Human resource allocation to multiple projects based on members’ expertise, group heterogeneity and social cohesion

Project managers regularly allocate human resources to construction projects. This critical task is usually executed by fulfilling the minimum project staffing requirements normally based around the quantity and competence of project members. However, research has shown that team performance can increase by up to 10% and 18%, respectively, as a consequence of the group members’ heterogeneity and social cohesion. Also, there is currently no practical quantitative tool which incorporates these aspects to allow project managers to achieve this task efficiently and objectively. A new quantitative model for the effective allocation of human resources to multiple projects, which takes into account group heterogeneity and social cohesion is proposed. This model is easy to build, update and use in real project environments with the use of a spreadsheet and a basic optimization engine (e.g. Excel Solver). A case study is proposed and solved with a Genetic Algorithm to illustrate the model implementation. Finally, a validation example is provided to exemplify how group heterogeneity and social cohesion condition academic achievement in an academic setting

Stability and accuracy of deterministic project duration forecasting methods in earned value management

[EN] Purpose Earned Value Management (EVM) is a project monitoring and control technique that enables the forecasting of a project's duration. Many EVM metrics and project duration forecasting methods have been proposed. However, very few studies have compared their accuracy and stability. Design/methodology/approach This paper presents an exhaustive stability and accuracy analysis of 27 deterministic EVM project duration forecasting methods. Stability is measured via Pearson's, Spearman's and Kendall's correlation coefficients while accuracy is measured by Mean Squared and Mean Absolute Percentage Errors. These parameters are determined at ten percentile intervals to track a given project's progress across 4,100 artificial project networks with varied topologies. Findings Findings support that stability and accuracy are inversely correlated for most forecasting methods, and also suggest that both significantly worsen as project networks become increasingly parallel. However, the AT + PD-ESmin forecasting method stands out as being the most accurate and reliable. Practical implications Implications of this study will allow construction project managers to resort to the simplest, most accurate and most stable EVM metrics when forecasting project duration. They will also be able to anticipate how the project topology (i.e., the network of activity predecessors) and the stage of project progress can condition their accuracy and stability. Originality/value Unlike previous research comparing EVM forecasting methods, this one includes all deterministic methods (classical and recent alike) and measures their performance in accordance with several parameters. Activity durations and costs are also modelled akin to those of construction projects.The first author acknowledges the University of Talca for his Doctoral Program Scholarship (RU-056-2019). The second author acknowledges the Spanish Ministry of Science and Innovation for his Ramon y Cajal contract (RYC-2017-22222) co-funded by the European Social Fund.Barrientos-Orellana, A.; Ballesteros-Pérez, P.; Mora-Melià, D.; González-Cruz, M.; Vanhoucke, M. (2022). Stability and accuracy of deterministic project duration forecasting methods in earned value management. Engineering, Construction and Architectural Management. 29(3):1449-1469. https://doi.org/10.1108/ECAM-12-2020-10451449146929

Earned Schedule min-max: Two new EVM metrics for monitoring and controlling projects

[EN] Earned Value Management (EVM) is a well-known project management technique for monitoring project pro-gress. Over the last 15 years, many promising EVM metrics have been proposed to get, among other improve-ments, better actual project duration and cost estimates. Papers comparing the performance of all these metricsare, however, scarce and sometimes contradictory.In this paper, a simulation and empirical comparison of 26 deterministic project duration forecasting tech-niques under the EVM framework is developed. Among them, two new metrics: Earned Schedule min (ESmin) andEarned Schedule max (ESmax) are proposed.ESminandESmaxoffer a new and simpler activity-level calculationapproach of the traditional Earned Schedule metric. Top performing (most accurate) metrics: Earned Schedule(ES), Earned Duration (ED) and Effective Earned Schedule (ES(e)) with Performance Factor 1 (PF= 1), areslightly outperformed by the new metrics which also offer some interesting applications for enhanced projectcontrol.The first author acknowledges the Spanish Ministry of Science, Innovation and Universities for his Ramon y Cajal contract (RYC-2017-22222) co -funded by the European Social Fund. This work was also supported by the second author's "Estancias de movilidad en el extranjero Jose Castillejo para jovenes doctores, 2017 (grant ref. CAS17/00488)" and the fourth author's "Estancias de profesores e investigadores senior en centros extranjeros, incluido el programa "Salvador de Madariaga" 2018 (grant ref. PRX18/00381)", both also from the Spanish Ministry of Science, Innovation and UniversitiesBallesteros Pérez, P.; Sanz-Ablanedo, E.; Mora-Melià, D.; González-Cruz, M.; Fuentes Bargues, JL.; Pellicer, E. (2019). Earned Schedule min-max: Two new EVM metrics for monitoring and controlling projects. Automation in Construction. 103:279-290. https://doi.org/10.1016/j.autcon.2019.03.016S27929010

