This research is focused on project scheduling with the aim to capture the monetary objectives of the project in the form of the maximisation of Net Present Value (NPV). In addition, this research is also highlighted key project management practices and scheduling methods. Project scheduling is very attractive for researchers and it has recently been drawn considerable attention because of the high cost of capital and the significant effect of the time value of money. This is the principal motivating factor behind this study. Project-scheduling problem is solved by priority rule-based heuristic methods in this study. The idea behind heuristic algorithms is to rank the activities by some rules. This research proposes a new rule called m-CCF with improved performance from the existing one. The m-CCF is also embedded in serial and parallel schedule generation schemes and is extended by implementing in a forward and backward strategy. The experiments are conducted to evaluate the performance of the proposed technique measuring the NPV generated for a particular project. This research also presents a framework summarising the previous research on project scheduling techniques. It is found that the m-CCF results in higher NPVs than any other heuristics. A series of different projects are examined to validate the potential of the m-CCF technique. The main findings of the research discover that the m-CCF is worthwhile to be employed in priority rule-based scheduling technique. Furthermore, the main findings suggest that it is beneficial to utilise forward-backward solution for scheduling improvement and selecting the schedule with the largest NPV among those available. In conclusion, this research contributes to existing knowledge by developing the combination of m-CCF priority rule methods and backward–forward scheduling. This can be considered as a good direction to develop further heuristics that can be exploited as a powerful tool in project planning and control systems.

The main goal of a scheduling process is to decide when and how to execute each of the project's activities. Despite large variety of researched scheduling problems, the majority of them can be described as generalisations of the resource-constrained project scheduling problem (RCPSP). Because of wide applicability and challenging difficulty, RCPSP has attracted vast amount of attention in the research community and great variety of heuristics have been adapted for solving it. Even though these heuristics are structurally different and operate according to diverse principles, they are designed to obtain only one solution at a time. In the recent researches on RCPSPs, it was proven that these kind of problems have complex multimodal fitness landscapes, which are characterised by a wide solution search spaces and presence of multiple local and global optima. The main goal of this thesis is twofold. Firstly, it presents a variation of the RCPSP that considers optimisation of projects in an uncertain environment where resources are modelled to adapt to their environment and, as the result of this, improve their efficiency. Secondly, modification of a novel evolutionary computation method Cuckoo Search (CS) is proposed, which has been adapted for solving combinatorial optimisation problems and modified to obtain multiple solutions. To test the proposed methodology, two sets of experiments are carried out. Firstly, the developed algorithm is applied to a real-life software development project. Secondly, the performance of the algorithm is tested on universal benchmark instances for scheduling problems which were modified to take into account specifics of the proposed optimisation model. The results of both experiments demonstrate that the proposed methodology achieves competitive level of performance and is capable of finding multiple global solutions, as well as prove its applicability in real-life projects.

The main goal of a scheduling process is to decide when and how to execute each of the project’s activities. Despite large variety of researched scheduling problems, the majority of them can be described as generalisations of the resource-constrained project scheduling problem (RCPSP). Because of wide applicability and challenging difficulty, RCPSP has attracted vast amount of attention in the research community and great variety of heuristics have been adapted for solving it. Even though these heuristics are structurally different and operate according to diverse principles, they are designed to obtain only one solution at a time. In the recent researches on RCPSPs, it was proven that these kind of problems have complex multimodal fitness landscapes, which are characterised by a wide solution search spaces and presence of multiple local and global optima. The main goal of this thesis is twofold. Firstly, it presents a variation of the RCPSP that considers optimisation of projects in an uncertain environment where resources are modelled to adapt to their environment and, as the result of this, improve their efficiency. Secondly, modification of a novel evolutionary computation method Cuckoo Search (CS) is proposed, which has been adapted for solving combinatorial optimisation problems and modified to obtain multiple solutions. To test the proposed methodology, two sets of experiments are carried out. Firstly, the developed algorithm is applied to a real-life software development project. Secondly, the performance of the algorithm is tested on universal benchmark instances for scheduling problems which were modified to take into account specifics of the proposed optimisation model. The results of both experiments demonstrate that the proposed methodology achieves competitive level of performance and is capable of finding multiple global solutions, as well as prove its applicability in real-life projects.

