Scheduling of Resource-Constrained Projects

Book excerpt: Project management has become a widespread instrument enabling organizations to efficiently master the challenges of steadily shortening product life cycles, global markets and decreasing profit margins. With projects increasing in size and complexity, their planning and control represents one of the most crucial management tasks. This is especially true for scheduling, which is concerned with establishing execution dates for the sub-activities to be performed in order to complete the project. The ability to manage projects where resources must be allocated between concurrent projects or even sub-activities of a single project requires the use of commercial project management software packages. However, the results yielded by the solution procedures included are often rather unsatisfactory. Scheduling of Resource-Constrained Projects develops more efficient procedures, which can easily be integrated into software packages by incorporated programming languages, and thus should be of great interest for practitioners as well as scientists working in the field of project management. The book is divided into two parts. In Part I, the project management process is described and the management tasks to be accomplished during project planning and control are discussed. This allows for identifying the major scheduling problems arising in the planning process, among which the resource-constrained project scheduling problem is the most important. Part II deals with efficient computer-based procedures for the resource-constrained project scheduling problem and its generalized version. Since both problems are NP-hard, the development of such procedures which yield satisfactory solutions in a reasonable amount of computation time is very challenging, and a number of new and very promising approaches are introduced. This includes heuristic procedures based on priority rules and tabu search as well as lower bound methods and branch and bound procedures which can be applied for computing optimal solutions.