Selecting projects in a portfolio using risk and ranking

There are three dimensions in project management: time, cost and performance. Risk is a characteristic related to the previous dimensions and their relationships. A risk equation is proposed based on the nature of the uncertainty associated to each dimension as well as the relationship between the uncertainties. A ranking equation that is able to prioritise projects is proposed and discussed. The problem solved here is which projects to select in a given portfolio of projects. The model is implemented in a group decision support system (GDSS) which can guide decisionmakers in their decision process. However, the system is not intended as a substitution of the decisionmaker task, but merely as an aid. The methodology used is analysis of the equations proposed and trial and error based on examples. This paper’s main contribution is the risk equation and the ranking equation

A bi-objective genetic algorithm approach to risk mitigation in project scheduling

A problem of risk mitigation in project scheduling is formulated as a bi-objective optimization problem, where the expected makespan and the expected total cost are both to be minimized. The expected total cost is the sum of four cost components: overhead cost, activity execution cost, cost of reducing risks and penalty cost for tardiness. Risks for activities are predefined. For each risk at an activity, various levels are defined, which correspond to the results of different preventive measures. Only those risks with a probable impact on the duration of the related activity are considered here. Impacts of risks are not only accounted for through the expected makespan but are also translated into cost and thus have an impact on the expected total cost. An MIP model and a heuristic solution approach based on genetic algorithms (GAs) is proposed. The experiments conducted indicate that GAs provide a fast and effective solution approach to the problem. For smaller problems, the results obtained by the GA are very good. For larger problems, there is room for improvement

The use of buffers in project management: the trade-off between stability and makespan.

During execution, projects may be subject to considerable uncertainty, which may lead to numerous schedule disruptions. Recent research efforts have focused on the generation of robust project baseline schedules that are protected against possible disruptions that may occur during schedule execution. The fundamental research issue we address in this paper is the potential trade-off between the quality robustness (measured in terms of project duration) and solution robustness (stability, measured in terms of the deviation between the planned and realised start times of the projected schedule). We provide an extensive analysis of the results of a simulation experiment set up to investigate whether it is beneficial to concentrate safety time in project and feeding buffers, or whether it is preferable to insert time buffers that are scattered in a clever way throughout the baseline project schedule in order to maximize schedule stability.Management; Project management; Project scheduling; Quality; Quality robustness; Robustness; Schedule stability; Scheduling; Simulation; Stability; Time; Uncertainty;

The trade-off between stability and makespan in resource-constrained project scheduling.

During the last decade a lot of research efforts in the project scheduling literature have concentrated on resource-constrained project scheduling under uncertainty. Most of this research focuses on protecting the project due date against disruptions during execution. Few efforts have been made to protect the starting times of intermediate activities. In this paper, we develop a heuristic algorithm for minimizing a stability cost function (weighted sum of deviations between planned and realized activity starting times). The algorithm basically proposes a clever way to add intermediate buffers to a minimal duration resource-constrained project schedule. We provide an extensive simulation experiment to investigate the trade-off between quality robustness (measured in terms of project duration) and solution robustness(stability). We address the issue whether to concentrate safety time in so-called project and feeding buffers in order to protect the planned project completion time or to scatter safety time throughout the baseline schedule in order to enhance stability.Stability; Project scheduling; Scheduling; Research; Uncertainty; Time; Heuristic; Simulation; Quality; Quality robustness; Robustness; Order; Scatter;

The trade-off between stability and makespan in resource-constrained project scheduling.

