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A New Model for the Project Portfolio Selection and Scheduling Problem with Defence Capability Options

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Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 26))

Abstract

This chapter proposes a novel optimization model for the project portfolio selection and scheduling problem (PPSSP), as part of the future force design (FFD) process, in the context of the Australian Defence Force (ADF) capability development. The core objective of the problem addressed in this study is to maximize the total portfolio value attained by the selection and scheduling of a set of projects, grouped in various subsets referred to as capability options (COs), while adhering to budgetary, scheduling, and operational constraints. While many of the independent aspects of this problem have been considered in the literature, no existing problem formulation adequately captures all aspects. To solve the proposed model, a custom-built heuristic approach is first developed. Three meta-heuristic approaches are examined to improve upon the heuristic solutions. Experiments are conducted over a set of 108 synthetic problem instances, depicting a wide variety of problem characteristics.

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Notes

  1. 1.

    For the purposes of this study, the current year is taken as 2021.

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Acknowledgements

This work was supported by the Australian Department of Defence, Defence Science and Technology Project RG191353.

Author information

Authors and Affiliations

  1. University of New South Wales Canberra at the Australian Defence Force Academy, Canberra, Australia

    Kyle Robert Harrison, Saber M. Elsayed & Ruhul Amin Sarker

  2. Joint Operations and Analysis Division, Defence Science and Technology Group, Department of Defence, Canberra, Australia

    Ivan L. Garanovich, Terence Weir & Sharon G. Boswell

Authors
  1. Kyle Robert Harrison
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  2. Saber M. Elsayed
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  3. Ivan L. Garanovich
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  4. Terence Weir
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  5. Sharon G. Boswell
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Corresponding author

Correspondence to Kyle Robert Harrison .

Editor information

Editors and Affiliations

  1. UNSW Canberra, Canberra, ACT, Australia

    Kyle Robert Harrison

  2. UNSW Canberra, Canberra, ACT, Australia

    Saber Elsayed

  3. Department of Defence, Defence Science and Technology Group, Canberra, ACT, Australia

    Ivan Leonidovich Garanovich

  4. Department of Defence, Defence Science and Technology Group, Canberra, ACT, Australia

    Terence Weir

  5. Department of Defence, Defence Science and Technology Group, Canberra, ACT, Australia

    Sharon G. Boswell

  6. UNSW Canberra, Canberra, ACT, Australia

    Ruhul Amin Sarker

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Harrison, K.R., Elsayed, S.M., Garanovich, I.L., Weir, T., Boswell, S.G., Sarker, R.A. (2022). A New Model for the Project Portfolio Selection and Scheduling Problem with Defence Capability Options. In: Harrison, K.R., Elsayed, S., Garanovich, I.L., Weir, T., Boswell, S.G., Sarker, R.A. (eds) Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling. Adaptation, Learning, and Optimization, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-030-88315-7_5

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