Laura Florez

Sustainable Workforce Scheduling in Construction Program Management

The multimode resource-constrained project scheduling problem (MRCPSP) deals with the scheduling of a set of projects with alternative requirements of renewable and non-renewable resources. Solutions to the MRCPSP usually consider objectives in terms of cost and time. However, social objectives related with the workforce may impact the performance of projects and affect program sustainability goals. To account for this new social input, this paper extends the MRCPSP and proposes a new multiobjective mixed-integer programming model. The proposed solution method uses an a priori lexicographic ordering of the objectives, followed by an ɛ-constraints approach. The model is illustrated with a case study of a construction program.

Optimization model for the selection of materials using the leed green building rating system

The green building philosophy has recently taken a central role in the construction industry, mitigating the environmental impact of buildings. Buildings are responsible of a large portion of harmful emissions and use a large fraction of the total natural resources. Moreover, the bad quality of indoor environments in office buildings can affect the health of employees, reducing their productivity. Because materials used in construction projects are responsible for a significant fraction of this environmental footprint, a better selection and use of materials could lead to a green certification such as the one given by the Leadership in Energy and Environmental Design (LEED) rating system. This paper illustrates how an optimization model can be used to help decision makers to select the right materials, incorporating design and budget constraints while maximizing the number of material-related LEED-based credits. A case study based on real data is analyzed from different angles to demonstrate the effect on the optimal choice of materials due to changes in construction market conditions.

Maximizing Labor Stability as a Sustainability Performance Indicator in Project Scheduling

Project scheduling is an important task in program management. The selection of resources and the harmonization of their work make program scheduling crucial for the success of a construction program for both the owner and contractor. To achieve long term economic, social, and environmental benefits, program managers need tools that can help them assess their program’s sustainability performance. Indicators are recognized as useful tools to measure performance across a range of sustainable principles. The multimode resource-constrained project scheduling problem (MRCPSP) deals with the scheduling of a set of projects with alternative requirements of resources to execute them. Minimization of project duration is usually considered as the objective; however, social objectives may impact the performance of projects and affect program sustainability goals. To account for this new social input, this paper adapts a mixed integer programming (MIP) model that solves the MRCPSP problem. Aside from the social objective, the model accounts for renewable resources and non-renewable required by projects during their life span. A literature search is undertaken to determine an indicator to assess performance with the objective of maximizing social sustainability of construction projects. The proposed model can be a valuable tool to assist in an industry that is moving towards sustainable practices.

Crew Allocation System for the Masonry Industry

Masonry construction is labor-intensive. Processes require a large number of crews made up of masons with diverse skills, capabilities, and personalities. Often crews are reassembled and the superintendent in the site is responsible for allocating crews to balance between the complexity of the job and the need for quality and high production rates. However, the masonry industry still faces increased time and low productivity rates that result from inefficiencies in crew allocation. This article presents a system for efficient crew allocation in the masonry industry formulated as a mixed-integer program. The system takes into consideration characteristics of masons and site conditions and how to relate these to determine the right crew for the right wall to increase productivity. With the system, superintendents are not only able to identify working patterns for each of the masons but also optimal crew formation, completion times, and labor costs. To validate the model, data from a real project in the United States is used to compare the crew allocation completed by the superintendent onsite with the one proposed by the system. The results showed that relating characteristics of workers with site conditions had a substantial impact on reducing the completion time to build the walls, maximizing the utilization of masons, and outlining opportunities for concurrent work.

Optimal Crew Design for Masonry Construction Projects Considering Contractor's Requirements and Workers' Needs

Masonry construction is labor-intensive. Its operations involve little to no mechanization and require a large number of crews made up of workers with diverse skills. Relationships between crews are tight and very dependent. Often tasks have to be completed concurrently and crews have to share resources and work space to complete their work. One of the problems masonry contractors face is the need to design crews, that is, determine the number of crews and the composition of each crew to be effectively used in the construction process to maximize workflow. This study proposes a mixed integer optimization model to assist contractors in the allocation of crews in masonry projects. To address realistic scenarios experienced by the contractors on masonry construction job sites, the model incorporates the rules that contractors use for crew design and makeup as well as time constraints. In addition, the model considers workers’ needs such as labor stability. The proposed model can be a valuable tool to assist masonry stakeholders in the process of allocating crews while meeting contractor’s requirements and workers’ needs.

Probability Density Function for Predicting Productivity in Masonry Construction Based on the Compatibility of a Crew

During the different phases of a masonry project, contractors collect detailed information about the labor productivity of its workers and the factors that influence productivity. Information includes quantitative data such as hours, activities, and tasks, and qualitative data such as ratings and personality factors. Personality factors have been found to be a key aspect that influences the compatibility of a crew and the productivity in masonry construction. This paper proposes a mathematical framework to determine how the compatibility between the workers in a crew can be used to predict productivity. A standard method for quantifying personality is used to determine the compatibility of a crew and empirically define a probability density to predict productivity. The probability density determines, for a given compatibility, the average productivity for a crew. The most interesting part of this probability density is that it accounts for variations in the productivity, resulting from the interaction and the relationships between the workers in a crew. The proposed probability distribution can be used to make more realistic predictions, by calculating confidence intervals, of the productivity of masonry crews and to better estimate times of construction, avoid crew conflicts, and find practical ways to increase production.

Feasibility of Implementing a Computer-Assisted Pavement Rehabilitation Decision Support System

Rehabilitation of urban highways is a critical issue confronting Department of Transportation agencies since most states have an inventory of highways that have exceeded their design lives. Delivery of cost-effective projects which minimize delays and traffic impacts of highway rehabilitation is needed. The Construction Analysis for Pavement Rehabilitation Strategies (CA4PRS) is a software program for predicting construction productivity during various closure timeframes for highway rehabilitation. This tool has been used in California and Washington among other states and has proved capable of providing significant scheduling and productivity inputs into early design planning. This study presents the results of evaluating the implementation and use of the CA4PRS by the Georgia Department of Transportation (GDOT) and the Oklahoma Department of Transportation (ODOT) in planning their highway projects. A knowledge inventory survey conducted at GDOT and ODOT is used to assess if the tool could be used in the context of Georgia and Oklahoma. Furthermore, the applicability of CA4PRS is tested in two case studies in an effort to evaluate its potential implementation.

Evaluation of Data Requirements for Computerized Constructability Analysis of Pavement Rehabilitation Projects

With the impending end of the serviceable life of the National Highway System, many transportation agencies have increased their focus on preservation, rehabilitation, and maintenance projects. The Construction Analysis for Pavement Rehabilitation Strategies (CA4PRS) software has been a useful decision making tool for various Departments of Transportation (DOTs) in the United States. CA4PRS is a schedule and traffic analysis tool that helps planners and designers select effective and economical rehabilitation strategies. The Georgia Department of Transportation (GDOT) has investigated the applicability of CA4PRS for its concrete pavement rehabilitation projects. Results showed that two main issues for the use of CA4PRS in the state of Georgia were (1) the need for modifications to GDOT’s operating procedures and (2) lack of data required for performing the CA4PRS analysis. This paper presents the initial steps in the evaluation of the data required for CA4PRS analysis of pavement rehabilitation projects in the state of Georgia. A comprehensive analysis of data collection practices was performed to determine required changes needed to satisfy CA4PRS software data requirements. RESEARCH BACKGROUND