An Optimization Model for the Dynamic Multi-Project Environment in Construction

This research is an effort in the field of construction multi-objective optimization. The purpose of the study is to improve construction business productivity or efficiency from the point of view of a general contractor.

The construction business is highly competitive, which is why a general contractor, one of the key players, must improve their performance in order to survive. A general contractor works on multiple projects, with different types of contracts and objectives, at the same time and is constantly challenged with balancing resources, schedules and making the right decisions, so that successful outcomes can be achieved for their projects. This is a typical multi-objective optimization problem; and, it is, very often, a complex task to find the best solution, as there are many variables that can change very rapidly.

In order to improve the construction business efficiency of a general contractor, the primary objective of this thesis is the development of an efficient multi-objective model that is capable of finding near optimal solutions for conflicting objectives when managing multiple-projects.

First, a conceptual framework, using a multi-objective optimization approach, is developed for identifying and designing the essential components and theoretical basis of the research problem, in order to find near optimal solutions and achieve the overall goal of the study.

Second, a model is developed for formulating the research problem, in terms of mathematical equations and algorithms, by identifying and describing the three objective functions (minimization of project duration, maximization of resource utilization, and maximization of profit), decision variables, and constraints. Time-resource relationship equations and cost breakdown equations for a high-rise building project are also derived.

Third, a simulation model is designed, developed and programmed for analyzing multi-project schedules, predicting potential problems, and forecasting the most likely durations of projects and program, by capturing the dynamic nature of schedules, randomness of activity duration, and project risks and uncertainties. The main purpose of the simulation model is to improve the computing efficiency of the optimization model.

Fourth, a genetic algorithm based optimization model is designed and developed, through integration of the problem formulation and simulation model, for finding near optimal solutions to the multi-objective problem in a real-time situation and helping decision-makers to optimize their business outcome.

The contributions of this research are a) the development of an efficient multi-objective optimization model that is capable of finding near optimal solutions for conflicting objectives when managing multiple-projects, b) the development of a new set of algorithms for formulating a typical multi-objective optimization problem in construction from a general contractor's point of view, and c) the development of a new and effective way to improve the computing efficiency for finding near optimal solutions.