Portfolio decision analysis for robust project selection and resource allocation

Organizations must take decisions on how to allocate resources to 'go/no-go' projects to maximize the value of their project portfolio. Often these decisions are complicated by several value criteria, multiple resource types and exogenous uncertainties that influence the projects' values. Especially when the number of projects is large, the efficiency of the resource allocation and the quality of the decision making process are likely to benefit from systematic use of portfolio decision analysis. This Dissertation develops and applies novel methods to manage uncertainty in decision analytic models for project portfolio selection. These methods capture incomplete information through sets of feasible model parameter values and use dominance relations to compare portfolios. Based on the computation of all non-dominated portfolios, these methods identify i) robust portfolios that perform well across the range of feasible parameter values and ii) projects that should surely be selected or rejected in the light of the incomplete information. These methods have several implications for project portfolio decision support. Explicit consideration of incomplete information contributes to the reliability of analysis, which is likely to increase the use of portfolio decision analysis in new contexts. Furthermore, cost and time savings in data elicitation may be achieved, because these methods can give robust decision recommendations based on incomplete data and identify projects for which additional information is beneficial. Finally, these methods support consensus building within organizations as different views about projects' quality or exogenous uncertainties can be considered simultaneously to identify projects on which further negotiations should be focused

A variable neighborhood search simheuristic for project portfolio selection under uncertainty

With limited nancial resources, decision-makers in rms and governments face the task of selecting the best portfolio of projects to invest in. As the pool of project proposals increases and more realistic constraints are considered, the problem becomes NP-hard. Thus, metaheuristics have been employed for solving large instances of the project portfolio selection problem (PPSP). However, most of the existing works do not account for uncertainty. This paper contributes to close this gap by analyzing a stochastic version of the PPSP: the goal is to maximize the expected net present value of the inversion, while considering random cash ows and discount rates in future periods, as well as a rich set of constraints including the maximum risk allowed. To solve this stochastic PPSP, a simulation-optimization algorithm is introduced. Our approach integrates a variable neighborhood search metaheuristic with Monte Carlo simulation. A series of computational experiments contribute to validate our approach and illustrate how the solutions vary as the level of uncertainty increases

IT/IS Project Portfolio Selection in the Presence of Project Interactions – Review and Synthesis of the Literature

Adequately considering interactions among IT/IS projectsin the process of constructing an IT/IS project portfoliois an important requirement for value-based IT/IS projectportfolio selection. A lot of articles already deal with modelingapproaches to incorporate such interactions, but theliterature lacks a common terminology and a structured perspectiveon the manifold types of interactions and their effects.When applied in business practice, this may lead toa systematically wrong project portfolio selection. Basedon a comprehensive literature review, our contributions are(1) an identication of relevant classication dimensions ofIT/IS project portfolio selection, (2) the development of aframework that provides a structured perspective on deterministic,intratemporal interactions, and { as the main contribution{ (3) a unication of the terminology and the semanticsof interactions among IT/IS projects. This workshall support decision-makers in the identication of possibleinteractions among IT/IS project proposals

Scenario-based portfolio model for building robust and proactive strategies

In order to address major changes in the operational environment, companies can (i) define scenarios that characterize different alternatives for this environment, (ii) assign probabilities to these scenarios, (iii) evaluate the performance of strategic actions across the scenarios, and (iv) choose those actions that are expected to perform best. In this paper, we develop a portfolio model to support the selection of such strategic actions when the information about scenario probabilities is possibly incomplete and may depend on the selected actions. This model helps build a strategy that is robust in that it performs relatively well in view of all available probability information, and proactive in that it can help steer the future as reflected by the scenarios toward the desired direction. We also report a case study in which the model helped a group of Nordic, globally operating steel and engineering companies build a platform ecosystem strategy that accounts for uncertainties related to markets, politics, and technological development

A Proposed Selection Process in Over-The-Top Project Portfolio Management

Purpose: The purpose of this paper is to propose an over-the-top (OTT) initiative selection process for communication service providers (CSPs) entering an OTT business. Design/methodology/approach: To achieve this objective, a literature review was conducted to comprehend the past and current practices of the project (or initiative) selection process as mainly suggested in project portfolio management (PPM). This literature was compared with specific situations and the needs of CSPs when constructing an OTT portfolio. Based on the contrast between the conventional project selection process and specific OTT characteristics, a different selection process is developed and tested using group model-building (GMB), which involved an in-depth interview, a questionnaire and a focus group discussion (FGD). Findings: The paper recommends five distinct steps for CSPs to construct an OTT initiative portfolio: candidate list of OTT initiatives, interdependency diagram, evaluation of all interdependent OTT initiatives, evaluation of all non-interdependent OTT initiatives and optimal portfolio of OTT initiatives. Research limitations/implications: The research is empirical, and various OTT services are implemented; the conclusion is derived only from one CSP, which operates as a group. Generalization of this approach will require further empirical tests on different CSPs, OTT players or any firms performing portfolio selection with a degree of interdependency among the projects. Practical implications: Having considered interdependency, the proposed OTT initiative selection steps can be further implemented by portfolio managers for more effective OTT initiative portfolio construction. Originality/value: While the previous literature and common practices suggest ensuring the benefits (mainly financial) of individual projects, this research accords higher priority to the success of the overall OTT initiative portfolio and recommends that an evaluation of the overall portfolio should occur prior to individual evaluation. Consequently, certain initiatives may not provide direct individual benefits. Those initiatives should remain within the portfolio because they are needed for the success of other initiatives.Peer Reviewe

