EMC_PPSSP_2021: Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling |
Submission link | https://easychair.org/conferences/?conf=emc-ppssp-2021 |
Abstract registration deadline | March 31, 2021 |
Submission deadline | March 31, 2021 |
Submission To Springer (Expected) | July 31, 2021 |
Synopsis
Many organizations face the challenge of selecting and scheduling a subset of available projects subject to various resource and operational constraints. Formally, this problem is known as the project portfolio selection and scheduling problem (PPSSP). It is well-known that the PPSSP is an NP-hard problem and, thus, there is no known polynomial algorithm for this problem. Despite the complexity associated with solving the PPSSP, many traditional approaches to this problem make use of exact solvers. While exact solvers provide definitive optimal solutions, they quickly become prohibitively expensive in terms of computation time when the problem size is increased. In contrast, evolutionary and memetic computing afford the capability for autonomous heuristic approaches and expert knowledge to be combined, and thereby provide an efficient means for high-quality approximation solutions to be attained. As such, these approaches can provide near real-time decision support information for portfolio design that can be used to augment and improve existing human-centric strategic decision-making processes.
This edited book will provide the reader with a broad overview of the PPSSP, its associated challenges, and approaches to addressing the problem using evolutionary and memetic computing. We welcome contributions by distinguished researchers and industry practitioners that highlight the state-of-the-art and recent trends in memetic computing and evolutionary computation for the PPSSP in any domain.
Topics of interest include (but not limited to):
- Review of evolutionary and memetic algorithms for portfolio selection and scheduling (one chapter only)
- Modelling and analysis of practical portfolio selection and scheduling (one/two chapters)
- New evolutionary and memetic algorithms for portfolio selection and/or scheduling
- Applications of evolutionary and memetic algorithms for portfolio selection and/or scheduling
- Portfolio selection and/or scheduling in complex organisation environments such as defence, non-profit organisations, and government departments
Submission Guidelines
Expressions of interest for chapter submissions, as a 1-2 paragraph abstract, are invited from distinguished researchers and industry practitioners. Completed chapter submissions are expected by 31 March 2021. Later submissions may also be considered, at the discretion of the editors. All submitted chapters will undergo a peer review process and must adhere to the Springer style and formatting requirements. Specifically, the use of the SVMult LaTeX package, available from Springer, is recommended. Chapters are expected to be between 25 and 30 pages, including figures and references, and must not be concurrently submitted to any other publication venue. Contributions that are deemed to be substantially similar to other published materials, at the discretion of the editors, may be rejected without review. Submissions are to be made through EasyChair at the following link: https://easychair.org/conferences/?conf=emc-ppssp-2021. Chapter contributors may be requested to serve as reviewers for other contributions.
Book Editors
- Dr. Kyle Robert Harrison, University of New South Wales Canberra at the Australian Defence Force Academy, Australia
- Dr. Saber Mohamed Elsayed, University of New South Wales Canberra at the Australian Defence Force Academy, Australia
- Dr. Ivan L. Garanovich, Defence Science and Technology Group, Department of Defence, Australia
- Dr. Terence Weir, Defence Science and Technology Group, Department of Defence, Australia
- Dr. Sharon G. Boswell, Defence Science and Technology Group, Department of Defence, Australia
- Prof. Ruhul Amin Sarker, University of New South Wales Canberra at the Australian Defence Force Academy, Australia
Publication
Accepted chapters will be published in a book entitled Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling, to be published by Springer as part of the book series Adaptation, Learning, and Optimization.
Contact
Contact Kyle Robert Harrison (kyle.harrison@unsw.edu.au) for inquiries or further information.