In this thesis, meta-heuristic algorithms are developed to obtain optimum or near optimum solutions for the time-cost-resource trade-off and resource leveling problems in project scheduling. Time cost trade-off, resource leveling, single-mode resource constrained project scheduling, multi-mode resource constrained project scheduling and resource constrained time cost trade-off problems are analyzed. Genetic algorithm simulated annealing, quantum simulated annealing, memetic algorithm, variable neighborhood search, particle swarm optimization, ant colony optimization and electromagnetic scatter search meta-heuristic algorithms are implemented for time cost trade-off problems with unlimited resources. In this thesis, three new meta-heuristic algorithms are developed by embedding meta-heuristic algorithms in each other. Hybrid genetic algorithm with simulated annealing presents the best results for time cost trade-off. Resource leveling problem is analyzed by five genetic algorithm based meta-heuristic algorithms. Apart from simple genetic algorithm, four meta-heuristic algorithms obtained same schedules obtained in the literature. In addition to this, in one of the test problems the solution is improved by the four meta-heuristic algorithms. For the resource constrained scheduling problems / genetic algorithm, genetic algorithm with simulated annealing, hybrid genetic algorithm with simulated annealing and particle swarm optimization meta-heuristic algorithms are implemented. The algorithms are tested by using the project sets of Kolisch and Sprecher (1996). Genetic algorithm with simulated annealing and hybrid genetic algorithm simulated annealing algorithm obtained very successful results when compared with the previous state of the art algorithms. 120-activity multi-mode problem set is produced by using the single mode problem set of Kolisch and Sprecher (1996) for the analysis of resource constrained time cost trade-off problem. Genetic algorithm with simulated annealing presented the least total project cost.

In project scheduling, the activity durations can often be reduced by dedicating additional resources. Time/Cost Trade-off Problem considers the compromise between the total cost and project duration. The discrete version of the problem assumes a number of time/cost pairs, so called modes, and selects a mode for each activity. In this thesis we consider the Discrete Time/Cost Trade-off Problem. We first study the Deadline Problem, i.e., the problem of minimizing total cost subject to a deadline on project duration. To solve the Deadline Problem, we propose several optimization and approximation algorithms that are based on optimal Linear Programming Relaxation solutions. We then analyze the problem of generating all efficient solutions, and propose an approach that uses the successive solutions of the Deadline Problem. Our computational results on large-sized problem instances have revealed the satisfactory behavior of our algorithms.

In this thesis, we consider a discrete time/cost trade-off problem with a single nonrenewable resource. We assume the resource is released at some prespecified time points and at some prespecified quantities. We also assume that the costs due to the activities are incurred at their completions. Our aim is to minimize total project completion time. We formulate the problem as a pure integer programming model. We show that the problem is strongly NP-hard. We find lower bounds by pure linear programming and mixed integer linear programming relaxations of the model. We develop three heuristic procedures using the optimal solutions of mixed integer linear program and pure linear program. The results of our computational study reveal the satisfactory performance of our heuristic procedures.

The negative impacts associated with highway pavement rehabilitation projects drove state highway agencies (SHAs) towards increased adoption of alternative contracting methods (ACMs) to accelerate the construction of such projects; hence, reducing these impacts on the travelling public. However, the application of such methods showed mixed results due to the lack of specific guidelines addressing the adoption of such methods and the selection of the best ACM for each project. This lack of guidelines stems from the lack of research studies examining the impact of each of these methods on the time/cost trade-off relationship in highway rehabilitation projects. Existing literature includes several studies aimed at developing generic and subjective guidelines based on past experiences that do not take into consideration the unique nature of each of these methods. Hence, this research study aimed at analyzing the SHAs’ decision making process regarding two of the most-widely used ACMs: Incentive/Disincentive (I/D) and Cost + Time (A+B) contracting methods, in order to support decision makers in choosing the most-suitable method for their projects. To this end, two models were developed in this dissertation to examine the time/cost trade-off for each method using simulation and regression analysis. Each model was validated against real-life projects and used to assign appropriate ID and “B” values based on the SHA’s desired duration reduction and available budget. Furthermore, a risk analysis module was developed to determine the most-likely duration reduction that the contractor can achieve for each project under each method. The developed models should help improve the decision making process regarding the selection and implementation of these methods in highway rehabilitation projects. For example, the models can help SHAs identify the minimum ID level that can be offered for each project and the expected duration that the contractors can bid on under the A+B contracting method. Finally, the models were contrasted and applied to real-life projects with different characteristics to verify existing guidelines and establish the candidate ACM for each project category. The findings of this study will benefit the society, SHAs, and the economy in general by optimizing the use of available time and money resources.