Project managers are faced with the challenging task of managing an environment filled with uncertainties that may lead to multiple disruptions during project execution. In particular, they are frequently confronted with planning for routine and non-routine unplanned work: known, identified, tasks that may or may not occur depending upon various, often unpredictable, factors. This problem is known as the stochastic task insertion problem, where tasks of deterministic duration occur stochastically. Traditionally, project managers may include an extra margin within deterministic task times or an extra time buffer may be allotted at the end of the project schedule to protect the final project completion milestone. Little scientific guidance is available to better integrate buffers strategically into the project schedule. Motivated by the Critical Chain and Buffer Management approach of Goldratt, this research identifies, defines, and demonstrates new buffer sizing techniques to improve project duration and stability metrics associated with the stochastic resource constrained project scheduling problem with stochastic task insertions. Specifically, this research defines and compares partial buffer sizing strategies for projects with varying levels of resource and network complexity factors as well as the level and location of the stochastically occurring tasks. Several project metrics may be impacted by the stochastic occurrence or non-occurrence of a task such as the project makespan and the project stability. New duration and stability metrics are developed in this research and are used to evaluate the effectiveness of the proposed buffer sizing techniques. These "robustness measures" are computed through the comparison of the characteristics of the initial schedule (termed the infeasible base schedule), a modified base schedule (or as-run schedule) and an optimized version of the base schedule (or perfect knowledge schedule). Seven new buffer sizing techniques are introduced in this research. Three are based on a fixed percentage of task duration and the remaining four provide variable buffer sizes based upon the location of the stochastic task in the schedule and knowledge of the task stochasticity characteristic. Experimental analysis shows that partial buffering produces improvements in the project stability and duration metrics when compared to other baseline scheduling approaches. Three of the new partial buffering techniques produced improvements in project metrics. One of these partial buffers was based on a fixed percentage of task duration and the other two used a variable buffer size based on knowledge of the location of the task in the project network. This research provides project schedulers with new partial buffering techniques and recommendations for the type of partial buffering technique that should be utilized when project duration and stability performance improvements are desired. When a project scheduler can identify potential unplanned work and where it might occur, the use of these partial buffer techniques will yield a better estimated makespan. Furthermore, it will result in less disruption to the planned schedule and minimize the amount of time that specific tasks will have to move to accommodate the unplanned tasks.

We propose a new method, based on semidefinite optimization, to find tight upper bounds on the expected project completion time and expected project tardiness in a stochastic project scheduling environment, when only limited information in the form of first and second (joint) moments of the durations of individual activities in the project is available. Our computational experiments suggest that the bounds provided by the new method are stronger and often significant compared to the bounds found by alternative methods. / Singapore-MIT Alliance (SMA)

It is a well-known fact that project activities are subject to considerable uncertainty, which may lead to multiple schedule disruptions during project execution. As a result, the random nature of activity durations has been the subject of numerous research efforts since the introduction of the initial PERT. A common problem which arises in project management is the fact that the planned schedule is often disrupted by several uncontrollable factors like weather conditions, other environmental factors, additional time that might be required for rework and correction of detected defects. As a result, project managers are often unable to meet the promised completion dates. It is therefore vital to take into account such possible disruptions and their potential negative consequences at the project schedule design stage. Hence, the ability of the pre-schedule to absorb disruptions may be very important in such settings. At this point two new criteria are used in modern scheduling literature: &quot / robustness&quot / and &quot / stability&quot / . In this thesis, we propose several stability measures. These measures are embedded in a tabu search algorithm to generate stable schedules in a multi resource environment subject to random disruptions.

