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The resource-constrained project scheduling problem is a widely studied problem in the literature. The goal is to construct a schedule for a set of activities, such that precedence and resource constraints are respected and that an objective function is optimized. In project scheduling literature, summary measures are often used as a tool to evaluate the performance of algorithms and to analyze instances and datasets. They can be classified in two groups, network measures describe the precedence constraints of a project, while resource measures focus on the resource constraints of the instance. In this manuscript we make an exhaustive evaluation of the summary measures for project scheduling. We provide an overview of the most prevalent measures and also introduce some new ones. For our tests we combine different datasets from the literature and generate a new set with diverse characteristics. We evaluate the performance of the summary measures on three dimensions: consistency, instance complexity and algorithm selection. We conclude by providing an overview of which measures are best suited for each of the three investigated dimensions. [ABSTRACT FROM AUTHOR]

Forecasting an ongoing project's actual duration is an essential aspect of project management which received considerable attention in the research community. In studies using Earned Value Management forecasting, it has been argued that the network topology is a driver to indicate the accuracy of these forecasts. However, a new project indicator has been recently defined, i.e. the project regularity, which reflects the value accrue according to the plan. It has shown to outperform the serial/parallel network topology indicator in specifying the accuracy of project forecasts. This paper introduces a novel way to define the project regularity, which provides project managers with an improved indication of the expected forecasting accuracy for their projects. The study is carried out on an empirical database consisting of 100 projects from different sectors, and the results are compared to the academic literature. The experiments show that the new indicator provides a better categorisation compared to the existing approaches. Further, they have shown that the ability of project categorisers to indicate the expected forecasting accuracy is affected by industry sector and project size. [ABSTRACT FROM AUTHOR]

The online version of the original article can be found at https://doi.org/10.1007/s10479-023-05470-8.Annals of Operations Researchhttps://doi.org/10.1007/s10479-023-05470-8Due to proofing error during typesetting Figs. 3, 4 and 5 were left blank, published with black bar and has now been updated.Original article has been updated.Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.By Rob Van Eynde; Mario Vanhoucke and José CoelhoReported by Author; Author; Author [Extracted from the article]

Budget allocation in project risk response is a vital issue in project risk management since it relates to a reasonable utilization of strict project budgets and effective mitigations of the risks that typify projects. This paper presents an integrated method based on an optimization model and fault tree analysis for allocating a risk response budget from a preventive and protective perspective. The proposed method consists of three main steps. The first step is to analyse and calculate risk probabilities and risk losses which involves identifying risk causes that may trigger a risk event to occur using fault tree analysis. It also identifies consequences once the risk event occurs, evaluates the occurrence probabilities of risk causes and expected financial losses of consequences. The second step is to build a relationship between the budget allocated to risk response strategies and the corresponding response effects. The third step is to construct an optimization model aiming at minimizing the total risk cost. We present proofs for the optimal risk response strategy in special cases of the budget allocation model. Furthermore, a detailed computational experiment is performed to gain insights into the three-phased budget allocation model for more general cases. The results show that an optimal risk response budget can be determined and the structure of the risk network has a significant impact on the preferred risk response strategy. [ABSTRACT FROM AUTHOR]

The goal of project control is monitoring the project progress during project execution to detect potential problems and taking corrective actions when necessary. Tolerance limits are a tool to assess whether the project progress is acceptable or not, and generate warnings signals that act as triggers for corrective action to the project manager. In this paper, three distinct types of tolerance limits that have been proposed in literature are validated on a large and diverse set of real-life projects mainly situated in the construction sector. Moreover, a novel approach to construct tolerance limits that integrate the project risk information into the monitoring process is introduced. The results of the empirical experiment have shown that integrating project-specific information into the construction of the tolerance limits results in a higher efficiency of the monitoring process. More specifically, while including cost information increases the efficiency only marginally, incorporating the available resource information substantially improves the efficiency of the monitoring process. Furthermore, when projects are not restricted by scarce resources, the efficiency can be enhanced by integrating the available project risk information. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent years, machine learning techniques, especially genetic programming (GP), have been a powerful approach for automated design of the priority rule-heuristics for the resource-constrained project scheduling problem (RCPSP). However, it requires intensive computing effort, carefully selected training data and appropriate assessment criteria. This research proposes a GP hyper-heuristic method with a duplicate removal technique to create new priority rules that outperform the traditional rules. The experiments have verified the efficiency of the proposed algorithm as compared to the standard GP approach. Furthermore, the impact of the training data selection and fitness evaluation have also been investigated. The results show that a compact training set can provide good output and existing evaluation methods are all usable for evolving efficient priority rules. The priority rules designed by the proposed approach are tested on extensive existing datasets and newly generated large projects with more than 1,000 activities. In order to achieve better performance on small-sized projects, we also develop a method to combine rules as efficient ensembles. Computational comparisons between GP-designed rules and traditional priority rules indicate the superiority and generalization capability of the proposed GP algorithm in solving the RCPSP. • An efficient genetic programming approach for the RCPSP is proposed. • The impact of the training data and evaluation function are investigated. • The GP-designed rules outperform any single traditional rule on various test data. • The GP-designed rules outperform the traditional rules method on large projects. • Three decision models are created to improve the performance on small-sized projects. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Simulation has played an important role in project-management studies of the last decades, but in order for them to produce practical results, a realistic distribution model for activity durations is indispensable. The construction industry often has needed historical records of project executions, to serve as inputs to the distribution models, but a clearly outlined calibration procedure is not always readily available, nor are their results readily interpretable. This study seeks to illustrate how data from the construction industry can be used to derive realistic input distributions. Therefore, the Parkinson simulation model with a lognormal core is applied to a large empirical dataset from the literature and the results are described. From a discussion of these results, an empirical classification of project executions is presented. Three possible uses are presented for the calibration procedure and the classification in project management simulation studies. These were validated using a case study of a construction company. [ABSTRACT FROM AUTHOR]

