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Scheduling problems have been studied since the late 1950s, primarily motivated by project planning and machine scheduling applications. Early research in project planning focused on scheduling with precedence constraints, assuming sufficient resources for activities. More recently, resource-constrained project scheduling problems have emerged, integrating scarce resources into the planning process. Similarly, machine scheduling has evolved to address more complex scenarios, bridging the gap between the two fields. Applications such as timetabling, rostering, and industrial scheduling link these areas further. This book explores complex scheduling problems and their solutions, structured in three parts. The first part introduces basic scheduling models and discrete optimization, covering complexity, shortest path algorithms, linear programming, network flow algorithms, and general optimization methods. The second part delves into resource-constrained project scheduling problems, detailing methods like constraint propagation, branch-and-bound algorithms, and heuristic procedures, along with discussions on lower bounds and general objective functions.

Besides scheduling problems for single and parallel machines and shop scheduling problems the book covers advanced models involving due-dates, sequence dependent changeover times and batching. Also multiprocessor task scheduling and problems with multipurpose machines are discussed. The methods used to solve these problems are linear programming, dynamic programming, branch-and-bound algorithms, and local search heuristics. Complexity results for the different classes of deterministic scheduling problems are summarized.