Record Nr.

UNINA9910298517303321

Autore

Melchiors Philipp

Titolo

Dynamic and Stochastic Multi-Project Planning / / by Philipp Melchiors

Pubbl/distr/stampa


Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015

ISBN

3-319-04540-7

Edizione

[1st ed. 2015.]

Descrizione fisica

1 online resource (216 p.)

Collana

Lecture Notes in Economics and Mathematical Systems, , 0075-8442 ; ; 673

Disciplina

330

519.6

658.40301

658.5

658404

658514

Soggetti

Project management

Operations research

Decision making

Management science

Management

Industrial management

Production management

Mathematical optimization

Project Management

Operations Research/Decision Theory

Operations Research, Management Science

Innovation/Technology Management

Operations Management

Discrete Optimization

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references.

Nota di contenuto

1. Introduction -- 2. Problem Statements -- 3. Literature Review -- 4. Continuous-time Markov Decision Processes -- 5. Generation of

Problem Instances -- 6. Scheduling Using Priority Policies -- 7. Optimal and Near Optimal Scheduling Policies -- 8. Integrated Dynamic Order Acceptance and Capacity Planning -- 9. Conclusions and Future Work.

Sommario/riassunto

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.