This title presents a large variety of models and algorithms dedicated to the resource-constrained project scheduling problem (RCPSP), which aims at scheduling at minimal duration a set of activities subject to precedence constraints and limited resource availabilities. In the first part, the standard variant of RCPSP is presented and analyzed as a combinatorial optimization problem. Constraint programming and integer linear programming formulations are given. Relaxations based on these formulations and also on related scheduling problems are presented. Exact methods and heuristics are surveyed. Computational experiments, aiming at providing an empirical insight on the difficulty of the problem, are provided. The second part of the book focuses on several other variants of the RCPSP and on their solution methods. Each variant takes account of real-life characteristics which are not considered in the standard version, such as possible interruptions of activities, production and consumption of resources, cost-based approaches and uncertainty considerations. The last part presents industrial case studies where the RCPSP plays a central part. Applications are presented in various domains such as assembly shop and rolling ingots production scheduling, project management in information technology companies and instruction scheduling for VLIW processor architectures.

Within a project human and non-human resources are pulled together in a tempo raray organization in order to achieve a predefined goal (d. [20], p. 187). That is, in contrast to manufacturing management, project management is directed to an end. One major function of project management is the scheduling of the project. Project scheduling is the time-based arrangement of the activities comprising the project subject to precedence-, time-and resource-constraints (d. [4], p. 170). In the 1950's the standard methods MPM (Metra Potential Method) and CPM (Cri tical Path Method) were developed. Given deterministic durations and precedence constraints the minimum project length, time windows for the start times and critical paths can be calculated. At the same time another group of researchers developed the Program Evaluation and Review Technique (PERT) (d. [19], [73] and [90]). In contrast to MPM and CPM, random variables describe the activity durations. Based on the optimistic, most likely and pessimistic estimations of the activity durations an assumed Beta distribution is derived in order to calculate the distribution of the project duration, the critical events, the distribution of earliest and latest occurence of an event, the distribution of the slack of the events and the probability of exceeding a date. By the time the estimates of the distributions have been improved (d. e.g. [52] and [56]). Nevertheless, there are some points of critique concerning the estimation of the resulting distributions and probabilities (d. e.g. [48], [49] and [50]).

This book introduces the field of resource-constrained project scheduling. State-of-the-art reviews of optimal and heuristic procedures are provided for classical project scheduling models. Furthermore, new models which are relevant for practical problem settings, are introduced. The main emphasis is on newly developed competitive heuristic methods. Contents: Introduction. - Description of the Problems.- Classification of Schedules.- Characterisation and Generation of Instances.- The Single-Mode Project Scheduling Problem.- The Multi-Mode Project Scheduling Problem.- Project Scheduling with Given Deadline.- Project Scheduling with Setup Times.- Applications to Production Management.- Concluding Remarks.- List of Notations.- List of Abbreviations.

This book deals with dynamic and stochastic methods for multi-project planning. Based on the idea of using queueing networks for the analysis of dynamic-stochastic multi-project environments this book addresses two problems: detailed scheduling of project activities, and integrated order acceptance and capacity planning. In an extensive simulation study, the book thoroughly investigates existing scheduling policies. To obtain optimal and near optimal scheduling policies new models and algorithms are proposed based on the theory of Markov decision processes and Approximate Dynamic programming. Then the book presents a new model for the effective computation of optimal policies based on a Markov decision process. Finally, the book provides insights into the structure of optimal policies.

This multi-author volume, containing contributions from international experts in the field, presents recent developments in project scheduling for both theory and practice. It is organized in three parts: I. Basic deterministic models; II. Special deterministic models; III. Stochastic models. A variety of approaches is presented dealing with multiple-category resource constraints, different mathematical models of activities, and various project performance measures in single and multiobjective formulation. Exact and heuristic algorithms are presented for both deterministic and stochastic project description.The volume will be of special interest to scientists, students, decision makers, executive managers, consultants and practitioners involved in systems management or operations research, in particular in business, engineering, and finance, but also in other areas of pure and applied sciences.