Abbas Hassan, Khaled El-Rayes and Mohamed Attalla
This paper presents the development of a novel model for optimizing the scheduling of crew deployments in repetitive construction projects while considering uncertainty in crew…
Abstract
Purpose
This paper presents the development of a novel model for optimizing the scheduling of crew deployments in repetitive construction projects while considering uncertainty in crew production rates.
Design/methodology/approach
The model computations are performed in two modules: (1) simulation module that integrates Monte Carlo simulation and a resource-driven scheduling technique to calculate the earliest crew deployment dates for all activities that fully comply with crew work continuity while considering uncertainty; and (2) optimization module that utilizes genetic algorithms to search for and identify optimal crew deployment plans that provide optimal trade-offs between project duration and crew deployment plan cost.
Findings
A real-life example of street renovation is analyzed to illustrate the use of the model and demonstrate its capabilities in optimizing the stochastic scheduling of crew deployments in repetitive construction projects.
Originality/value
The original contribution of this research is creating a novel multiobjective stochastic scheduling optimization model for both serial and nonserial repetitive construction projects that is capable of identifying an optimal crew deployment plan that simultaneously minimizes project duration and crew deployment cost.
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Vahid Mohagheghi, Seyed Meysam Mousavi, Mohammad Mojtahedi and Sidney Newton
Project selection is a critical decision for any organization seeking to commission a large-scale construction project. Project selection is a complex multi-criteria…
Abstract
Purpose
Project selection is a critical decision for any organization seeking to commission a large-scale construction project. Project selection is a complex multi-criteria decision-making problem with significant uncertainty and high risks. Fuzzy set theory has been used to address various aspects of project uncertainty, but with key practical limitations. This study aims to develop and apply a novel Pythagorean fuzzy sets (PFSs) approach that overcomes these key limitations.
Design/methodology/approach
The study is particular to complex project selection in the context of increasing interest in resilience as a key project selection criterion. Project resilience is proposed and considered in the specific situation of a large-scale construction project selection case study. The case study develops and applies a PFS approach to manage project uncertainty. The case study is presented to demonstrate how PFS is applied to a practical problem of realistic complexity. Working through the case study highlights some of the key benefits of the PFS approach for practicing project managers and decision-makers in general.
Findings
The PFSs approach proposed in this study is shown to be scalable, efficient, generalizable and practical. The results confirm that the inclusion of last aggregation and last defuzzification avoids the potentially critical information loss and relative lack of transparency. Most especially, the developed PFS is able to accommodate and manage domain expert expressions of uncertainty that are realistic and practical.
Originality/value
The main novelty of this study is to address project resilience in the form of multi-criteria evaluation and decision-making under PFS uncertainty. The approach is defined mathematically and presented as a six-step approach to decision-making. The PFS approach is given to allow multiple domain experts to focus more clearly on accurate expressions of their agreement and disagreement. PFS is shown to be an important new direction in practical multi-criteria decision-making methods for the project management practitioner.
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The paper aims to provide a structured framework for comparing different productivity estimation methodologies and evaluate their sensitivity to operational coefficients variation…
Abstract
Purpose
The paper aims to provide a structured framework for comparing different productivity estimation methodologies and evaluate their sensitivity to operational coefficients variation for excavation operations.
Design/methodology/approach
Two process‐oriented methodologies were analysed in a deterministic fashion in terms of their input requirements and their respective outputs. A phase‐oriented framework was presented to enable their comparison. The research methodology allows the estimation of excavation productivity in relation to the selected operational coefficients.
Findings
The system productivity is significantly influenced by operational conditions, such as the digging depth and the swing angle from the excavation front to the dumping position. Each methodology presents a differing sensitivity to every operational factor. Since the excavator is considered as the system's leading resource, the variation on productivity has direct implications for the truck fleet size and the unit cost of operations.
Originality/value
The proposed approach is useful in analyzing process‐oriented productivity estimation methodologies under a given set of operational coefficients when no historical data is available. Thus, it provides an alternative to intuitive estimates based solely on personal judgment. The concept of “baseline reference” conditions is introduced, so as to enable the transformation of any operational scenario into equivalent mathematical models that allow comparisons between different estimation methodologies and computational approaches.
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The main objective of this research is to identify the most important human resource management (HRM) practices, which have the potential to enhance labour productivity using…
Abstract
Purpose
The main objective of this research is to identify the most important human resource management (HRM) practices, which have the potential to enhance labour productivity using fuzzy synthetic evaluation approach.
Design/methodology/approach
The study used a mixed-methods research design in which qualitative data were collected and analysed during Phase I and quantitative data were analysed during Phase II. Nineteen experts who have experience in building construction projects were involved in interviews conducted in Phase I. During Phase II, quantitative data were collected from contractors that were involved in the delivery of building projects using questionnaires and the data were analysed using FSE technique.
Findings
Clear delegation of responsibility, stability of organisational structure and crew composition are found to be the three most important HRM practices that can enhance productivity in building construction projects. The findings of the study showed that the overall importance index computed using the FSE model is 3.65 (≈ 4) with an equivalent linguistic term of “very important”. The study also suggested that the top three HRM practices should be implemented conjointly as there is no significant difference among their weights.
Originality/value
The output of this research can provide important information regarding the HRM practices in the Australian construction industry. Thus, international developers or contractors who want to do construction business in Australia can implement the essential HRM practices so that the productivity of their construction projects will not be affected negatively.
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Nnedinma Umeokafor and Abimbola Windapo
There are serious implications for adopting inappropriate research strategies and methods, and this is evident in the Built Environment (BE) given the under adoption of…
Abstract
Purpose
There are serious implications for adopting inappropriate research strategies and methods, and this is evident in the Built Environment (BE) given the under adoption of qualitative strategies in some countries. Therefore, based on empirical evidence from Nigeria, the purpose of this study is to examine the challenges to and opportunities for establishing Qualitative Approach (QA) to BE research in higher education institutions (HEIs) and to develop an improvement framework for QA.
