Purpose
Construction projects are complex projects taking place in dynamic environments, which necessitates accounting for different uncertainties during the planning stage. There is a significant lack of management tools for repetitive projects accounting for uncertainties in the construction environment. The purpose of this paper is to present an algorithm for the optimized scheduling of repetitive construction projects under uncertainty.
Design/methodology/approach
Fuzzy set theory is utilized to model uncertainties associated with various input parameters. The developed algorithm has two main components: optimization component and buffering component. The optimization component presents a dynamic programming approach that processes fuzzy numbers. The buffering component converts the optimized fuzzy schedule into a deterministic schedule and inserts time buffers to protect the schedule against anticipated delays. Agreement Index (AI) is used to capture the user’s desired level of confidence in the produced schedule while sizing buffers. The algorithm is capable of optimizing for cost or time objectives. An example project drawn from literature is analysed to demonstrate the capabilities of the developed algorithm and to allow comparison of results to those previously generated.
Findings
Testing the algorithm revealed several findings. Fuzzy numbers can be utilized to capture uncertainty in various inputs without the need for historical data. The modified algorithm is capable of optimizing schedules, for different objectives, under uncertainty. Finally AI can be used to capture users’ desired confidence in the final schedule.
Originality/value
Project planners can utilize this algorithm to optimize repetitive projects schedules, while modelling uncertainty in different input parameters, without the need for relevant historical data.
This paper presents an automated method for estimating productivity of earthmoving operations in near-real-time. The developed method utilizes Global Positioning System (GPS) and Google Earth to extract the data needed to perform the estimation process. A GPS device is mounted on a hauling unit to capture the spatial data along designated hauling roads for the project. The variations in the captured cycle times were used to model the uncertainty associated with the operation involved. This was carried out by automated classification, data fitting, and computer simulation. The automated classification is applied through a spreadsheet application that classifies GPS data and identifies, accordingly, durations of different activities in each cycle using spatial coordinates and directions captured by GPS and recorded on its receiver. The data fitting was carried out using commercially available software to generate the probability distribution functions used in the simulation software "Extend V.6". The simulation was utilized to balance the production of an excavator with that of the hauling units. A spreadsheet application was developed to perform the calculations. An example of an actual project was analyzed to demonstrate the use of the developed method and illustrates its essential features. The analyzed case study demonstrates how the proposed method can assist project managers in taking corrective actions based on the near-real-time actual data captured and processed to estimate productivity of the operations involved.Résumé : Cet article présente une méthode automatisée pour évaluer la productivité des opérations de terrassement en temps quasi réel. La méthode mise sur pied utilise le système de localisation GPS et Google Earth pour extraire les données requises à la réalisation du processus d'estimation. Un dispositif GPS est installé sur une unité de transport pour capter les données spatiales le long de la route de transport désignée pour le projet. Les variations des temps de cycle captés ont été utilisées pour modéliser l'incertitude associée à l'opération. Cela a été réalisé par classification automatique, adéquation des données et simulation informatique. La classification automatisée est effectuée dans un chiffrier qui classifie les données GPS et identifie les durées des diverses activités dans chaque cycle en utilisant les coordonnées spatiales et les directions captées par le GPS et enregistrées sur son récepteur. L'adéquation des données a été réalisée en utilisant un logiciel disponible sur le marché pour générer les fonctions de distribution des probabilités utilisées dans le logiciel de simulation, « Extend V.6 ». La simulation a été utilisée pour équilibrer la production d'une excavatrice et celle des unités de transport. Un chiffrier a été développé pour faire les calculs. Un exemple d'un projet réel a été analysé pour démontrer l'utilisation de la méthode développée et illustre ses caractéristiques importantes. L'étude de cas analysée démontre comment la méthode proposée peut aider les gestio...
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