Optimization of time and cost in construction projects has been subject to extensive research since the development of the Critical Path Method (CPM). Many researchers have investigated various versions of the well-known Time-Cost Trade-off (TCT) problem including linear, convex, concave, and also the discrete (DTCT) version. Traditional methods in the literature for optimizing time and cost of construction projects range from mathematical methods to evolutionary-based ones, such as genetic algorithms, particle swarm, ant-colony, and leap frog optimization. However, none of the existing research studies has dealt with the optimization of large-scale projects in which any small saving would be significant. Traditional approaches have all been applied to projects of less than 100 activities which are far less than what exists in real-world construction projects. The objective of this study is to utilize recent developments in computation technology and novel optimization techniques such as Constraint Programming (CP) to improve the current limitations in solving large-scale DTCT problems. Throughout the first part of this research, an Excel-based TCT model has been developed to investigate the performance of traditional optimization methods, such as mathematical programming and genetic algorithms, for solving large TCT problems. The result of several experimentations confirms the inefficiency of traditional methods for optimizing large TCT problems. Subsequently, a TCT model has been developed using Optimization Programming Language (OPL) to implement the Constraint Programming (CP) technique. CP Optimizer of IBM ILOG Optimization Studio has been used to solve the model and to successfully optimize several projects ranging from a small project of 18 activities to very large projects consisting of more than 10,000 activities. Constraint programming proved to be very efficient in solving large-scale TCT problems, generating substantially better results in terms of solution quality and processing speed. While traditional optimization methods have been used to optimize projects consisting of less than one hundred activities, constraint programming demonstrated its capability of solving TCT problems comprising of thousands of activities. As such, the developed model represents a significant improvement in optimization of time and cost of large-scale construction projects and can greatly enhance the level of planning and control in such projects.

The time/cost trade-off models in project management aim to compress the project completion time by accelerating the activity durations at an expense of additional resources. The budget problem in discrete time/cost trade-off scheduling selects the time/cost mode -among the discrete set of specified modes- for each activity so as to minimize the project completion time without exceeding the available budget. There may be alternative modes that solve the budget problem optimally, however each solution may have a different total cost value. In this study we aim to find the minimum cost solution among the optimal solutions of the budget problem. We analyze the structure of the problem together with its linear programming relaxation and derive some mechanisms for reducing the problem size. We solve the reduced problem by linear programming relaxation and branch and bound based approximation and optimization algorithms. We find that our branch and bound algorithm finds optimal solutions for medium-sized problem instances in reasonable times and the approximation algorithms produce high quality solutions. We also discuss the way our algorithms could be used to construct the time/cost trade-off curve.

Optimization of time and cost in construction projects has been subject to extensive research since the development of the Critical Path Method (CPM). Many researchers have investigated various versions of the well-known Time-Cost Trade-off (TCT) problem including linear, convex, concave, and also the discrete (DTCT) version. Traditional methods in the literature for optimizing time and cost of construction projects range from mathematical methods to evolutionary-based ones, such as genetic algorithms, particle swarm, ant-colony, and leap frog optimization. However, none of the existing research studies has dealt with the optimization of large-scale projects in which any small saving would be significant. Traditional approaches have all been applied to projects of less than 100 activities which are far less than what exists in real-world construction projects. The objective of this study is to utilize recent developments in computation technology and novel optimization techniques such as Constraint Programming (CP) to improve the current limitations in solving large-scale DTCT problems. Throughout the first part of this research, an Excel-based TCT model has been developed to investigate the performance of traditional optimization methods, such as mathematical programming and genetic algorithms, for solving large TCT problems. The result of several experimentations confirms the inefficiency of traditional methods for optimizing large TCT problems. Subsequently, a TCT model has been developed using Optimization Programming Language (OPL) to implement the Constraint Programming (CP) technique. CP Optimizer of IBM ILOG Optimization Studio has been used to solve the model and to successfully optimize several projects ranging from a small project of 18 activities to very large projects consisting of more than 10,000 activities. Constraint programming proved to be very efficient in solving large-scale TCT problems, generating substantially better results in terms of solution quality and processing speed. While traditional optimization methods have been used to optimize projects consisting of less than one hundred activities, constraint programming demonstrated its capability of solving TCT problems comprising of thousands of activities. As such, the developed model represents a significant improvement in optimization of time and cost of large-scale construction projects and can greatly enhance the level of planning and control in such projects.