Design/methodology/approach
Academics and research students in the BE research of Nigerian HEIs were interviewed and the data analysed thematically. Based on the findings, including recommendations from the respondents, a framework for improving the use of QA in BE research was developed and academics evaluated it for workability.
Findings
This study reveals that the challenges to QA in BE research include information constraints, socio-cultural issues and the negative attitudes of senior academics to QA. The opportunities include the realisation for a paradigm shift, the characteristics of the socio-cultural context and features of BE and the general potentials of QA. The proposed framework encompasses encouraging and providing a platform for international collaboration between academics in developing and developed countries, and preferential treatment for QA. It also enables regulatory and incentive mechanisms, which will act as drivers.
Practical implications
This study provides stakeholders in academia with knowledge and a detailed guideline for establishing QA to research in the BE.
Originality/value
This study provides a country context-based detailed guide for establishing QA in HEIs BE research towards ensuring that research strategies adopted in BE research are fit for purpose, in turn are aligned to addressing problems in the society. There is little or no study of this nature in BE.
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DAVID J. EDWARDS and SILAS YISA
Utilization of off‐highway vehicles forms an essential part of UK industry's efforts to augment the productivity of plant operations and reduce production costs. However…
Abstract
Utilization of off‐highway vehicles forms an essential part of UK industry's efforts to augment the productivity of plant operations and reduce production costs. However, uninterrupted utilization of plant and equipment is requisite to reaping the maximum benefit of mechanization; one particular problem being plant breakdown duration and its impact upon process productivity. Predicting the duration of plant downtime would enable plant managers to develop suitable contingency plans to reduce the impact of downtime. This paper presents a stochastic mathematical modelling methodology (more specifically, probability density function of random numbers) which predicts the probable magnitude of ‘the next’ breakdown, in terms of duration for tracked hydraulic excavators. A random sample of 33 machines was obtained from opencast mining contractors, containing 1070 observations of machine breakdown duration. Utilization of the random numbers technique will engender improved maintenance practice by providing a practical methodology for planning, scheduling and controlling future plant resource requirements. The paper concludes with direction for future research which aims to: extend the model's application to cover other industrial settings and plant items; to predict the time at which breakdown will occur (vis‐à‐vis the duration of breakdown); and apply the random numbers modelling to individual machine compartments.
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DAVID J. EDWARDS and GARY D. HOLT
Hydraulic excavator cycle time and associated unit costs of excavation for given input estimating data, for machines operating in the UK construction industry, are predicted…
Abstract
Hydraulic excavator cycle time and associated unit costs of excavation for given input estimating data, for machines operating in the UK construction industry, are predicted. Using multiple regression analysis, three variables are identified as accurate predictors of cycle time: machine weight, digging depth and machine swing angle. With a coefficient of determination (R2) of 0.88, a mean percentage error (MPE) of −5.49, and a mean absolute error (MAPE) of 3.67, the cycle time model is robust; this is further validated using chi‐square analysis and Pearson's correlation coefficient (on predicted and actual values of machine cycle time). An illustrative example of the model's application to determine machine productivity is given. The paper concludes with a spreadsheet model for calculating excavation costs (m3 and cost per h) which is able to deal with any combination of the three independent cycle time predictor variables and other estimator's input data.
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C.M. TAM, THOMAS K.L. TONG and SHARON L. TSE
This paper aims to develop a quantitative model for predicting the productivity of excavators using artificial neural networks (ANN), which is then compared with the multiple…
Abstract
This paper aims to develop a quantitative model for predicting the productivity of excavators using artificial neural networks (ANN), which is then compared with the multiple regression model developed by Edwards & Holt (2000). A neural network using the architecture of multilayer feedforward (MLFF) is used to model the productivity of excavators. Finally, the modelling methods, predictive behaviours and the advantages of each model are discussed. The results show that the ANN model is suitable for mapping the non‐linear relationship between excavation activities and the performance of excavators. It concludes that the ANN model is an ideal alternative for estimating the productivity of excavators.
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Lihui Zhang, Guyu Dai, Xin Zou and Jianxun Qi
Interrupting work continuity provides a way to improve some project performance, but unexpected and harmful interruptions may impede the implementation. This paper aims to…
Abstract
Purpose
Interrupting work continuity provides a way to improve some project performance, but unexpected and harmful interruptions may impede the implementation. This paper aims to mitigate the negative impact caused by work continuity uncertainty based on the notion of robustness.
Design/methodology/approach
This paper develops a float-based robustness measurement method for the work continuity uncertainty in repetitive projects. A multi-objective optimization model is formulated to generate a schedule that achieves a balance between crew numbers and robustness. This model is solved using two modules: optimization module and decision-making module. The Monte Carlo simulation is designed to validate the effectiveness of the generated schedule.
Findings
The results confirmed that it is necessary to consider the robustness as an essential factor when scheduling a repetitive project with uncertainty. Project managers may develop a schedule that is subject to delays if they only make decisions according to the results of the deadline satisfaction problem. The Monte Carlo simulation validated that an appropriate way to measure robustness is conducive to generating a schedule that can avoid unnecessary delay, compared to the schedule generated by the traditional model.
Originality/value
Available studies assume that the work continuity is constant, but it cannot always be maintained when affected by uncertainty. This paper regards the work continuity as a new type of uncertainty factor and investigates how to mitigate its negative effects. The proposed float-based robustness measurement can measure the ability of a schedule to absorb unpredictable and harmful interruptions, and the proposed multi-objective scheduling model provides a way to incorporate the uncertainty into a schedule.