Case: Peatland Selection

The importance of environmental decision making is growing. Private companies and public organizations are facing decisions involving multiple objectives. In particular, focusing solely on financial objectives is no longer enough but taking into account the environmental, social and political objectives is needed. The methods used to solve these environmental problems have been based on heuristic approaches. However, these methods lack the capability to provide optimal solutions as most of the environmental decisions are portfolio selection problems. Robust Portfolio Modeling (RPM) is a decision analysis method that combines mathematical optimization in portfolio selection to incomplete preference information. This incomplete information is common in environmental decision making which includes multiple stakeholders with conflicting views. However, RPM has not been applied before to real-life environmental cases. This thesis will first explore the characteristics of environmental decision making, secondly go through different methods used in environmental decision making and finally apply RPM methodology into peatland selection case. The results of RPM are then compared to the results of the heuristic YODA method previously used in the same peatland selection case. Results indicate that RPM and YODA select highly different type of peatlands. RPM takes better into account the cumulative effects related to portfolio selection than YODA. Therefore, it is argued that RPM might be suitable for environmental decision making

Managing project interdependencies in IT/IS project portfolios: a review of managerial issues

Adequately managing project interdependencies among diverse and simultaneous projects is deemed critical for successful implementation of project portfolios. The challenge is significant because it may entail managing a complex network of project interdependencies that keeps changing over time. The present study investigates the managerial challenges that may undermine effective management of project interdependencies in IT/IS project portfolios. The investigation is based on evidence from reviewing relevant literature and documented studies associated with managing project interdependencies. The main contribution of this study is to discuss three managerial challenges of project interdependencies in project portfolios. We discuss the challenges from three perspectives: types of interdependencies; patterns of interaction in interdependencies; and cost/benefit impact of project interdependencies

Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection

A multiobjective binary integer programming model for R&D project portfolio selection with competing objectives is developed when problem coefficients in both objective functions and constraints are uncertain. Robust optimization is used in dealing with uncertainty while an interactive procedure is used in making tradeoffs among the multiple objectives. Robust nondominated solutions are generated by solving the linearized counterpart of the robust augmented weighted Tchebycheff programs. A decision maker’s most preferred solution is identified in the interactive robust weighted Tchebycheff procedure by progressively eliciting and incorporating the decision maker’s preference information into the solution process. An example is presented to illustrate the solution approach and performance. The developed approach can also be applied to general multiobjective mixed integer programming problems

Proposing a Hybrid Approach to Predict, Schedule and Select the Most Robust Project Portfolio under Uncertainty

Suitable project portfolio selection in inconsistent economy that can reduce the portfolio risks and increasing utilities for investors has gained significant research attentions.   This article addresses the project portfolio selection in which conventional certain (1) prediction, (2) optimization and (3) clustering approaches cannot be used to face uncertainty. To predict the real value of affecting project risk parameters, neural network has been used; Then to determine the optimized sequences and procedures, the proposed model have been evaluated using the multi-objective shuffle frog leaping algorithm (SFLA) by robust optimization approach; To suggest different risk criteria, K-means algorithm utilized to categorize the candidate projects and differentiating the clusters.  As the proposed hybrid methodology is studied on 420 different construction projects in an Iranian construction company in two economic stable years and an instable year in Iran real estate market. The results show 96 percent prediction-optimization capability due to different desired criteria

A System-of-Systems Approach to Enterprise Analytics Design: Acquisition Support in the Age of Machine Learning and Artificial Intelligence

Acquisition Research Program Sponsored Report SeriesSponsored Acquisition Research & Technical ReportsSystem-of-Systems (SoS) capability emerges from the collaboration of multiple systems, which are acquired from independent organizations. Even though the systems contribute to and benefit from the larger SoS, the data analytics and decision-making about the independent system is rarely shared across the SoS stakeholders. The objective of this work is to identify how the sharing of datasets and the corresponding analytics among SoS stakeholders can lead to an improved SoS capability. Our objective is to characterize how the sharing of connected data sets may lead to deployment of different predictive (predicting an outcome from data) and prescriptive (determining a preferred strategy) analytics and lead to better decision outcomes at the SoS level. We build and demonstrate a framework for this objective based on extensive literature review and generating appropriate predictive and prescriptive methodologies that can be used for SoS analysis: Additionally, we propose to utilize machine learning techniques to predict the SoS capability achievable by sharing pertinent datasets and to prescribe the information links between systems to enable this sharing. Two case studies demonstrate the use of the framework and prospects for meeting the objective. Highlights of our study are summarized next.Approved for public release; distribution is unlimited.Approved for public release; distribution is unlimited