Optimized scheduling for repetitive construction projects

An object-oriented model is presented for optimized scheduling of repetitive construction projects such as: high-rise buildings, housing projects, highways, pipeline networks, bridges and tunnels. The model provides a number of practical features, and incorporates newly developed algorithms for scheduling of repetitive construction projects including: (1) a resource-driven scheduling algorithm for repetitive activities; (2) an interruption algorithm; and (3) an optimization procedure. The scheduling algorithm identifies the scheduled start and finish times as well as the assigned crew for each unit of a repetitive activity. The algorithm provides a schedule that complies with precedence relationships, crew availability and crew work continuity constraints, and considers the impact of a number of practical factors commonly encountered during scheduling. The interruption algorithm generates feasible interruption vectors for each repetitive activity in the project and provides added advantage over available formulations that consider arbitrary user-specified interruption vectors. The optimization procedure is based on a dynamic programming formulation. Unlike available dynamic programming formulations, the present formulation is capable of incorporating cost in the optimization process, thus offering valuable support to project team members in minimizing the overall cost of the project. For each repetitive activity in the project, the present model assists the planner in selecting the optimum crew formation and interruption vector from a set of possible alternatives. As such, the model can be used to evaluate the impact of different project acceleration strategies (i.e. multiple crews, increased crew size, overtime policies, or additional shifts) on the overall cost. The present model is implemented as a prototype software system. The system is developed as a 32-bit windows application that supports user-friendly interface including menus, dialog boxes, and windows. A number of application examples are analyzed to illustrate the use of the model and demonstrate its capabilities. The model can be used as a decision support system for generating optimized schedules for repetitive construction projects. This should contribute to cost-effective delivery of constructed projects

OPTIMIZING THE SELECTION OF SUSTAINABILITY MEASURES TO MINIMIZE LIFE-CYCLE COST OF EXISTING BUILDINGS

Buildings have significant impacts on the environment and economy as they were reported by the World Business Council for Sustainable Development in 2009 to account for 40% of the global energy consumption. Building owners are increasingly seeking to integrate sustainability and green measures in their buildings to minimize energy and water consumption as well as life-cycle cost. Due to the large number of feasible combinations of sustainability measures, decision makers are often faced with a challenging task that requires them to identify an optimal set of upgrade measures to minimize the building life-cycle cost. This paper presents a model for optimizing the selection of building upgrade measures to minimize the life-cycle cost of existing buildings while complying with owner-specified requirements for building operational performance and budget constraints. The optimization model accounts for initial upgrade cost, operational cost and saving, escalation in utility costs, maintenance cost, replacement cost, and salvage value of building fixtures and equipment, and renewable energy systems. A case study of a rest area building in the state of Illinois in the United States was analyzed to illustrate the unique capabilities of the developed optimization model. The main findings of this analysis illustrate the capabilities of the model in identifying optimal building upgrade measures to achieve the highest savings of building life-cycle cost within a user-specified upgrade budget; and generating practical and detailed recommendations on replacing building fixtures and equipment and installing renewable energy systems.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Minimizing greenhouse gas emissions and water consumption of existing buildings

Buildings are responsible for 38% of all carbon emissions and 14% of water consumption in the United States. These negative environmental impacts can significantly be reduced by implementing green upgrade measures such as energy-efficient lighting and HVAC systems, motion sensors, photovoltaic systems, and water-saving plumbing fixtures. Building owners in the public and private sectors often search for an optimal set of upgrade measures that is capable of minimizing the negative environmental impacts of their buildings. This paper presents the development of an optimization model that is capable of identifying optimal selection of building upgrade measures to minimize greenhouse gas emission and water consumption of existing buildings while complying with limited upgrade budgets. The model is developed in four main development steps: metrics identification step that quantifies greenhouse gas emissions and water consumption of existing buildings; model formulation step that formulates the model decision variables, objective function, and constraints; implementation step that executes the model computations and specifies the model input and output data; and validation step that evaluates the model performance using a case study of an existing building. The results of the model illustrate its new and unique capabilities in providing detailed results, which include specifications for the recommended upgrade measures, their location in the building, and required upgrade cost to minimize greenhouse gas emissions and water consumption of existing buildings.Non UBCUnreviewedFacultyOthe

Measurement of pavement surface reflectance for a balloon lighting system

Nighttime construction offers many advantages to the public and to state agencies. Under these conditions, traffic is minimal and construction operations can be conducted effectively and quickly. In addition, cooler temperatures are favorable for the equipment and the material being installed. Despite these advantages, controlling glare is a critical and important issue in adequately lighting highway work zones. One of the controlling parameters in glare calculation is the pavement luminance, which is a quantitative measure of the surface brightness. Pavement luminance is based on predefined parameters known as r values provided by the Illuminating Engineering Society of North America (IESNA) for four standard pavement surfaces in the r tables. With the recent introduction of balloon lighting system for construction applications, the objective of this study was to measure pavement reflectance characteristics for this system in the laboratory and to compare the results to the standard r tables. For this purpose, a laboratory experimental setup was developed. On average, measured r values were 20% greater than the IESNA standard values for concrete surfaces (R1), 84% greater for R2 standard surfaces, and 95% greater for R3 standard surfaces. An analysis of variance statistical analysis indicated that the differences between the measured r values and standard r tables were not significant for concrete surfaces but were statistically significant for asphalt road surfaces. This was attributed to the weathering of the road samples used in this study, to the shorter towers used with balloon lights, and to major changes in asphalt construction practices and mix ingredients in the past 30 years. Recommendations based on the results of this study are presented. © 2008 ASCE