We present a novel optimisation approach for incentive contract design within a project setting. the structure of the remuneration is one of the key challenges faced by the project owner when (s)he decides to hire a contractor. The proposed technique builds on the recently proposed contract design methodology by Kerkhove and Vanhoucke (Omega, 2015). Specifically, a novel multi-objective scatter search heuristic is proposed and implemented using parallelisation. Both single- and multi-population implementations of this heuristic are compared to the original full-factorial approach as well as commercial optimisation software. The results of the computational experiments indicate that the single-population parallel scatter search procedure significantly outperforms the other solution strategies in terms of both speed and solution quality.

In this article, the Discrete Time/Cost Tradeoff Problem (DTCTP) is revisited in light of a student experiment. Two solution strategies are distilled from the data of 444 participants and are structured by means of five building blocks: focus, activity criticality, ranking, intensity, and action. The impact of complexity and uncertainty on the cost objective is quantified in a large computational experiment. Specific attention is allocated to the influence of the actual and perceived complexity and uncertainty and the cost repercussions when reality and perception do not coincide.

This manuscript focuses on the single- and multi-mode resource-constrained project scheduling problem with discounted cash flows (RCPSPDC and MRCPSPDC) and three payment models. The contribution of the paper is twofold. First, we extend a new scheduling technique, which moves activities in order to improve the project net present value. This more general version is applicable to multiple problem formulations and provides an overarching framework in which these models can be implemented. The changes in activity finish times take other activities and the possible changes in the finish times of these other activities into account, by forming a set of activities which is subsequently moved in time. The scheduling technique is implemented within a genetic algorithm metaheuristic and employs two penalty functions, one for deadline feasibility and one for non-renewable resource feasibility. Second, we test the proposed approach on several datasets from literature and illustrate the added value of each part of the algorithm. The influence of data parameters on the project net present value is highlighted. The detailed results provided in this paper can be used as future benchmarks for each of the six models discussed.

This paper presents five Artificial Intelligence (AI) methods to predict the final duration of a project. A methodology that involves Monte Carlo simulation, Principal Component Analysis and cross-validation is proposed and can be applied by academics and practitioners. The performance of the AI methods is assessed by means of a large and topologically diverse dataset and is benchmarked against the best performing Earned Value Management/Earned Schedule (EVM/ES) methods. The results show that the AI methods outperform the EVM/ES methods if the training and test sets are at least similar to one another. Additionally, the AI methods report excellent early and mid-stage forecasting results. A robustness experiment gradually increases the discrepancy between the training and test sets and demonstrates the limitations of the newly proposed AI methods.

This paper explores the use of multivariate regression methods for project schedule control within a statistical project control framework. These multivariate regression methods monitor the activity level performance of an ongoing project from the earned value management/earned schedule (EVM/ES) observations that are made at a high level of the work breakdown structure (WBS). These estimates can be used to calculate the longest path in the project and to produce warning signals for project schedule control. The effort that is spent by the project manager is thereby reduced, since a drill-down of the WBS is no longer required for every review period. An extensive computational experiment was set up to test and compare four distinct multivariate regression methods on a database of project networks. The kernel principal component regression method, when used with a radial base function kernel, was found to outperform the other presented regression methods.

In the integrated project scheduling and personnel staffing problem the project activities are scheduled and simultaneously a staffing plan is composed to carry out a single project. In this way, the project schedule that leads to the staffing plan with minimum cost is determined. In this paper, we evaluate different scheduling policies and practices for different personnel resource types. We examine the impact on the staffing cost when the personnel resources are scheduled in a cyclic versus a non-cyclic manner for different (days on, days off)-patterns. Furthermore, the impact of introducing more flexible resource types, such as overtime and temporary help, is explored in relationship with the activity resource demand variability. Computational results show that non-cyclic scheduling leads to a considerable lower staffing cost under all circumstances compared to cyclic scheduling. However, despite the tractability of the resource requirements, flexible temporary resources are essential on top of the regular personnel resources to respond to the variability in demand. The addition of overtime on a strategic staffing level only marginally decreases the personnel cost.

In this paper, an overview is given of the project data instances available in the literature to carry out academic research in the field of integrated project management and control. This research field aims at integrating static planning methods and risk analyses with dynamic project control methodologies using the state-of-the-art knowledge from literature and the best practices from the professional project management discipline. Various subtopics of this challenging discipline have been investigated from different angles, each time using project data available in literature, obtained from project data generators or based on a sample of empirical case studies. This paper gives an overall overview of the wide variety of project data that are available and are used in various research publications. It will be shown how the combination of artificial data and empirical data leads to improved knowledge on and deeper insights into the structure and characteristics of projects useful for academic research and professional use. While the artificial data can be best used to test novel ideas under a strict design in a controlled academic environment, empirical data can serve as the necessary validation step to translate the academic research results into practical ideas, aiming at narrowing the bridge between the theoretical knowledge and practical relevance.

This manuscript gives a brief overview of three sets of experiments in the classroom with students following a Project Management (PM) course module using a blended learning approach. The impact of communication on the student performance using business games as well as the advantages of the use of integrative case studies and their impact on the learning experience of these students are tested. The performance of students is measured by their quantitative output on the business game or case exercise, while their learning experience is measured by the student evaluations. The experiments have been carried out on a sample of students with a different background, ranging from university students with or without a strong quantitative background but no practical experience, to MBA students at business schools and PM professionals participating in a PM training. The results have been presented at an international workshop on computer supported education in Lisbon (Portugal) in 2015 and details have been published in Vanhoucke and Wauters (2015).

Project scheduling takes the perspective of a single decision-maker, where the owner is directly involved in the management and control of the project. In this paper, we study the multi-mode project scheduling problem from an agency perspective, where agency arises from the risk-averse contractor’s hidden effort, which influences the duration and cost of project activities. We formulate the owner’s problem of determining the optimal parameters of a linear incentive contract as a bi-level program, where the lower level is a multi-mode project scheduling problem with time/cost trade-offs, representing the contractor’s problem. Two benchmarks for the optimal contract are formulated, providing bounds on the expected total cost to the owner, and performance measures are defined accordingly. A stylized model of the owner’s problem is presented and solved in closed form for the simple case where an aggregate effort can be exerted at the project level, with restrictive assumptions on the variance of noise and the cost of effort. However, when effort is exerted at the activity level, solving the owner’s problem involves the solution of the multi-mode project scheduling problem. A numerical study for such situation is presented, which illustrates the trade-off between incentives and risk, and the effect of this trade-off on different project related costs. The findings from the stylized model and those from the numerical results of the presented example are compared in a computational experiment on 15 randomly generated projects.