Part I: Classical Models - Classification, Exact Algorithms. 1. A Classification Scheme for Project Scheduling; W. Herroelen, et al. 2. Solving Large-Sized Resource-Constrained Project Scheduling Problems; P. Brucker, S. Knust. 3. Lower Bounds in Different Problem Classes of Project Schedules with Resource Constraints; E. Pesch. 4. Algorithms for Scheduling Projects with Generalized Precedence Relations; B. de Reyck, et al. 5. An Exact Solution Procedure for Maximizing the Net Present Value of Cash Flows in a Network; S. Baroum, J. Patterson. 6. Solving a Preemptive Project Scheduling Problem with Coloring Techniques; L. Bianco, et al. Part II: Classical Models - Heuristics, Benchmark Instances, Software Evolution. 7. Heuristic Algorithms for the Resource-Constrained Project Scheduling Problem: Classification and Computational Analysis; R. Kolisch, S. Hartman. 8. A Heuristic Procedure for the Multi-Mode Project Scheduling Problem Based on Benders' Decomposition; V. Maniezzo, A. Mingozzi. 9. Benchmark Instances for Project Scheduling Problems; R. Kolisch, et al. 10. A Survey of Interval Capacity Consistency Tests for Time- and Resource-Constrained Scheduling; U. Dorndorf, et al. 11. The Evolution of Software Quality in Project Scheduling; C. Maroto, et al. Part III: New Models. 12. Methods for Resource-Constrained Project Scheduling with Regular and Nonregular Objective Functions and Schedule-Dependent Time Windows; K. Neumann, J. Zimmerman. 13. Project Scheduling under Discrete and Continuous Resources; J. Jó;zefowska, et al. 14. Scheduling of Projects with Stochastic Evolution Structure; K. Neumann. 15. Project Scheduling with Stochastic Activity Interruptions; V. Valls, et al. 16. Fuzzy Multi-Mode Resource-Constrained Project Scheduling with Multiple Objectives; M. Hapke, et al. 17. Knowledge-Based Multiobjective Project Scheduling Problems; J. Nabrzyski, J. Węglarz. Part IV: Extensions and Applications. 18. New Modelling Concepts and Their Impact on Resource-Constrained Project Scheduling; A. Drexl, et al. 19. Integrating Quality as a Measure of Performance in Resource-Constrained Project Scheduling Problems; S.S. Erenguc, O.I. Tukel. 20. Cognitive Science and Project Scheduling: More Realistic Representation; P. Baptiste, O. Grunder. 21. On Payment Scheduling in Client-Contractor Negotiations in Projects: An Overview of the Problem and Research Issues; N. Dayanand, R. Padman. 22. Project Management in Audit Staff Scheduling; B. Dodin.
Project scheduling problems are, generally speaking, the problems of allocating scarce resources over time to perform a given set of activities. The resources are nothing other than the arbitrary means which activities complete for. Also the activities can have a variety of interpretations. Thus, project scheduling problems appear in a large spectrum of real-world situations, and, in consequence, they have been intensively studied for almost fourty years. Almost a decade has passed since the multi-author monograph: R. Slowinski, 1. W~glarz (eds. ), Advances in Project Scheduling, Elsevier, 1989, summarizing the state-of-the-art across project scheduling problems, was published. Since then, considerable progress has been made in all directions of modelling and finding solutions to these problems. Thus, the proposal by Professor Frederick S. Hillier to edit a handbook which reports on the recent advances in the field came at an exceptionally good time and motivated me to accept the challenge. Fortunately, almost all leading experts in the field have accepted my invitation and presented their completely new advances often combined with expository surveys. Thanks to them, the handbook stands a good chance of becoming a key reference point on the current state-of-the-art in project scheduling, as well as on new directions in the area. The contents are divided into four parts. The first one, dealing with classical models -exact algorithms, is preceded by a proposition of the classification scheme for scheduling problems.
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