Masonry construction is labor-intensive. Processes involve little to no mechanization and require a large number of crews made up of workers with diverse skills, capabilities, and personalities. Relationships among crews are tight and very dependent. Often crews are re-assembled and the superintendent is responsible for assigning workers to crews and allocating crews to different tasks to maximize workflow. This dynamic environment can influence the motivation of workers and impose pressure and stress on them. Workers, unlike other resources, have their own needs and requirements beyond the financial compensation for their work. Workers place a great value on requirements such as certainty about work assignments, matching assignments to career development goals, and work satisfaction. If managed properly, workers may bring considerable benefits to both the project and the contractor. A project that links workers to career goals not only allows contractors to develop more qualified staff for its future projects, but also gives the worker opportunities for career growth and development. Additionally, job satisfaction and efficiency increases from suitable worker assignment and consideration of tasks. Therefore, the study of sustainable labor management practices is of interest in masonry construction and other labor-intensive industries. A mixed-integer programming (MIP) model enables the integration of workers needs and contractor requirements into the process of labor allocation. Furthermore, the model can be used to quantify strategies that maximize productivity, quality of work, and the well-being of workers. Developing such a model is a necessary task. To plan and manage masonry construction, the contractor has to take into account not only multiple workers with different characteristics but also rules for crew design and makeup and project requirements in terms of personnel needs. Providing an analytical description of all the needs and requirements is challenging. Therefore, to determine labor management practices that indeed maximize production and maximize workers satisfaction, the model needs to realistically represent the realities in masonry construction sites and staffing practices, while remaining computationally manageable such that optimization models can be derived. This dissertation proposes a decision support system (DSS) for sustainable labor management in masonry construction that takes into consideration information on workers and job characteristics with the intention of assisting decision makers in allocating crews. Firstly, semi-structured interviews were conducted with masonry practitioners to gather perspectives on labor requirements, rules for crew design, and drivers for crew makeup. Secondly, a model that incorporates realities was implemented. The model supports masonry contractors and superintendent in the challenging process of managing crews, that is, to determine the composition of each crew and the allocation of crews to maximize productivity and workflow while considering workers’ preferences and well-being. With the DSS, project managers and superintendents are not only able to identify working patterns for each of the workers but also optimal crew formation and investment and labor costs. Data from real case study is used to compare the schedule and allocation on the site with the one proposed by the model. The comparison shows the model can optimize the allocation of crews to reduce the completion time to build the walls while maximizing the utilization of masons and outlining opportunities for concurrent work. It is expected that the DSS will help contractors improve productivity and quality while efficiently managing masonry workers in a more sustainable way. The contributions for the masonry industry are two-fold. Firstly, the proposed model considers a set of rules that masonry practitioners typically use to design crews of masons and analytically captures the realities of masonry construction jobsites when managing labor. Secondly, it attempts to quantify and mathematically model the practices that contractors use for crew makeup and evaluate labor management allocation both in terms of contractor requirements and worker needs. Literature review indicates that the existing models for labor allocation have not taken into consideration masonry site realities. An optimization framework, which combines masonry site realities from the semi-structured interviews is proposed. The framework results in a MIP model that is used to solve a crew scheduling and allocation problem. The model is formulated to determine which masons are in a crew and to assign crews to the different walls in a project. Additionally, it is used to evaluate crew design strategies that maximize productivity.

The focus of this thesis is on the precast concrete products manufacturing industry, which as one of the labour-intensive industries requires a substantial number of highly skilled operators in terms of crews to produce the final product. A crew is a group of multi-skilled chargehands and operators that have various skills and experience necessary to conduct an activity in a professional way. The high cost of skilled operators and the apparent inefficiencies of utilising such skilled operators in the industry are the major driving force. To achieve this, optimal crew allocation is required. Crew allocation is complex because of the multi-criteria nature of the problem and availability of thousands of possibilities and allocation alternatives. There is a gap in previous research efforts associated with crew allocation planning in the precast industry. Current practices suggest that the crew allocation process is carried out intuitively and the allocation of crews to production processes is subjective. This has led to high process-waiting times, improper allocation of skilled operators and ultimately higher production costs. In this context, the aim of this research is to propose an effective crew allocation methodology and a computer-based intelligent simulation model for its implementation. The objective of the approach is to guarantee a better workflow through minimising process-waiting time, optimising operator utilisation, and subsequently reducing the allocation cost. This research develops a holistic and integrated methodology for modelling crew allocation problems by reviewing state-of-art resource allocation techniques, structured interviews with production managers, site visits and a detailed case study. The methodology is developed using an IDEF0 process model and a generic process map for both the business and the production processes of the precast manufacturing system. A multi-layered genetic algorithm model is developed in conjunction with a process-simulation model to form a hybrid allocation system dubbed ‘SIM_Crew’. The model incorporates databases (Excel and MS Access), a simulation model (developed using Arena 12.0) and genetic algorithms (developed using Visual Basic for Applications) to facilitate the generation and evaluation of various “what-if” crew allocation scenarios. A number of performance criteria have been developed to evaluate the allocation plans. ‘SIM_Crew’ enables the investigation and analysis of allocating possible schedules and provides a facility to visualise the production processes. ‘SIM_Crew’ was validated using real life case study data and it was concluded that the allocation of crews to precast processes using genetic algorithm improves the throughput time and reduces the allocation cost as compared with real life production data. It is anticipated that future use of this research will solve the crew allocation problem in the precast industry.