abstract: Ramping up a semiconductor wafer fabrication facility is a challenging endeavor. One of the key components of this process is to schedule a large number of activities in installing and qualifying (Install/Qual) the capital intensive and sophisticated manufacturing equipment. Activities in the Install/Qual process share multiple types of expensive and scare resources and each activity might potentially have multiple processing options. In this dissertation, the semiconductor capital equipment Install/Qual scheduling problem is modeled as a multi-mode resource-constrained project scheduling problem (MRCPSP) with multiple special extensions. Three phases of research are carried out: the first phase studies the special problem characteristics of the Install/Qual process, including multiple activity processing options, time-varying resource availability levels, resource vacations, and activity splitting that does not allow preemption. A modified precedence tree-based branch-and-bound algorithm is proposed to solve small size academic problem instances to optimality. Heuristic-based methodologies are the main focus of phase 2. Modified priority rule-based simple heuristics and a modified random key-based genetic algorithm (RKGA) are proposed to search for Install/Qual schedules with short makespans but subject to resource constraints. Methodologies are tested on both small and large random academic problem instances and instances that are similar to the actual Install/Qual process of a major semiconductor manufacturer. In phase 3, a decision making framework is proposed to strategically plan the Install/Qual capacity ramp. Product market demand, product market price, resource consumption cost, as well as the payment of capital equipment, are considered. A modified simulated annealing (SA) algorithm-based optimization module is integrated with a Monte Carlo simulation-based simulation module to search for good capacity ramping strategies under uncertain market information. The decision making framework can be used during the Install/Qual schedule planning phase as well as the Install/Qual schedule execution phase when there is a portion of equipment that has already been installed or qualified. Computational experiments demonstrate the effectiveness of the decision making framework. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2015

The main goal of a scheduling process is to decide when and how to execute each of the project’s activities. Despite large variety of researched scheduling problems, the majority of them can be described as generalisations of the resource-constrained project scheduling problem (RCPSP). Because of wide applicability and challenging difficulty, RCPSP has attracted vast amount of attention in the research community and great variety of heuristics have been adapted for solving it. Even though these heuristics are structurally different and operate according to diverse principles, they are designed to obtain only one solution at a time. In the recent researches on RCPSPs, it was proven that these kind of problems have complex multimodal fitness landscapes, which are characterised by a wide solution search spaces and presence of multiple local and global optima. The main goal of this thesis is twofold. Firstly, it presents a variation of the RCPSP that considers optimisation of projects in an uncertain environment where resources are modelled to adapt to their environment and, as the result of this, improve their efficiency. Secondly, modification of a novel evolutionary computation method Cuckoo Search (CS) is proposed, which has been adapted for solving combinatorial optimisation problems and modified to obtain multiple solutions. To test the proposed methodology, two sets of experiments are carried out. First, the developed algorithm is applied to a real-life software development project. Second, performance of the algorithm is tested on universal benchmark instances for scheduling problems which were modified to take into account specifics of the proposed optimisation model. The results of both experiments demonstrate that the proposed methodology achieves competitive level of performance and is capable of finding multiple global solutions, as well as prove its applicability in real-life projects.

In Multi-Skill Project Scheduling Problem (MSPSP) with hierarchical levels of skills, there are more than one skill type and for each skill type there are levels corresponding to proficiencies in that skill. The purpose of the problem is to minimize or maximize an objective by assigning resources with different kinds of skills and skill levels to the project activities according to the activity requirements while satisfying the other problem dependent constraints. Although single-objective case of the problem has been studied by a few researchers, biobjective case has not been studied yet. In this study, two objectives, which are the makespan and the total skill wasted, are taken into account and while trying to minimize the makespan, minimizing the total skills wasted is aimed. By the second objective, overqualification for the jobs is tried to be minimized in order to prevent job dissatisfaction. The biobjective problem is solved using a Multiobjective Genetic Algorithm, NSGA-II. The results of the proposed algorithm are compared with the GAMS results for small-sized problems and with the random search for larger problem sizes.

無 / Critical Chain Project Management (CCPM) has merged in last few years as a novel approach for managing projects. While there were many previous researches studied CCPM concerning with single project management, but CCPM multi project management was hardly paid attention, especially capacity-constraint buffer sizing approach. However, there were some research papers which examined and illustrated CCPM under multi-project environment; those papers assumed all the subprojects were identical. Despite the fact that such situation is impractical. The purpose of this dissertation is to compare Cut and paste method (C&PM) with Root square error method (RSEM) for applying in project buffer, feeding buffer and capacity-constraint buffer sizing and to change some subproject parameters which make an impact on the project schedule for multi-project scheduling. Keywords: Critical chain project management, Multi Project Scheduling, Buffer Management, Capacity constraint buffer, Buffer sizing method.