The ability to accurately forecast a project's final duration and cost is essential to successful project management. The technique of earned value management (EVM) is considered to provide an effective methodology for obtaining such forecasts; however, this has not yet been adequately tested on empirical data. Therefore, the accuracy of the most commonly used EVM time and cost forecasting methods is evaluated on a diverse and qualitative database consisting of 51 real-life projects. As most projects originate from the construction industry, an explicit focus on these construction projects is provided. Moreover, the desired real forecasting outcomes based on the actual project progress data are also supported by a Monte Carlo simulation study. It is demonstrated that highly accurate time and cost forecasts can be obtained by applying the EVM methodology. Furthermore, the best performing forecasting methods for the projects in the considered database are identified, also taking into account timeliness and the influence of the project network structure. [ABSTRACT FROM AUTHOR]

Recent literature on project management has emphasised the effort which is spent by the management team during the project control process. Based on this effort, a functional distinction can be made between a top down and a bottom up project control approach. A top down control approach refers to the use of a project control system that generates project based performance metrics to give a general overview of the project performance. Actions are triggered based on these general performance metrics, which need further investigation to detect problems at the activity level. A bottom up project control system refers to a system in which detailed activity information needs to be available constantly during the project control process, which requires more effort. In this research, we propose two new project control approaches, which combines elements of both top down and bottom up control. To this end, we integrate the earned value management/earned schedule (EVM/ES) method with multiple control points inspired by critical chain/buffer management (CC/BM). We show how the EVM/ES control approach is complementary with the concept of buffers and how they can improve the project control process when cleverly combined. These combined top down approaches overcome some of the drawbacks of traditional EVM/ES mentioned in the literature, while minimally increasing the effort spent by the project manager. A large computational experiment is set up to test the approach against other control procedures within a broad range of simulated dynamic project progress situations. [ABSTRACT FROM AUTHOR]

Support Vector Machines are methods that stem from Artificial Intelligence and attempt to learn the relation between data inputs and one or multiple output values. However, the application of these methods has barely been explored in a project control context. In this paper, a forecasting analysis is presented that compares the proposed Support Vector Regression model with the best performing Earned Value and Earned Schedule methods. The parameters of the SVM are tuned using a cross-validation and grid search procedure, after which a large computational experiment is conducted. The results show that the Support Vector Machine Regression outperforms the currently available forecasting methods. Additionally, a robustness experiment has been set up to investigate the performance of the proposed method when the discrepancy between training and test set becomes larger. [ABSTRACT FROM AUTHOR]

The article examines the Integrated Physician and Surgery Scheduling Problem (IPSSP) method. It presents a mathematical model that combines the commonly used constraints and objectives from physician rostering and surgery scheduling problems. The experiment reveals a relative ranking of the most influential surgery and physician constraints, and serves as a guide to assist hospital managers in discussing contract negotiations with physicians.

