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.

A custom pack combines medical disposable items into a single sterile package that is used for surgical procedures. Although custom packs are gaining importance in hospitals due to their potential benefits in reducing surgery setup times, little is known on methodologies to configure them, especially if the number of medical items, procedure types and surgeons is large. In this paper, we propose a mathematical programming approach to guide hospitals in developing or reconfiguring their custom packs. In particular, we are interested in minimising points of touch, which we define as a measure for physical contact between staff and medical materials. Starting from an integer non-linear programming model, we develop both an exact linear programming (LP) solution approach and an LP-based heuristic. Next, we also describe a simulated annealing approach to benchmark the mathematical programming methods. A computational experiment, based on real data of a medium-sized Belgian hospital, compares the optimised results with the performance of the hospital's current configuration settings and indicates how to improve future usage. Next to this base case, we introduce scenarios in which we examine to what extent the results are sensitive for waste, i.e. adding more items to the custom pack than is technically required for some of the custom pack's procedures, since this can increase its applicability towards other procedures. We point at some interesting insights that can be taken up by the hospital management to guide the configuration and accompanying negotiation processes.

In this paper, an overview is presented of the existing metaheuristic solution procedures to solve the multi-mode resource-constrained-project scheduling problem, in which multiple execution modes are available for each of the activities of the project. A fair comparison is made between the different metaheuristic algorithms on the existing benchmark datasets and on a newly generated dataset. Computational results are provided and recommendations for future research are formulated.

In recent years, a variety of novel approaches for fulfilling the important management task of accurately forecasting project duration have been proposed, with many of them based on the earned value management (EVM) methodology. However, these state-of-the-art approaches have often not been adequately tested on a large database, nor has their validity been empirically proven. Therefore, we evaluate the accuracy and timeliness of three promising deterministic techniques and their mutual combinations on a real-life project database. More specifically, two techniques respectively integrate rework and activity sensitivity in EVM time forecasting as extensions, while a third innovatively calculates schedule performance from time-based metrics and is appropriately called earned duration management or EDM(t). The results indicate that all three of the considered techniques are relevant. More concretely, the two EVM extensions exhibit accuracy-enhancing power for different applications, while EDM(t) performs very similar to the best EVM methods and shows potential to improve them.

Learning effects assume that the efficiency of a resource increases with the duration of a task. Although these effects are commonly used in machine scheduling environments, they are rarely used in a project scheduling setting. In this paper, the effect of learning in a project scheduling environment is studied and applied to the discrete time/resource trade-off scheduling problem (DTRTP), where each activity has a fixed work content for which a set of execution modes (duration/resource requirement pairs) can be defined. Computational results emphasize the significant impact of learning effects on the project schedule, measure the margin of error made by ignoring learning and show that timely incorporation of learning effects can lead to significant makespan improvements.

Different statistical process control (SPC) approaches were proposed over the years for project management using earned value management/earned schedule. A detailed examination of these approaches has led us to express a need for a unified framework in which to test and compare them. The main drivers for this need were the lack of a formal definition for a state of control, the unavailability of a benchmark dataset, the absence of measures to quantify the SPC performance and the lack of consensus on how to overcome and test the normality assumption. In this paper, we present such a framework that combines a classification from empirical data, a known project dataset, a sound simulation model and two quantitative measures for project control efficiency. Four SPC approaches from prior literature have been implemented and an exhaustive experiment was set up to compare and to discuss their value for the project management practice.

The timely completion of a project is one of its main factors for success. During the scheduling phase, a project buffer can be installed to protect the project deadline. During the execution phase, tolerance limits that generate warning signals when the project deadline is endangered should be constructed to monitor the buffer consumption. These tolerance limits will be constructed for the dynamic progress data provided by the Earned Value Management/Earned schedule methodology (EVM/ES). In this paper, we incorporate information on the availability of scarce resources into the construction of analytical tolerance limits for EVM/ES, in order to improve the efficiency and reliability of these tolerance limits. In order to review the performance of the limits, a computational experiment has been carried out in which they are compared to analytical tolerance limits that disregard the availability of resources. Results have shown that the performance of analytical tolerance limits can be significantly enhanced by incorporating the available resource information.

This paper presents an overview of the existing literature on project control and earned value management (EVM), aiming at fulfilling three ambitions. First, the journal selection procedure allows to discern between high-quality journals and more popular business magazines. Second, the collected papers on project control and EVM, published in the selected journals, are classified based on a framework consisting of six distinct classes. Third, the classification framework indicates current trends and potential areas for future research, which can be summarized as follows: (i) increased attention to the stochastic nature of projects, (ii) enhanced validation of the proposed methodology using a large historical dataset or a simulation experiment, (iii) expansion of integrated control models, focusing on time and cost as well as other factors such as quality and sustainability, and (iv) development and validation of corrective action procedures.