D, Polat G (2013a). Resource leveling in line-of-balance scheduling. Computer-Aided Civil and Infrastructure Engineering, 28(9): 679–692

[12] Damci A, Arditi D, Polat G (2013b). Multiresource leveling in line-of-balance scheduling. Journal of Construction Engineering and Management, 139(9): 1108–1116

[13] Damci A, Arditi D, Polat G (2016). Impacts of different objective functions on resource leveling in line-of-balance scheduling. KSCE Journal of Civil Engineering, 20(1): 58–67

[14] Damci A, Polat G (2014). Impacts of different objective functions on resource leveling in construction projects. Journal of Civil Engineering and Management, 20(4): 537–547

[15] Damci A, Polat G, Akin F D, Turkoglu H (2019). Resource levelling with float consumption rate. In: Proceedings of the Creative Construction Conference 2019. Budapest, 597–602

[16] de la Garza J, Prateapusanond A, Ambani N (2007). Preallocation of total float in the application of a critical path method based construction contract. Journal of Construction Engineering and Management, 133(11): 836–845

[17] de la Garza J, Vorster M, Parvin M (1991). Total float traded as commodity. Journal of Construction Engineering and Management, 117(4): 716–727

[18] Easa S M (1989). Resource leveling in construction by optimization. Journal of Construction Engineering and Management, 115(2): 302–316

[19] El-Sayegh S (2018). Resource levelling optimization model considering float loss impact. Engineering, Construction, and Architectural Management, 25(5): 639–653

[20] Gong D, Rowings Jr J E (1995). Calculation of safe float use in risk-analysis-oriented network scheduling. International Journal of Project Management, 13(3): 187–194

[21] Gould F E (2012). Managing the Construction Process: Estimating, Scheduling, and Project Control. 4th ed. Upper Saddle River, NJ: Prentice Hall

[22] Hajdu M (2015). Point-to-point versus traditional precedence relations for modeling activity overlapping. Procedia Engineering, 123: 208–215

[23] Hajdu M (2018). Survey of precedence relationships: Classification and algorithms. Automation in Construction, 95: 245–259

[24] Hariga M, El-Sayegh S M (2011). Cost optimization model for the multiresource leveling problem with allowed activity splitting. Journal of Construction Engineering and Management, 137(1): 56–64

[25] Harris R B (1978). Precedence and Arrow Networking Techniques for Construction. New York: John Wiley and Sons

[26] Harris R B (1990). Packing method for resource leveling (PACK). Journal of Construction Engineering and Management, 116(2): 331–350

[27] Hashemi Doulabi S H, Seifi A, Shariat S Y (2011). Efficient hybrid genetic algorithm for resource leveling via activity splitting. Journal of Construction Engineering and Management, 137(2): 137–146

[28] Hiyassat M A S (2000). Modification of minimum moment approach in resource leveling. Journal of Construction Engineering and Management, 126(4): 278–284

[29] Hiyassat M A S (2001). Applying modified minimum moment method to multiple resource leveling. Journal of Construction Engineering and Management, 127(3): 192–198

[30] Householder J L, Rutland H E (1990). Who owns the float? Journal of Construction Engineering and Management, 116(1): 130–133

[31] Kyriklidis C, Vassiliadis V, Kirytopoulos K, Dounias G (2014). Hybrid nature-inspired intelligence for the resource leveling problem. Operations Research, 14(3): 387–407

[32] Lu M, Li H (2003). Resource-activity critical-path method for construction planning. Journal of Construction Engineering and Management, 129(4): 412–420

[33] Naylor H F W (2012). Construction Project Management: Planning and Scheduling. New York: Delmar Pub

[34] Ponce de Leon G (1986). Float ownership: Specs treatment. Cost Engineering, 28(10): 12–15

[35] Ponz-Tienda J L, Yepes V, Pellicer E, Moreno-Flores J (2013). The resource leveling problem with multiple resources using an adaptive genetic algorithm. Automation in Construction, 29: 161–172

[36] Popescu C M (1976). How to use CPM in practice, Part II—Resources. Austin: University of Texas at Austin

[37] Prateapusanond A (2003). Study on a Comprehensive Practice of Total Float Pre-allocation and Management for the Application of a CPM-based Construction Contract. Dissertation for the Doctoral Degree. Blacksburg, VA: Virginia Polytechnic Institute

[38] Project Management Institute (2017). A Guide to the Project Management Body of Knowledge (PMBOK). Pennsylvania: Project Management Institute

[39] Qiao J, Li Y (2018). Resource leveling using normalized entropy and relative entropy. Automation in Construction, 87: 263–272

[40] Sakka Z, El-Sayegh S (2007). Float consumption impact on cost and schedule in the construction industry. Journal of Construction Engineering and Management, 133(2): 124–130

[41] Savin D, Alkass S, Fazio P (1996). Construction resource leveling using neural networks. Canadian Journal of Civil Engineering, 23(4): 917–925

[42] Senouci A B, Adeli H (2001). Resource scheduling using neural dynamics model of Adeli and Park. Journal of Construction Engineering and Management, 127(1): 28–34

[43] Son J, Skibniewski M J (1999). Multiheuristic approach for resource leveling problem in construction engineering: Hybrid approach. Journal of Construction Engineering and Management, 125(1): 23–31

[44] Wagner H M, Giglio R J, Glaser R G (1964). Preventive maintenance scheduling by mathematical programming. Management Science, 10(2): 316–334

[45] Wiest J D, Levy F K (1977). A Management Guide to PERT/CPM: With GERT/PDM/DCPM and Other Networks. 2nd ed. Upper Saddle River, NJ: Prentice-Hall

RIGHTS & PERMISSIONS

2020 Higher Education Press