Optimized scheduling and buffering of repetitive construction projects under uncertainty. Issue 6 (21st November 2016)

• Record Type: Journal Article
• Authors: Bakry, Ibrahim; Moselhi, Osama; Zayed, Tarek
• Is Part Of: Engineering, construction and architectural management. Volume 23:Issue 6(2016)
• Page Start: 782
• Page End: 800
• Publication Date: 2016-11-21
• Subjects: Construction management -- Dynamic programming -- Fuzzy optimization -- Linear scheduling -- Repetitive projects -- Time buffers; Construction industry -- Management -- Periodicals; Engineering -- Management -- Periodicals; Engineering -- Periodicals; Building -- Periodicals; 624.068
• DOI: 10.1108/ECAM-05-2014-0069
• Languages: English
• ISSNs: 0969-9988

Abstract

Abstract : 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.