This paper gives an overview of three simulation studies in dynamic project scheduling integrating baseline scheduling with risk analysis and project control. This integration is known in the literature as dynamic scheduling. An integrated project control method is presented using a project control simulation approach that combines the three topics into a single decision support system. The method makes use of Monte Carlo simulations and connects schedule risk analysis (SRA) with earned value management (EVM). A corrective action mechanism is added to the simulation model to measure the efficiency of two alternative project control methods. At the end of the paper, a summary of recent and state-of-the-art results is given, and directions for future research based on a new research study are presented. [ABSTRACT FROM AUTHOR]

In this paper, we present a hybrid genetic algorithm for the well-known nurse scheduling problem (NSP). The NSP involves the construction of roster schedules for nursing staff in order to maximize the quality of the roster schedule subject to various hard constraints. In the literature, several genetic algorithms have been proposed to solve the NSP under various assumptions. The contribution of this paper is twofold. First, we extensively compare the various crossover operators and test them on a standard dataset in a solitary approach. Second, we propose several options to hybridize the various crossover operators. [ABSTRACT FROM AUTHOR]

Repetitive projects involve the repetition of activities along the stages of the project. Since the resources required to perform these activities move from one stage to the other, a main objective of scheduling these projects is to maintain the continuity of work of these resources so as to minimize the idle time of resources. This requirement, often referred to as work continuity constraints, involves a tradeoff between total project duration and the resource idle time. The contribution of this paper is threefold. First, we provide an extensive literature summary of the topic under study. Although most research papers deal with the scheduling of construction projects, we show that this can be extended to many other environments. Second, we propose an exact search procedure for scheduling repetitive projects with work continuity constraints. This algorithm iteratively shifts repeating activities further in time in order to decrease the resource idle time. We have embedded this recursive search procedure in a horizon-varying algorithm in order to detect the complete tradeoff profile between resource idle time and project duration. The procedure has been coded in Visual C++ and has been validated on a randomly generated problem set. Finally, we illustrate the concepts on three examples. First, the use of our new algorithm is illustrated on a small fictive problem example from literature. In a second example, we show that work continuity constraints involve a tradeoff between total project duration and the resource idle time. A last example describes the scheduling of a well-known real-life project that aims at the construction of a tunnel at the Westerschelde in The Netherlands. [ABSTRACT FROM AUTHOR]

Priority rules are applied in many commercial software tools for scheduling projects under limited resources because of their known advantages such as the ease of implementation, their intuitive working, and their fast speed. Moreover, while numerous research papers present comparison studies between different priority rules, managers often do not know which rules should be used for their specific project, and therefore have no other choice than selecting a priority rule at random and hope for the best. This paper introduces a decision tree approach to classify and detect the best performing priority rule for the resource-constrained project scheduling problem (RCPSP). The research relies on two classification models to map project indicators onto the performance of the priority rule. Using such models, the performance of each priority rule can be predicted, and these predictions are then used to automatically select the best performing priority rule for a specific project with known network and resource indicator values. A set of computational experiment is set up to evaluate the performance of the newly proposed classification models using the most well-known priority rules from the literature. The experiments compare the performance of multi-label classification models with multi-class classification models, and show that these models can outperform the average performance of using any single priority rule. It will be argued that this approach can be easily extended to any extension of the RCPSP without changing the methodology used in this study. • The decision tree is used to classify the best performing priority rule for RCPSP. • Two models are proposed to map indicators onto the performance of priority rules. • Three computational experiments evaluate the effectiveness of the models. • Both classification models outperform any single priority rule. [ABSTRACT FROM AUTHOR]

This article summarises the research studies published in the special issue on Project Management and Scheduling devoted to the 18th International Conference on Project Management and Scheduling (PMS). The special issue contains state-of-the art research in the field of (non-)robust project and machine scheduling and the contribution of each individual study to the academic literature are discussed. We notice that there is a growing interest in the research community to investigate robust scheduling approaches and optimisation problems observed in real-life business settings. This allows us to derive some interesting future research directions for the project and machine scheduling community. [ABSTRACT FROM AUTHOR]

Just like any physical system, projects have entropy that must be managed by spending energy. The entropy is the project's tendency to move to a state of disorder (schedule delays, cost overruns), and the energy process is an inherent part of any project management methodology. In order to manage the inherent uncertainty of these projects, accurate estimates (for durations, costs, resources, ...) are crucial to make informed decisions. Without these estimates, managers have to fall back to their own intuition and experience, which are undoubtedly crucial for making decisions, but are are often subject to biases and hard to quantify. This paper builds further on two published calibration methods that aim to extract data from real projects and calibrate them to better estimate the parameters for the probability distributions of activity durations. Both methods rely on the lognormal distribution model to estimate uncertainty in activity durations and perform a sequence of statistical hypothesis tests that take the possible presence of two human biases into account. Based on these two existing methods, a new so-called statistical partitioning heuristic is presented that integrates the best elements of the two methods to further improve the accuracy of estimating the distribution of activity duration uncertainty. A computational experiment has been carried out on an empirical database of 83 empirical projects. The experiment shows that the new statistical partitioning method performs at least as good as, and often better than, the two existing calibration methods. The improvement will allow a better quantification of the activity duration uncertainty, which will eventually lead to a better prediction of the project schedule and more realistic expectations about the project outcomes. Consequently, the project manager will be able to better cope with the inherent uncertainty (entropy) of projects with a minimum managerial effort (energy). [ABSTRACT FROM AUTHOR]

