A Multicriteria Decision Support System for Competence-Driven Project Portfolio Selection

Stummer, Christian; Kiesling, Elmar; Gutjahr, Walter J.

doi:10.1142/s0219622009003429

Cited by 52 publications

(22 citation statements)

References 55 publications

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“…For instance, instead of considering expertise as a Boolean property, one can imagine that there might be developers with low, medium or high expertise, as in the work of Gutjahr et al [36] and Stummer et al [20]. Moreover, complex projects might require more than one expertise for each WP, thus future extensions of the approach proposed in this paper should support that.…”

Section: Resultsmentioning

confidence: 99%

“…• the possibility of periodically re-organizing the people distribution to teams, as well as the possibility (as in [36,20]) that developers might only spend part of their available time on the WP they have been assigned to.…”

Section: Resultsmentioning

confidence: 99%

“…We share with Gutjahr et al [36] and and Stummer et al [20] the assumption of atomic tasks, the fact that a work package is organized around a single precise skill and the high level ideas. However, they tackle a different problem, the portfolio managment problem while we assume the project is already assigned.…”

Section: Related Workmentioning

confidence: 92%

“…Recently, Gutjahr et al [36] and Stummer et al [20] formulated the portfolio selection scheduling and staffing problem as a non linear mixed integer optimization. The proposed objective function aggregates two terms: a per project gain and a global company efficiency improvement over the scheduled time span.…”

Section: Related Workmentioning

confidence: 99%

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The use of search‐based optimization techniques to schedule and staff software projects: an approach and an empirical study

2011

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SUMMARYAllocating resources to a software project and assigning tasks to teams constitute crucial activities that affect project cost and completion time. Finding a solution for such a problem is NP-hard; this requires managers to be supported by proper tools in performing such an allocation. This paper shows how search-based optimization techniques can be combined with a queuing simulation model to address these problems. The obtained staff and task allocations aim to minimize the completion time and reduce schedule fragmentation. The proposed approach allows project managers to run multiple simulations, compare results and consider trade-offs between increasing the staffing level and anticipating the project completion date and between reducing the fragmentation and accepting project delays. The paper presents results from the application of the proposed search-based project planning approach to data obtained from two large scale commercial software maintenance projects.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 92%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

The use of search‐based optimization techniques to schedule and staff software projects: an approach and an empirical study

2011

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“…Moreover, with growing number of objectives, the number of efficient solutions may become too large for evaluation by direct comparison. The article Stummer et al (2009) proposes an interactive decision support system enabling the user to explore the solutions of model (34.22)-(34.24). Graphical representation tools are offered to facilitate the analysis, and special mechanisms allow a gradual interactive restriction of the currently considered set of candidate solutions by introducing or strengthening aspiration levels for certain objective functions.…”

Section: Other Recent Literaturementioning

confidence: 99%

Robust Optimization for the Discrete Time-Cost Tradeoff Problem with Cost Uncertainty

Hazır

Haouari

Erel

2014

Handbook on Project Management and Scheduling Vol. 2

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This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher's location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) PrefaceThis handbook is devoted to scientific approaches to the management and scheduling of projects. Due to their practical relevance, project management and scheduling have been important subjects of inquiry since the early days of Management Science and Operations Research and remain an active and vibrant field of study. The handbook is meant to provide an overview of some of the most active current areas of research. Each chapter has been written by well-recognized scholars, who have made original contributions to their topic. The handbook covers both theoretical concepts and a wide range of applications. For our general readers, we give a brief introduction to elements of project management and scheduling in the first chapter, where we also survey the contents of this book. We believe that the handbook will be a valuable and comprehensive reference to researchers and practitioners in project management and scheduling and hope that it might stimulate further research in this exciting and practically important field.Short-listing and selecting the contributions to this handbook and working with more than one hundred authors have been a challenging and rewarding experience for us. We are grateful to Günter Sch...

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Blended near‐optimal alternative generation, visualization, and interaction for water resources decision making

Rosenberg

2015

Water Resources Research

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State-of-the-art systems analysis techniques focus on efficiently finding optimal solutions. Yet an optimal solution is optimal only for the modeled issues and managers often seek near-optimal alternatives that address unmodeled objectives, preferences, limits, uncertainties, and other issues. Early on, Modeling to Generate Alternatives (MGA) formalized near-optimal as performance within a tolerable deviation from the optimal objective function value and identified a few maximally different alternatives that addressed some unmodeled issues. This paper presents new stratified, Monte-Carlo Markov Chain sampling and parallel coordinate plotting tools that generate and communicate the structure and extent of the nearoptimal region to an optimization problem. Interactive plot controls allow users to explore region features of most interest. Controls also streamline the process to elicit unmodeled issues and update the model formulation in response to elicited issues. Use for an example, single-objective, linear water quality management problem at Echo Reservoir, Utah, identifies numerous and flexible practices to reduce the phosphorus load to the reservoir and maintain close-to-optimal performance. Flexibility is upheld by further interactive alternative generation, transforming the formulation into a multiobjective problem, and relaxing the tolerance parameter to expand the near-optimal region. Compared to MGA, the new blended tools generate more numerous alternatives faster, more fully show the near-optimal region, and help elicit a larger set of unmodeled issues.

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A Multicriteria Decision Support System for Competence-Driven Project Portfolio Selection

Cited by 52 publications

References 55 publications

The use of search‐based optimization techniques to schedule and staff software projects: an approach and an empirical study

The use of search‐based optimization techniques to schedule and staff software projects: an approach and an empirical study

Robust Optimization for the Discrete Time-Cost Tradeoff Problem with Cost Uncertainty

Blended near‐optimal alternative generation, visualization, and interaction for water resources decision making

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