Solving a novel multi-divisional project portfolio selection and scheduling problem

A common problem faced by organizations is how to select and schedule an optimal portfolio of projects subject to various constraints, such as a limited budget. This problem is known as the project portfolio selection and scheduling problem (PPSSP). Despite the widespread nature of this problem, no existing model adequately addresses a sufficient set of characteristics that arise in real-world problems. One contribution of this article is the proposal of a novel, practical class of PPSSP that consists of multiple groups of projects, proposed by different sections of a major organization. The proposed problem can be considered as a generalized PPSSP given that many specific PPSSPs reported in the literature can be generated by relaxing certain constraints. As this is a novel formulation, existing algorithms cannot ensure high-quality solutions to this problem. Thus, a further contribution of this article is the design of three hybrid meta-heuristic algorithms based on a custom-purpose heuristic and local search operator. A case problem, inspired by future force design (FFD) in the Australian Defence Force (ADF), is presented to exemplify the applicability of this model to a real-world problem. Results indicate that the obtained solutions are of acceptable quality for implementation

GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion

This paper proposes a novel formulation of the project portfolio selection and scheduling problem inspired by the Future Defense Force Design process in the context of the Australian Defence Force capability development. The core objective of the problem is to maximize the total capability portfolio value attained by the selection and scheduling of a set of capability projects, grouped in various subsets referred to as capability options, while adhering to budgetary, scheduling, and operational constraints. To provide initial solutions to the proposed model, a custom heuristic is developed and used to seed an initial population for a Genetic Algorithm

2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020

Given a set of candidate projects, selecting and scheduling an optimal subset of the projects is a complex problem faced by many organizations. This problem is referred to as the Project Portfolio Selection and Scheduling Problem (PPSSP) and is known to be NP-hard. In the defence sector, the PPSSP arises as a sub-process of Future Force Design (FFD), which is a strategic planning task that assists in the decision making process for future defence force capability programming. The PPSSP faced in the defence context has its own set of challenges above and beyond those typical of the problem. As such, this study investigates a formulation of the PPSSP inspired by the FFD process in the context of the Australian Department of Defence (DoD). Given the NP-hard nature of the problem, four metaheuristics are examined on large-scale synthetic data sets. Three different solution representations are examined and results are compared against solutions provided by a commercial exact solver. Results indicate that there is no observed significant difference in total portfolio value attained by the proposed meta-heuristic approaches and the commercial solver, thereby justifying their usage in this domain

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Future force design is a crucial task that assists in the creation of an effective future defence force. The primary objective of this task is to select a set of projects, within a fixed planning window and subject to budgetary constraints, that will lead to improved capabilities. While inherently related to the well-known multi-period knapsack problem, addressing this problem in the context of the defence sector gives rise to a number of unique nuances and associated challenges. Furthermore, the literature pertaining to the selection and scheduling of projects for capability-based planning in the defence sector is rather limited. To address this literature gap, this paper formalizes a multi-period project selection and scheduling problem inspired by future force design. Numerous heuristics, both random and deterministic, along with a hybrid genetic algorithm, are employed to optimize a set of instances of the proposed problem formulation with various characteristics derived from real-world, public defence data made available by the Australian Department of Defence

A Hybrid Multi-Population Approach to the Project Portfolio Selection and Scheduling Problem for Future Force Design

Future Force Design (FFD) is a strategic planning activity that decides the programming of defence capability options. This is a complex problem faced by the Australian Department of Defence (DoD) and requires the simultaneous selection and scheduling of projects. Specifically, this is a NP-hard problem known as the Project Portfolio Selection and Scheduling Problem (PPSSP). While the PPSSP is a complex problem itself, its complexity is further increased when coupled with the additional characteristics that arise in the context of defence-oriented planning, such as long planning periods and complex operational constraints. As a result, many previous studies examined only a small number of projects over a short planning period and are largely unsuitable for the scale required in the defence sector. To address this issue, two primary contributions are made in this paper. Firstly, this study describes a complex practical PPSSP, inspired by the FFD process, and develops a corresponding mathematical model. Problem instances are derived from real-world, publicly-available defence data. Secondly, to address instances of the problem, two existing meta-heuristics are considered and a hybrid, multi-population approach is proposed. Results are compared against those attained by a commercial exact solver and indicate that there is no statistically significant difference in performance between the proposed multi-population approach and the exact solver. A key benefit of the proposed meta-heuristic approach is that its run time is not significantly influenced by the complexity of the problem instance. Additionally, many interesting practical insights regarding the solution of selection and scheduling problems are uncovered