In this paper, we study the project staffing problem with discrete time/resource trade-offs to minimise the personnel staffing budget. This staffing problem embeds activity scheduling flexibility by incorporating the project scheduling problem into the personnel staffing problem to improve the quality of the staffing plan. In addition, we introduce extra demand scheduling flexibility resulting from the design of alternative execution modes for the activities, modelled via discrete time/resource trade-offs. In this way, the project manager is able to decide on the team size and duration for every activity. We propose a two-stage methodology to first design specific alternative activity modes using heuristic rules-of-thumb and subsequently we assess the resulting quality, i.e. the staffing cost, via the integrated composition of the project schedule and associated staffing plan. The heuristic mode generation rules determine the selection of a limited set of relevant activities and modes. The computational results show that the impact of these heuristic generation rules on the staffing budget is dependent on the defined relation between different activity alternatives for a particular activity and on the estimated characteristics of the activity base modes. We show that by focusing on a particular well-chosen subset of activity alternatives or on a particular subset of activities, high-quality solutions realising most of the potential cost improvements resulting from the discrete time/resource trade-offs can be derived with a reduced effort. [ABSTRACT FROM AUTHOR]

Abstract Since project control involves taking decisions that affect the future, the ability to accurately forecast the final duration and cost of projects is of major importance. In this paper, we focus on improving the accuracy of project duration forecasting by introducing a forecasting approach for Earned Value Management (EVM) and Earned Duration Management (EDM) that combines the schedule performance and schedule adherence of the project in progress. As the schedule adherence has not yet been defined formally for EDM, we extend the EVM-based measure of schedule adherence, the p-factor, to EDM and refer to this measure as the c-factor. Moreover, we aim to improve the ability to indicate the expected forecasting accuracy for a project by extending the EVM concept of project regularity to EDM. The introduced forecasting approach and the EDM project regularity indicator are applied to a large number of real-life projects, mainly situated in the construction sector. The conducted empirical experiment shows that the project duration forecasting accuracy can be increased by focusing on both the schedule performance and schedule adherence. Further, this study shows that the EDM project regularity indicator is indeed a more reliable indicator of forecasting accuracy. [ABSTRACT FROM AUTHOR]

In this paper, the authors focus on the stability of earned value management (EVM) forecasting methods. The contribution is threefold. First of all, a new criterion to measure stability that does not suffer from the disadvantages of the historically employed concept is proposed. Second, the stability of time and cost forecasting methods is compared and contrasted by means of a computational experiment on a topologically diverse data set. Throughout these experiments, the forecasting accuracy is reported as well, facilitating a trade-off between accuracy and stability. Finally, it is shown show that the novel stability metric can be used in practical environments using two real-life projects. The conclusions of this empirical validation are found to be largely in line with the computational results. [ABSTRACT FROM AUTHOR]

In this paper, a metaheuristic solution procedure for the Time-Constrained Project Scheduling Problem is proposed, in which additional resources can be temporarily allocated to meet a given deadline. The problem consists of determining a schedule such that the project is completed on time and that the total additional cost for the resources is minimized. For this problem, an artificial immune system is proposed, in which each solution is represented by a vector of activity start times. A local search procedure, which tries to shift cost causing activities, is applied to each population schedule. Computational experiments are applied to modified resource-constrained project scheduling problem benchmark instances and reveal promising results. [ABSTRACT FROM AUTHOR]

In Schedule Risk Analysis (SRA), activity sensitivity metrics measure the importance of activities in a project schedule. Highly sensitive activities are those more likely to increase project duration variability and/or cause project duration extensions. Several activity sensitivity metrics have been proposed over the years, but a comparison of all of them has never been made. This has made it difficult to know which metrics perform better and under what circumstances. In this paper, an extensive comparison of all relevant SRA activity sensitivity metrics is performed using a set of 4100 artificial projects. Unlike previous studies, the comparison framework is decoupled from corrective actions (e.g. activity crashing) which allows the merits of each metric to be assessed individually. Additionally, a new metric that performs better for overall sensitivity ranking is proposed. Results show that most sensitivity metrics do not perform well unless they are applied iteratively (the sensitivity of the remaining scheduled activities has to be recalculated whenever the duration variability of at least one activity has been restricted). However, if applied iteratively, most metrics can enhance project monitoring and control, while significantly shortening project duration. • A performance comparison of all relevant SRA metrics to date is presented. • A new SRA metric named Criticality-Slack-Sensitivity index (CSS) is proposed. • CSS is the top-performing metric in one-off calculation mode. • The Schedule Sensitivity Index (SSI) is the best metric in iterative calculation. • The duration of construction projects can be significantly shortened using SRA. [ABSTRACT FROM AUTHOR]