During the last decade, considerable research efforts in the project scheduling literature have concentrated on resource-constrained project scheduling under uncertainty. Most of this research focuses on protecting the project due date against disruptions during execution. Few efforts have been made to protect the starting times of intermediate activities. In this paper, we develop a heuristic algorithm for minimizing a stability cost function (weighted sum of deviations between planned and realized activity starting times). The algorithm basically proposes a clever way to scatter time buffers throughout the baseline schedule. We provide an extensive simulation experiment to investigate the trade-off between quality robustness (measured in terms of project duration) and solution robustness (stability). We address the issue whether to concentrate safety time in so-called project and feeding buffers in order to protect the planned project completion time or to scatter safety time throughout the baseline schedule in order to enhance stability.Project management; Scheduling/sequencing; Simulation methods;

Solution and quality robust project scheduling: a methodological framework.

The vast majority of the research efforts in project scheduling over the past several years has concentrated on the development of exact and suboptimal procedures for the generation of a baseline schedule assuming complete information and a deterministic environment. During execution, however, projects may be the subject of considerable uncertainty, which may lead to numerous schedule disruptions. Predictive-reactive scheduling refers to the process where a baseline schedule is developed prior to the start of the project and updated if necessary during project execution. It is the objective of this paper to review possible procedures for the generation of proactive (robust) schedules, which are as well as possible protected against schedule disruptions, and for the deployment of reactive scheduling procedures that may be used to revise or re-optimize the baseline schedule when unexpected events occur. We also offer a methodological framework that should allow project management to identify the proper scheduling methodology for different project scheduling environments. Finally, we survey the basics of Critical Chain scheduling and indicate in which environments it is useful.Framework; Information; Management; Processes; Project management; Project scheduling; Project scheduling under uncertainty; Stability; Robust scheduling; Quality; Scheduling; Stability; Uncertainty;

On the construction of stable project baseline schedules.

The vast majority of project scheduling efforts assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. In reality, however, project activities are subject to considerable uncertainty, which generally leads to numerous schedule disruptions. It is of interest to develop pre-schedules that can absorb disruptions in activity durations without affecting the planning of other activities, such that co-ordination of resources and material procurement for each of the activities can be performed as smoothly as possible. The objective of this paper is to develop and evaluate various approaches for constructing a stable pre-schedule, which is unlikely to undergo major changes when it needs to be repaired as a reaction to minor activity duration disruptions.

Project scheduling under undertainty – survey and research potentials.

The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;

Robust scheduling and robustness measures for the discrete time/cost trade-off problem

Cataloged from PDF version of article.Projects are often subject to various sources of uncertainties that have a negative impact on activity durations and costs. Therefore, it is crucial to develop effective approaches to generate robust project schedules that are less vulnerable to disruptions caused by uncontrollable factors. In this paper, we investigate the robust discrete time/cost trade-off problem, which is a multi-mode project scheduling problem with important practical relevance. We introduce surrogate measures that aim at providing an accurate estimate of the schedule robustness. The pertinence of each proposed measure is assessed through computational experiments. Using the insights revealed by the computational study, we propose a two-stage robust scheduling algorithm. Finally, we provide evidence that the proposed approach can be extended to solve a complex robust problem with tardiness penalties and earliness revenues. 2010 Elsevier B.V. All rights reserved

Design and pilot run of fuzzy synthetic model (FSM) for risk evaluation in civil engineering

Most of the current construction risk assessment tools deliver unsatisfactory results because the prerequisite for their effective applications rely on the availability of high quality data especially during the early stage of a project. Unfortunately, such data are limited, ambiguous or even not exist due to the great uncertainty inherent in construction projects. Based on Fuzzy Synthetic Analysis (FSA), a model development team was formed among construction engineers, IT professionals, and Mathematicians in developing a holistic risk assessment model to estimate the construction risks especially for the situations with incomplete data and vague environments. Through qualitative scales defined by triangular fuzzy numbers used in pairwise comparisons to capture the vagueness in the linguistic variables, a risk assessment model using Analytic Hierarchy Process (AHP) was developed. The Pilot Run revealed the developed Fuzzy Synthetic Model (FSM) could accelerate the decision-making process and provide optimal allocation of project resources to mitigate possible risks detrimental to the success of a project in terms of time, cost, and quality