Rapid Analysis for Subcontractor Resource Allocation

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1. Introduction

Specialist contractors – generally known as subcontractors – perform the majority of the work on commercial construction projects. It is common for a typical construction project to utilize dozens of subcontractors, with 80-90% of the work of most building projects in the U.S. being performed by subcontractors (Hinze and Tracey 1994). The performance of the subcontractor is critical to project success (Arditi and Chotibhongs 2005; Gray and Flanagan 1989). Commonly, subcontractors have to perform work on multiple projects simultaneously (Sacks 2004), but with limited resources, each subcontractor strives to maximum its workload at any given time for optimum resource utilization (Mathews et al. 2003; O'Brien and Fischer 2000). Thus, subcontractors work activities are subject to individual resource constraints (Kim and Paulson 2003).

During construction, changes frequently occur and conflict with subcontractor’s capacity constraints, causing immediate reallocation of resource (O'Brien and Fischer 2000). As a result, resource allocation due to frequent changes is a critical challenge for subcontractors. Because of frequent changes, subcontractors normally switch resources among projects, and they strive to understand the potential impacts after reallocating resources. Unfortunately, most existing tools are too complicated and provide limited support for resource reallocation across projects, especially for rapid what-if analysis scenarios for decision making. This leads to reliance on manual resource allocation methods. In response, this paper presents a tool for multi-project resource allocation. A test case with a variety of projects and task durations is presented to demonstrate the tool.

2. Literature Revi...

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...lulu, HI., United states, 593-599.

Sturts Dossick, C., and Schunk, T. K. (2007). "Subcontractor schedule control method." Journal of Construction Engineering and Management, 133(3), 262-265.

Tommelein, I. D., Riley, D. R., and Howell, G. A. (1999). "Parade Game: Impact of work flow variability on trade performance." Journal of Construction Engineering and Management, 125(5), 304-310.

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Zhang, H., and Tam, C. M. (2003). "Fuzzy decision-making for dynamic resource allocation." Construction Management and Economics, 21(1), 31-41.

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