In pursuance of decreasing costs, both the client and the contractor would strive to speed up the construction project. However, accelerating the project schedule will impose additional cost and might be profitable up to a certain limit. Paramount for construction management, analyses of this trade-off between duration and cost is hailed as the time-cost trade-off (TCT) optimization. Inadequacies of existing commercial software packages for such analyses tied with eminence of discretization, motivated development of different paradigms of particle swarm optimizers (PSO) for three extensions of discrete TCT problems (DTCTPs). A sole-PSO algorithm for concomitant minimization of time and cost is proposed which involves minimal adjustments to shift focus to the completion deadline problem. A hybrid model is also developed to unravel the time-cost curve extension of DCTCPs. Engaging novel principles for evaluation of cost-slopes, and pbest/gbest positions, the hybrid SAM-PSO model combines complementary strengths of overhauled versions of the Siemens Approximation Method (SAM) and the PSO algorithm. Effectiveness and efficiency of the proposed algorithms are validated employing instances derived from the literature. Throughout computational experiments, mixed integer programming technique is implemented to introduce the optimal non-dominated fronts of two specific benchmark problems for the very first time in the literature. Another chief contribution of this thesis can be depicted as potency of SAM-PSO model in locating the entire Pareto fronts of the practiced instances, within acceptable time-frames with reasonable deviations from the optima. Possible further improvements and applications of SAM-PSO model are suggested in the conclusion.

This study develops a systems integration project scheduling model which identifies the assignment of activity responsibilities that minimizes expected project implementation cost, considering the project risk. Assignment of resources to the individual jobs comprising the project is a persistent problem in project management. Mostly, skilled labor is an essential resource and both the time and the cost incurred to perform a job depend on the resource to which job is assigned. A systems integration project includes implementation issues in the areas of shipping, installation, and commissioning. Implementation problems lead to project delays, increased costs, and decreased performance, leading to customer dissatisfaction with the systems integrator. Activities can be performed in one of three ways: by the integrator, by the customer, or jointly between the integrator and customer. In this study we select the performer (mode) of each activity comprising the project network while taking into consideration the varying cost, duration and extreme event probability of each activity among different modes-integrator, joint work and customer. Use of the model will permit customers and integrators to mutually agree on an appropriate assignment of responsibilities in the contract. Systems integrators can also use the model to improve their implementation services offerings. An experimental design and a Monte-Carlo simulation study were conducted to see the effects of the parameters of the problem on the selection of modes.

This work focuses on the need for new knowledge to allow hierarchical multi-project management to be conducted in the construction industry, which is characterised by high uncertainty, fragmentation, complex decisions, dynamic changes and long-distance communication. A dynamic integrated project management approach is required at strategic, tactical and operational levels in order to achieve adaptability. The work sees the multi-project planning and control problem in the context of supply chain management at main contractor companies. A portfolio manager must select and prioritise the projects, bid and negotiate with a wide range of clients, while project managers are dealing with subcontractors, suppliers, etc whose relationships and collaborations are critical to the optimisation of schedules in which time, cost and safety (etc) criteria must be achieved. Literature review and case studies were used to investigate existing approaches to hierarchical multi-project management, to identify the relationships and interactions between the parties concerned, and to investigate the possibilities for integration. A system framework was developed using a multi-agent-system architecture and utilising procedures adapted from literature to deal with short, medium and long-term planning. The framework is based on in-depth case study and integrates time-cost trade-off for project optimisation with multi-attribute utility theory to facilitate project scheduling, subcontractor selection and bid negotiation at the single project level. In addition, at the enterprise level, key performance indicator rule models are devised to align enterprise supply chain configuration (strategic decision) with bid selection and bid preparation/negotiation (tactical decision) and project supply chain selection (operational decision). Across the hierarchical framework the required quantitative and qualitative methods are integrated for project scheduling, risk assessment and subcontractor evaluation. Thus, experience sharing and knowledge management facilitate project planning across the scattered construction sites. The mathematical aspects were verified using real data from in-depth case study and a test case. The correctness, usefulness and applicability of the framework for users was assessed by creating a prototype Multi Agent System-Decision Support System (MAS-DSS) which was evaluated empirically with four case studies in national, international, large and small companies. The positive feedback from these cases indicates strong acceptance of the framework by experienced practitioners. It provides an original contribution to the literature on planning and supply chain management by integrating a practical solution for the dynamic and uncertain complex multi-project environment of the construction industry.