Weather-wise: a weather-aware planning tool for improving construction productivity and dealing with claims

The influence of unforeseen, extreme weather in construction works usually impacts productivity, causes significant project delays and constitutes a frequent source of contractor’s claims. However, construction practitioners cannot count on sound methods for mediating when weather-related claims arise, nor harnessing the influence of weather variability in construction projects. Building on the few most recent quantitative studies identifying those key weather agents and levels of intensity that affect some standard building construction activities, a new stochastic model that processes and replicates the spatio-temporal variability of combined weather variables is proposed. This model can help anticipate weather-related project duration variability; improving construction productivity by selecting the best project start date; and objectively evaluating weather-related claims. A two-building construction case study using different Spanish locations is used to demonstrate the model. The results showed that ignoring the influence of weather can lead to an extension of 5-20% longer project duration compared to planned

Efficiency Criteria as a Solution to the Uncertainty in the Choice of Population Size in Population-Based Algorithms Applied to Water Network Optimization

[EN] Different Population-based Algorithms (PbAs) have been used in recent years to solve all types of optimization problems related to water resource issues. However, the performances of these techniques depend heavily on correctly setting some specific parameters that guide the search for solutions. The initial random population size P is the only parameter common to all PbAs, but this parameter has received little attention from researchers. This paper explores P behaviour in a pipe-sizing problem considering both quality and speed criteria. To relate both concepts, this study applies a method based on an efficiency ratio E. First, specific parameters in each algorithm are calibrated with a fixed P. Second, specific parameters remain fixed, and the initial population size P is modified. After more than 600,000 simulations, the influence of P on obtaining successful solutions is statistically analysed. The proposed methodology is applied to four well-known benchmark networks and four different algorithms. The main conclusion of this study is that using a small population size is more efficient above a certain minimum size. Moreover, the results ensure optimal parameter calibration in each algorithm, and they can be used to select the most appropriate algorithm depending on the complexity of the problem and the goal of optimization.This study was supported by the Program Initiation into research (Project 11140128) of the Comision Nacional de Investigacion Cientifica y Tecnologica (Conicyt), Chile.Mora Meliá, D.; Gutiérrez Bahamondes, JH.; Iglesias Rey, PL.; Martínez-Solano, FJ. (2016). Efficiency Criteria as a Solution to the Uncertainty in the Choice of Population Size in Population-Based Algorithms Applied to Water Network Optimization. Water. 2016(8). https://doi.org/10.3390/w8120583S5832016

The multiple team formation problem using sociometry

The Team Formation problem (TFP) has become a well-known problem in the OR literature over the last few years. In this problem, the allocation of multiple individuals that match a required set of skills as a group must be chosen to maximise one or several social positive attributes. Specifically, the aim of the current research is two-fold. First, two new dimensions of the TFP are added by considering multiple projects and fractions of people's dedication. This new problem is named the Multiple Team Formation Problem (MTFP). Second, an optimization model consisting in a quadratic objective function, linear constraints and integer variables is proposed for the problem. The optimization model is solved by three algorithms: a Constraint Programming approach provided by a commercial solver, a Local Search heuristic and a Variable Neighbourhood Search metaheuristic. These three algorithms constitute the first attempt to solve the MTFP, being a variable neighbourhood local search metaheuristic the most efficient in almost all cases. Applications of this problem commonly appear in real-life situations, particularly with the current and ongoing development of social network analysis. Therefore, this work opens multiple paths for future research